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Human linguistic units are hierarchical, and our brain responds differently when processing linguistic units during sentence comprehension, especially when the modality of the received signal is different (auditory, visual, or audio-visual). However, it is unclear how the brain processes and integrates language information at different linguistic units (words, phrases, and sentences) provided simultaneously in audio and visual modalities. To address the issue, we presented participants with sequences of short Chinese sentences through auditory or visual or combined audio- visual modalities, while electroencephalographic responses were recorded. With a frequency tagging approach, we analyzed the neural representations of basic linguistic units (i.e., characters/monosyllabic words) and higher-level linguistic structures (i.e., phrases and sentences) across the three modalities separately. We found that audio-visual integration occurs at all linguistic units, and the brain areas involved in the integration varied across different linguistic levels. In particular, the integration of sentences activated the local left prefrontal area. Therefore, we used continuous theta-burst stimulation (cTBS) to verify that the left prefrontal cortex plays a vital role in the audio-visual integration of sentence information. Our findings suggest the advantage of bimodal language comprehension at hierarchical stages in language-related information processing and provide evidence for the causal role of the left prefrontal regions in processing information of audio-visual sentences.

Recent evidence suggests that about 30%of patients with mild to moderate Alzheimer’s disease (AD) without a known diagnosis of epilepsy may display epileptiform spikes during electroencephalographic (EEG) recordings. These abnormal discharges occur predominantly during sleep and may be associated with accelerated disease progression. Subclinical spikes may represent a relevant target for clinical drug interventions, and there is a clear unmet need for preclinical testing of novel disease modifying agents in suitable animal models. Transgenic rodent models of AD pathology exhibit various forms of epileptiform EEG activity related to the abnormal levels of amyloid species in the brain. Among them, large-amplitude cortical and hippocampal EEG spikes in mouse and rat AD models may be reminiscent of the subclinical epileptiform EEG spikes recorded in some AD patients. This article reports the recommendations of a multidisciplinary panel of experts on optimal EEG markers and experimental designs to measure and report epileptiform activities and their response to symptomatic and disease-modifying drugs in transgenic AD model rodents. These recommendations may harmonize future preclinical EEG studies in the drug discovery research and may increase the comparability of experimental outcomes and their translational clinical value.

Neurocomputational models of cognition have framed aesthetic appreciation within the domain of knowledge acquisition and learning, suggesting that aesthetic appreciation might be considered as a hedonic feedback on successful perceptual learning dynamics. Such hypothesis, however, has never been empirically demonstrated yet. In order to investigate the relationship between aesthetic appreciation and learning, we measured the EEG mismatch negativity (MMN) response to more or less appreciated musical intervals, which is considered as a reliable index of perceptual learning. To this end, we measured the MMN to frequency (Hz) standard and frequency deviant musical intervals (Experiment 1) while participants were asked to judge their beauty. For each single stimulus, we also computed an information-theoretic index of perceptual learning (Bayesian surprise). We found that more appreciated musical intervals were associated with a larger MMN responses, which, in turn, correlated with trial-by-trial fluctuations in Bayesian surprise (Experiment 1). Coherently with previous results, Bayesian surprise was also found to correlate with slower RTs in a detection task of the same stimuli, evidencing that motor behavior is inhibited in presence of surprising sensory states triggering perceptual learning (Experiment 2). Our results provide empirical evidence of the existence of a positive correlation between aesthetic appreciation and EEG indexes of perceptual learning. We argue that the sense of beauty might have evolved to signal the nervous system new sensory knowledge acquisition and motivate the individual to search for informationally profitable stimuli. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Transcranial direct current stimulation (tDCS) is a possible neurotherapeutic alternative to psychostimulants in Attention-Deficit/Hyperactivity Disorder (ADHD). However, very little is known regarding the mechanisms of action of tDCS in children and adolescents with ADHD. We conducted the first multi-session, sham-controlled study of anodal tDCS over right inferior frontal cortex (rIFC), a consistently under-functioning region in ADHD, combined with cognitive training (CT) in 50 children/adolescents with ADHD. This study investigated the underlying mechanisms of action on resting and Go/No-Go Task-based QEEG measures in a subgroup of 23 participants with ADHD (n, sham=10; anodal tDCS=13). We found no significant sham versus anodal tDCS group differences in QEEG spectral power during rest and Go/No-Go Task performance, no correlation between the QEEG and Go/No-Go Task performance, and no effect on clinical and cognitive outcome measures. These findings extend the null clinical or cognitive effects in our whole sample of 50 children/adolescents with ADHD. Our findings do not indicate multi-session anodal tDCS with CT over rIFC as a treatment for children/adolescents with ADHD. Larger RCTs should explore different protocols titrated to the individual and using comprehensive measures to assess cognitive, clinical, and neural effects of tDCS and its underlying mechanisms of action in ADHD.

Significance In the adult brain, multimodal speech perception that interfaces with a bidirectional interaction of perception and production speech systems is increasingly accepted. Speech perception in infancy is already highly multisensory, suggesting an early emerging representation for speech across sensory modalities. We provide electrophysiological evidence for sensorimotor influences on auditory speech discrimination responses in 3-mo-old infants who are several months away from producing canonical babbling. Auditorily, infants discriminated both contrasts tested. However, a tongue-tip articulatory inhibition diminished the /ɗa/-/ɖa/ discrimination (both phones involve the tongue-tip movement as a place of articulation), whereas accentuating the /ba/-/ɗa/ discrimination (different articulators involved in the articulation of the two phones). These findings suggest that prebabbling infants’ speech perception is more robustly multisensory than previously considered., While there is increasing acceptance that even young infants detect correspondences between heard and seen speech, the common view is that oral-motor movements related to speech production cannot influence speech perception until infants begin to babble or speak. We investigated the extent of multimodal speech influences on auditory speech perception in prebabbling infants who have limited speech-like oral-motor repertoires. We used event-related potentials (ERPs) to examine how sensorimotor influences to the infant’s own articulatory movements impact auditory speech perception in 3-mo-old infants. In experiment 1, there were ERP discriminative responses to phonetic category changes across two phonetic contrasts (bilabial–dental /ba/-/ɗa/; dental–retroflex /ɗa/-/ɖa/) in a mismatch paradigm, indicating that infants auditorily discriminated both contrasts. In experiment 2, inhibiting infants’ own tongue-tip movements had a disruptive influence on the early ERP discriminative response to the /ɗa/-/ɖa/ contrast only. The same articulatory inhibition had contrasting effects on the perception of the /ba/-/ɗa/ contrast, which requires different articulators (the lips vs. the tongue) during production, and the /ɗa/-/ɖa/ contrast, whereby both phones require tongue-tip movement as a place of articulation. This articulatory distinction between the two contrasts plausibly accounts for the distinct influence of tongue-tip suppression on the neural responses to phonetic category change perception in definitively prebabbling, 3-mo-old, infants. The results showing a specificity in the relation between oral-motor inhibition and phonetic speech discrimination suggest a surprisingly early mapping between auditory and motor speech representation already in prebabbling infants.

Some aspects of our memory are enhanced by emotion, whereas other can be unaffected or even hinder. In particular, previous studies report impaired associative memory of emotional content, an effect termed associative 'emotional interference'. The current study utilized EEG and an associative recognition paradigm to investigate the cognitive and neural mechanisms associated with this effect. In two experiments, participants studied negative and neutral stimulus-pairs that were either semantically related or unrelated. In Experiment 1 emotions were relevant to the encoding task (valence judgment) whereas in Experiment 2 emotions were irrelevant (familiarity judgment). In a subsequent associative recognition test, EEG was recorded while participants discriminated between intact, rearranged, and new pairs. An associative emotional interference effect was observed in both experiments, but was attenuated for semantically related pairs when emotions were relevant to the study task. Moreover, a modulation of an early associative memory ERP component depended on task relevancy of emotions, and occurred for negative pairs when emotions were relevant, but for semantically related pairs when emotions were irrelevant. A later ERP component depicted a more general pattern, and was observed in all experimental conditions. These results suggest that both emotions and semantic relations can act as organizing principles that promote associative binding. Their ability to contribute to successful retrieval depends on specific task demands.

In this study, some devices and utilities used for taking electroencephalography (EEG) records to be used in the diagnosis and treatment of brain-related diseases are introduced and the analyzes made on the records are given. The purpose of this study is to explain how, and by which tools the brain electrical activity is measured, the EEG method used for measurement and the analysis methods of EEG, and to show the benefits of applying system research principles to the electrical activities obtained with important studies. The device used for taking EEG recordings is the Emotiv EPOC + 14 Channel Mobile EEG device. The programs used for the recording and analysis of the EEG recordings in computer environment are EMOTIV App, EmotivPRO, EmotivBCI, Emotiv Xavier Contorlpanel ve Emotiv Brain Activitiy Map programs.

Brain–Computer Interface technology plays a vital role in facilitating post-stroke patients’ ability to carry out their daily activities of living. The extraction of features and the classification of electroencephalogram (EEG) signals are pertinent parts in enabling such a system. This research investigates the efficacy of Transfer Learning models namely ResNet50 V2, ResNet101 V2, and ResNet152 V2 in extracting features from CWT converted wink-based EEG signals, prior to its classification via a fine-tuned Support Vector Machine (SVM) classifier. It was shown that ResNet152 V2-SVM pipeline could achieve an excellent accuracy on all train, test and validation datasets.

Objective Electroencephalography (EEG) is an important tool for neurological outcome prediction after cardiac arrest. However, the complexity of continuous EEG data limits timely and accurate interpretation by clinicians. We develop a deep neural network (DNN) model to leverage complex EEG trends for early and accurate assessment of cardiac arrest coma recovery likelihood. Methods We developed a multiscale DNN combining convolutional neural networks (CNN) and recurrent neural networks (long short-term memory [LSTM]) using EEG and demographic information (age, gender, shockable rhythm) from a multicenter cohort of 1,038 cardiac arrest patients. The CNN learns EEG feature representations while the multiscale LSTM captures short-term and long-term EEG dynamics on multiple time scales. Poor outcome is defined as a Cerebral Performance Category (CPC) score of 3-5 and good outcome as CPC score 1-2 at 3-6 months after cardiac arrest. Performance is evaluated using area under the receiver operating characteristic curve (AUC) and calibration error. Results Model performance increased with EEG duration, with AUC increasing from 0.83 (95% Confidence Interval [CI] 0.79-0.87 at 12h to 0.91 (95%CI 0.88-0.93) at 66h. Sensitivity of good and poor outcome prediction was 77% and 75% at a specificity of 90%, respectively. Sensitivity of poor outcome was 50% at a specificity of 99%. Predicted probability was well matched to the observation frequency of poor outcomes, with a calibration error of 0.11 [0.09-0.14]. Conclusions These results demonstrate that incorporating EEG evolution over time improves the accuracy of neurologic outcome prediction for patients with coma after cardiac arrest.

Virtual reality-based exercise (exergames) improves cognition of the elderly but the neurophysiological effects are poorly understood. The hypothesis herein established is that an ultrafast neurophysiological adaptation occurs in prefrontal cortex of elderly after completion of a single exergames session. To reinforce the aforementioned hypothesis, individuals living in a Long-Term Care Home (LTCH) participated in the study and were randomly allocated into two groups (Virtual Reality Group, VRG, n = 5; and Active Control Group, ACG n = 5). VRG performed six exercises with exergames and ACG performed exercises with the same VRG movements but with no virtual reality. Assessment of frontal cortical activity at rest and during cognitive testing via electroencephalographic activity (EEG) was performed before and immediately after the intervention. Significant decrease in relative power of EEG (RPEEG) Beta brainwave (−29 ± 18%) in the left prefrontal cortex of VRG compared to ACG (4 ± 9%) (p = 0.007). A slight improvement on semantic fluency in VRG (ES=0.21) was noted. An ultrafast prefrontal cortical adaptation may occur as an effect of a single exergames session, causing a small improvement on cognition of institutionalized elderly.

This study presents a method with high accuracy performance that aims to automatically detect schizophrenia (SZ) from electroencephalography (EEG) records. Unlike related literature studies using traditional machine learning algorithms, the features required for the training of the network are automatically extracted from the EEG records in our method. In order to obtain the time frequency features of the EEG signals, the signal was converted into 2D by using the Continuous Wavelet Transform method. This study has the highest accuracy performance in the relevant literature by using 2D time frequency features in automatic detection of SZ disease. It is trained with Visual Geometry Group-16 (VGG16), an advanced convolutional neural networks (CNN) deep learning network architecture, to extract key features found on scalogram images and train the network. The study shows a high success in classifying SZ patients and healthy individuals with a very satisfactory accuracy of 98% and 99.5%, respectively, using two different datasets consisting of individuals from different age groups. Using different techniques [Activization Maximization, Saliency Map, and Gradient-weighted Class Activation Mapping (Grad-CAM)] to visualize the learning outcomes of the CNN network, the relationship of frequency components between SZ and the healthy individual is clearly shown. Moreover, with these interpretable outcomes, the difference between SZ patients and healthy individuals can be distinguished very easily help for expert opinion.

Interruptions lead to a deterioration of primary task performance. Applied research usually describes a delay in primary task resumption as an essential component of this performance deficit. Here, we investigate this approach using electrophysiological correlates of the focusing of attention within working memory, a process that is fundamental to switching between different tasks. A lateralized working memory task was frequently interrupted by either a high- or low-demanding arithmetic task and a subsequent retrospective cue indicated the working memory item required for later report. The detrimental effect of interruptions on primary task performance was most pronounced for high-demanding interruptions. After retro-cue presentation, fronto-central theta power (4–7 Hz) was lowest following high-demanding interruptions and posterior alpha power (8–14 Hz) was less suppressed in the two interruption conditions. These effects might be related to a deficit in attentional control processes following the retrospective cue. Furthermore, we introduce the suppression of posterior alpha power contralateral to the remembered primary task stimuli during the interruption phase as a temporal marker for primary task resumption. Especially for cognitively demanding interruption tasks, this effect seems to overlap in time with the processing of the interruption, which should contribute to the primary task performance deficit.

Highlights • Unexplained dizziness in the elderly may result from cerebral small vessel disease. • Dizzy elderly patients differed from controls in EEG power when standing. • EEG power when standing correlated with subjective (perceived) instability., Objective To examine the hypothesis that small vessel disease disrupts postural networks in older adults with unexplained dizziness in the elderly (UDE). Methods Simultaneous electroencephalography and postural sway measurements were undertaken in upright, eyes closed standing, and sitting postures (as baseline) in 19 younger adults, 33 older controls and 36 older patients with UDE. Older adults underwent magnetic resonance imaging to determine whole brain white matter hyperintensity volumes, a measure of small vessel disease. Linear regression was used to estimate the effect of instability on electroencephalographic power and connectivity. Results Ageing increased theta and alpha desynchronisation on standing. In older controls, delta and gamma power increased, and theta and alpha power reduced with instability. Dizzy older patients had higher white matter hyperintensity volumes and more theta desynchronisation during periods of instability. White matter hyperintensity volume and delta power during periods of instability were correlated, positively in controls but negatively in dizzy older patients. Delta power correlated with subjective dizziness and instability. Conclusions Neural resource demands of postural control increase with age, particularly in patients with UDE, driven by small vessel disease. Significance EEG correlates of postural control saturate in older adults with UDE, offering a neuro-physiological basis to this common syndrome.

Reach&grasp requires highly coordinated activation of different brain areas. We investigated whether reach&grasp kinematics is associated to EEG-based networks changes. We enrolled 10 healthy subjects. We analyzed the reach&grasp kinematics of 15 reach&grasp movements performed with each upper limb. Simultaneously, we obtained a 64-channel EEG, synchronized with the reach&grasp movement time points. We elaborated EEG signals with EEGLAB 12 in order to obtain event related synchronization/desynchronization (ERS/ERD) and lagged linear coherence between Brodmann areas. Finally, we evaluated network topology via sLORETA software, measuring network local and global efficiency (clustering and path length) and the overall balance (small-worldness). We observed a widespread ERD in α and β bands during reach&grasp, especially in the centro-parietal regions of the hemisphere contralateral to the movement. Regarding functional connectivity, we observed an α lagged linear coherence reduction among Brodmann areas contralateral to the arm involved in the reach&grasp movement. Interestingly, left arm movement determined widespread changes of α lagged linear coherence, specifically among right occipital regions, insular cortex and somatosensory cortex, while the right arm movement exerted a restricted contralateral sensory-motor cortex modulation. Finally, no change between rest and movement was found for clustering, path length and small-worldness. Through a synchronized acquisition, we explored the cortical correlates of the reach&grasp movement. Despite EEG perturbations, suggesting that the non-dominant reach&grasp network has a complex architecture probably linked to the necessity of a higher visual control, the pivotal topological measures of network local and global efficiency remained unaffected.

Background: Animal models of absence epilepsy are widely used in childhood absence epilepsy studies. Absence seizures appear in the brain’s electrical activity as a specific spike wave discharge (SWD) pattern. Reviewing long-term brain electrical activity is time-consuming and automatic methods are necessary. On the other hand, nonlinear techniques such as phase space are effective in brain electrical activity analysis. In this study, we present a novel SWD-detection framework based on the geometrical characteristics of the phase space. Methods: The method consists of the following steps: (1) Rat stereotaxic surgery and cortical electrode implantation, (2) Long-term brain electrical activity recording, (3) Phase space reconstruction, (4) Extracting geometrical features such as volume, occupied space, and curvature of brain signal trajectories, and (5) Detecting SDWs based on the thresholding method. We evaluated the approach with the accuracy of the SWDs detection method. Results: It has been demonstrated that the features change significantly in transition from a normal state to epileptic seizures. The proposed approach detected SWDs with 98% accuracy. Conclusion: The result supports that nonlinear approaches can identify the dynamics of brain electrical activity signals.

Post-cardiac arrest brain injury (PCABI) is caused by initial ischaemia and subsequent reperfusion of the brain following resuscitation. In those who are admitted to intensive care unit after cardiac arrest, PCABI manifests as coma, and is the main cause of mortality and long-term disability. This review describes the mechanisms of PCABI, its treatment options, its outcomes, and the suggested strategies for outcome prediction.

The human electroencephalogram (EEG) constitutes a nonstationary, nonlinear electrophysiological signal resulting from synchronous firing of neurons in thalamocortical structures of the brain. Due to the complexity of the brain's physiological structures and its rhythmic oscillations, analysis of EEG often utilises spectral analysis methods.Aim: to improve clinical monitoring of neurophysiological signals and to further explain basic principles of functional mechanisms in the brain during anaesthesia.Material and methods. In this paper we used Empirical Mode decomposition (EMD), a novel spectral analysis method especially suited for nonstationary and nonlinear signals. EMD and the related Hilbert-Huang Transform (HHT) decompose signal into constituent Intrinsic Mode Functions (IMFs). In this study we applied EMD to analyse burst-suppression (BS) in the human EEG during induction of general anaesthesia (GA) with propofol. BS is a state characterised by cyclic changes between significant depression of brain activity and hyper-active bursts with variable duration, amplitude, and waveform shape. BS arises after induction into deep general anaesthesia after an intravenous bolus of general anaesthetics. Here we studied the behaviour of BS using the burst-suppression ratio (BSR).Results. Comparing correlations between EEG and IMF BSRs, we determined BSR was driven mainly by alpha activity. BSRs for different spectral components (IMFs 1-4) showed differing rates of return to baseline after the end of BS in EEG, indicating BS might differentially impair neural generators of low-frequency EEG oscillations and thalamocortical functional connectivity.Conclusion. Studying BS using EMD represents a novel form of analysis with the potential to elucidate neurophysiological mechanisms of this state and its impact on post-operative patient prognosis.

Enabling to obtain brain activation signs, electroencephalography is currently used in many applications as a medical diagnostic method. Brain-computer interface (BCI) applications are developed to facilitate the lives of individuals who have not lost their brain functions yet have lost their motor and communication abilities. In this study, a BCI system is proposed to make classification using Bi-directional long short term memory (Bi-LSTM) neural networks. In the designed system, spectral entropy method including instantaneous frequency change of signal is used as feature fusion. In the study, electroencephalography (EEG) data of 10 participants are collected with Emotiv EPOC+ device using 2x2 visual stimulus matrix prepared on Unity. Each symbol of the 2x2 matrix includes stimulus such as doctor, police, fireman and family. These stimuli are demonstrated to participants with a fixed order. As data collection protocol, 200 ms stimulus time and 300 ms interstimulus interval are used. As the performance success of classification, the average accuracy rates are obtained to be 98.6% for training set and 96.9% for the test set. In addition, in classification of P300 EEG signals, the results obtained via Bi-LSTM are compared with the results obtained using 1 dimensional convolutional neural networks (1DCNN) and support vector machines (SVM) classification methods. Moreover, in the study, information transfer rate (ITR) is provided as 40.39 at an acceptable level. Research Fund of the Sakarya University of Applied Sciences [2015-50-02-038]; Ethics Committee of the Sakarya University Faculty of Medicine [16214662/050,01,04/2] This work was supported by Research Fund of the Sakarya University of Applied Sciences. Project Number: 2015-50-02-038. This work experimental protocol was approved 28.12.2016 dated and 16214662/050,01,04/2 numbered by Ethics Committee of the Sakarya University Faculty of Medicine.

BACKGROUND: The supplementary motor area (SMA) is implicated in stereotypic multi-limb movements such as walking with arm swing. Gait difficulties in Parkinson's Disease (PD) include reduced arm swing, which is associated with reduced SMA activity.OBJECTIVE: To test whether enhanced arm swing improves Parkinsonian gait and explore the role of the SMA in such an improvement.METHODS: Cortical activity and gait characteristics were assessed by ambulant EEG, accelerometers and video recordings in 27 PD patients with self-reported gait difficulties and 35 healthy participants when walking normally. Within these two groups, 19 PD patients additionally walked with enhanced arm swing and 30 healthy participants walked without arm swing. Power changes across the EEG frequency spectrum were assessed by Event Related Spectral Perturbation analysis of recordings from Fz over the putative SMA and gait analysis was performed.RESULTS: Baseline PD gait, characterized by reduced arm swing among other features, exhibited reduced within-step Event Related Desynchronization (ERD)/Synchronization (ERS) alternation (Fz; 20-50Hz), accompanied by a reduced step length and walking speed. All became similar to normal gait when patients walked with enhanced arm swing. When healthy controls walked without arm swing, their alternating ERD-ERS pattern decreased, mimicking baseline PD gait.CONCLUSION: Enhanced arm swing may serve as a driving force to overcome impaired gait control in PD patients by restoring reduced ERD-ERS alternation over the putative SMA. Accompanied by increased step length and walking speed, this provides a neural underpinning of arm swing as an effective rehabilitation concept for improving Parkinsonian gait.

Introduction The present research investigates the neural correlates of attentional bias in fibromyalgia (FM) with a dot-probe task performed during an electroencephalogram (EEG) recording. Methods For this purpose, 30 female participants were recruited, divided into two groups: a group of patients with FM (FM, n = 15, Mage = 51.87) and a healthy control group (HC) (HC, n = 15, Mage = 46.13). Results The results did not show behavioral differences between groups, but the EEG results showed that healthy controls had larger P300 amplitudes than patients with FM. Regarding late positive potentials (LPP), we found that patients with FM had larger amplitudes than healthy controls in a later time window. Conclusion In summary, while the P300 results suggest that patients allocate less attentional resources to the task, the increased amplitudes of their LPP suggest augmented emotional processing of the target stimuli. Altogether, our results seem to support the thesis of generalized attentional deficits in FM., Plain Language Summary Fibromyalgia (FM) is a chronic musculoskeletal pain condition. There has been discussion in the scientific literature as to whether patients with FM suffer from a generalized attentional deficit or an attentional bias—preferentially selecting pain-related information. Attentional bias in FM patients has been studied as hypervigilance, which refers to early detection of pain-related information or innocuous information. Thus, the aim of this study was to test whether there is a generalized attentional deficit or attentional bias in relation to pain in patients with FM, by studying the neural activity underlying cognitive processes, specifically with evoked potentials (P300 and late positive potential—LPP). The P300 has been related to the use of attentional resources and the LPP to affective modulation. For this purpose, we studied two groups: a group of patients with FM and a healthy control group. Our hypotheses considered that FM patients, compared to healthy controls, would show an attentional bias for pain-related words (1) reflected in higher hits and shorter reaction time when detecting the target of a cognitive task (dot-probe), and (2) manifested by increased amplitudes of P300 and LPP evoked potentials while performing the task. The electrophysiological results suggest that FM patients may have a generalized attentional deficit and, despite this being the case, FM patients are more emotionally involved in the task.

Background Virtual reality training (VRT) is a new method for the rehabilitation of musculoskeletal impairments. However, the clinical and central effects of VRT have not been investigated in patients with patellofemoral pain (PFP). To comprehensively assess the effects of VRT on clinical indices and brain function, we used a randomized clinical trial based on clinical and brain mapping assessment. Methods Twenty-six women with PFP for more than 6 months were randomly allocated to 2 groups: intervention and control. The intervention consisted of lifestyle education + 8 weeks VRT, in 24 sessions each lasting 40 min of training, whereas the control group just received lifestyle education. The balance was the primary outcome and was measured by the modified star excursion balance test. Secondary outcomes included pain, function, quality of life, and brain function which were assessed by visual analogue scale, step down test and Kujala questionnaire, SF-36, and EEG, respectively. Pre-intervention, post-intervention and follow-up (1 month after the end of the intervention) measurements were taken for all outcome measures except EEG, which was evaluated only at pre-intervention and post-intervention). Analyses of variance was used to compare the clinical outcomes between the two groups. The independent t-test also was used for between group EEG analyses. Results Balance score (P ), function (P ), and quality of life (P = 0.001) improved significantly at post-intervention and 1 month follow-up in the VRT group compared with the control group. VRT group showed a significantly decreased pain score (P = 0.004). Alpha (P ) and theta (P = 0.01) power activity also increased in the brain of the VRT group. Conclusion This study demonstrated that long term VRT was capable of improving both clinical impairments and brain function in patients with PFP. Therefore, therapists and clinicians can use this method as a more holistic approach in the rehabilitation of PFP. Trial registration IRCT, IRCT20090831002391N40. Registered 23 / 10 / 2019.

Contains fulltext : 236486.pdf (Publisher’s version ) (Open Access) Objective: Brain-Computer Interface (BCI) spellers that make use of code-modulated Visual Evoked Potentials (cVEP) may provide a fast and more accurate alternative to existing visual BCI spellers for patients with Amyotrophic Lateral Sclerosis (ALS). However, so far the cVEP speller has only been tested on healthy participants. Methods: We assess the brain responses, BCI performance and user experience of the cVEP speller in 20 healthy participants and 10 ALS patients. All participants performed a cued and free spelling task, and a free selection of Yes/No answers. Results: 27 out of 30 participants could perform the cued spelling task with an average accuracy of 79% for ALS patients, 88% for healthy older participants and 94% for healthy young participants. All 30 participants could answer Yes/No questions freely, with an average accuracy of around 90%. Conclusions: With ALS patients typing on average 10 characters per minute, the cVEP speller presented in this paper outperforms other visual BCI spellers. Significance These results support a general usability of cVEP signals for ALS patients, which may extend far beyond the tested speller to control e.g. an alarm, automatic door, or TV within a smart home. 12 p.

Electroencephalogram (EEG) is a convenient neuroimaging technique due to its non-invasive setup, practical usage, and high temporal resolution. EEG allows to detect brain electrical activity to diagnose neurological disorders. Epilepsy is a crucial neurologic disorder that is reasoned from occurrence of sudden and repeated seizures. The goal of this paper is to classify the focal (epileptogenic area) and non-focal (non-epileptogenic area) EEG records with cepstral coefficients and machine learning algorithms. Analysis is carried out using publicly available Bern-Barcelona EEG dataset. Mel Frequency Cepstral Coefficients (MFCC) are calculated from EEG epochs. Feature sets are normalized with z-score and dimension reduction is realized using Principal Component Analysis. Fine Tree, Quadratic Discriminant Analysis, Logistic Regression, Gaussian Naïve Bayes, Cubic Support Vector Machine, weighted k-nearest neighbors, and Bagged Trees are applied for classification stage. A value of k=10 is used for cross validation. All focal and non-focal EEG pairs are perfectly classified with acc., sen., spe., and F1-score of 100% and AUC with 1 via. Quadratic Discriminant Analysis, Logistic Regression, Cubic SVM and Weighted k-NN. Proposed work recommends MFCCs as a single marker and this provides less computation workload, practicality, and direct processing of focal / non-focal EEG time series. Proposed methodology in this paper serves one of the highest achievements to literature and can assist neurologist and physicians to validate their diagnosis.

At what level of spatial resolution can the human brain recognize a familiar face in a crowd of strangers? Does it depend on whether one approaches or rather moves back from the crowd? To answer these questions, 16 observers viewed different unsegmented images of unfamiliar faces alternating at 6 Hz, with spatial frequency (SF) content progressively increasing (i.e., coarse-to-fine) or decreasing (fine-to-coarse) in different sequences. Variable natural images of celebrity faces every sixth stimulus generated an objective neural index of single-glanced automatic familiar face recognition (FFR) at 1 Hz in participants’ electroencephalogram (EEG). For blurry images increasing in spatial resolution, the neural FFR response over occipitotemporal regions emerged abruptly with additional cues at about 6.3–8.7 cycles/head width, immediately reaching amplitude saturation. When the same images progressively decreased in resolution, the FFR response disappeared already below 12 cycles/head width, thus providing no support for a predictive coding hypothesis. Overall, these observations indicate that rapid automatic recognition of heterogenous natural views of familiar faces is achieved from coarser visual inputs than generally thought, and support a coarse-to-fine FFR dynamics in the human brain.

Purpose of the study. To study functional changes in the neuropsychological status of reproductive age patients with newly diagnosed breast cancer and to assess the possibility of using xenon-oxygen therapy to correct disorders.Patients and methods. This study included 60 reproductive age patients with newly diagnosed breast cancer who were undergoing surgical treatment at the Department of Bone, Skin, Soft Tissue and Breast Tumors of the National Medical Research Centre for Oncology of the Ministry of Health of Russia from 2018 to 2020. The main group included 30 patients with breast cancer diagnosed at the surgical stage of combined treatment, who underwent a rehabilitation course of xenon-oxygen therapy in the early postoperative period. The control group was formed from patients with the same diagnosis, without the use of this therapy. The functional state of the central nervous system in all patients was assessed by the parameters of the electroencephalography (EEG) bioelectrical activity. For the final assessment of the physiological and psychological state of the patients, a standardized questionnaires of the quality of life – ESAS, MOS-SF‑36, were used. Statistical data processing was performed using the Statistica 10 software package. Results. During the study, statistically significant differences were found in the assessment of subjective indicators, so in the group of patients using xenon-oxygen therapy, there was an improvement in well-being by 2.6 times, a decrease in depression by 2.3 times, a decrease in symptoms of nausea by 3 times, anxiety 1.9 times (p < 0.05). Against the background of an improvement in the psychophysiological state, the EEG showed a significant increase in the power of slow delta and theta rhythms, an increase in the power of the alpha rhythm and a decrease in the power of the beta rhythm, while in the patients of the control group only a decrease in the power of the beta rhythm was noted.Conclusion. The early postoperative period in patients with newly diagnosed breast cancer is characterized by the formation of a depressive symptom complex. The use of a course of xenon-oxygen therapy contributes to the normalization of the subjective feeling of physical and psychological health, increasing vital and social activity. Changes in the indicators of brain bioelectric activity and an improvement in psychophysiological state occur against the background of changes in brain activity caused by the normalizing effect of xenon.

Long-standing affective science theories conceive the perception of emotional stimuli either as discrete categories (for example, an angry voice) or continuous dimensional attributes (for example, an intense and negative vocal emotion). Which position provides a better account is still widely debated. Here we contrast the positions to account for acoustics-independent perceptual and cerebral representational geometry of perceived voice emotions. We combined multimodal imaging of the cerebral response to heard vocal stimuli (using functional magnetic resonance imaging and magneto-encephalography) with post-scanning behavioural assessment of voice emotion perception. By using representational similarity analysis, we find that categories prevail in perceptual and early (less than 200 ms) frontotemporal cerebral representational geometries and that dimensions impinge predominantly on a later limbic–temporal network (at 240 ms and after 500 ms). These results reconcile the two opposing views by reframing the perception of emotions as the interplay of cerebral networks with different representational dynamics that emphasize either categories or dimensions. Are vocal emotions perceived as categories (for example, anger) or as dimensions (for example, an intense negative emotion)? Here, using functional magnetic resonance imaging and magneto-encephalography, Giordano et al. find that categories prevail in early (less than 200 ms) neural representations, whereas dimensions emerge later (240 ms and more than 500 ms).

Alzheimer’s disease is diagnosed via means of daily activity assessment. The EEG recording evaluation is a supporting tool that can assist the practitioner to recognize the illness, especially in the early stages. This paper presents a new approach for detecting Alzheimer’s disease and potentially mild cognitive impairment according to the measured EEG records. The proposed method evaluates the amount of novelty in the EEG signal as a feature for EEG record classification. The novelty is measured from the parameters of EEG signal adaptive filtration. A linear neuron with gradient descent adaptation was used as the filter in predictive settings. The extracted feature (novelty measure) is later classified to obtain Alzheimer’s disease diagnosis. The proposed approach was cross-validated on a dataset containing EEG records of 59 patients suffering from Alzheimer’s disease; seven patients with mild cognitive impairment (MCI) and 102 controls. The results of cross-validation yield 90.73% specificity and 89.51% sensitivity. The proposed method of feature extraction from EEG is completely new and can be used with any classifier for the diagnosis of Alzheimer’s disease from EEG records.

Background Electrophysiological techniques have been used for discriminating myoclonus from other hyperkinetic movement disorders and for classifying the myoclonus subtype. This study was carried out on patients with different subtypes of myoclonus to determine the electrophysiological characteristics and the anatomical classification of myoclonus of different etiologies. This study included 20 patients with different subtypes of myoclonus compared with 30 control participants. Electrophysiological study was carried out for all patients by somatosensory evoked potential (SSEP) and electroencephalography (EEG) while the control group underwent SSEP. SSEP was evaluated in patients and control groups by stimulation of right and left median nerves. Results This study included 50 cases with myoclonus of different causes with mean age of 39.3 ± 15.7 and consisted of 23 males and 27 females. Twenty-nine (58%) of the patients were epileptics, while 21 (42%) were non-epileptics. Cases were classified anatomically into ten cases with cortical myoclonus (20%), 12 cases with subcortical myoclonus (24%), and 28 cases with cortical–subcortical myoclonus (56%). There was a significant difference regarding the presence of EEG findings in epileptic myoclonic and non-epileptic myoclonic groups (P = 0.005). Also, there were significant differences regarding P24 amplitude, N33 amplitude, P24–N33 peak-to-peak complex amplitude regarding all types of myoclonus. Primary myoclonic epilepsy (PME) demonstrated significant giant response, juvenile myoclonic epilepsy (JME) demonstrated no enhancement compared to controls, while secondary myoclonus demonstrated lower giant response compared to PME. Conclusion Somatosensory evoked potential and electroencephalography are important for the diagnosis and anatomical sub-classification of myoclonus and so may help in decision-making regarding to the subsequent management.

Background: Repetitive Transcranial Magnetic Stimulation (rTMS) is an effective treatment for Major Depressive Disorder (MDD), but therapeutic benefit is highly variable. Clinical improvement is related to changes in brain circuits, which have preferred resonant frequencies (RFs) and vary across individuals. Objective: We developed a novel rTMS-electroencephalography (rTMS-EEG) interrogation paradigm to identify RFs using the association of power/connectivity measures with symptom severity and treatment outcome. Methods: 35 subjects underwent rTMS interrogation at 71 frequencies ranging from 3 to 17 Hz administered to left dorsolateral prefrontal cortex (DLPFC). rTMS-EEG was used to assess resonance in oscillatory power/connectivity changes (phase coherence [PC], envelope correlation [EC], and spectral correlation coefficient [SCC]) after each frequency. Multiple regression was used to detect relationships between 10 Hz resonance and baseline symptoms as well as clinical improvement after 10 sessions of 10 Hz rTMS treatment. Results: Baseline symptom severity was significantly associated with SCC resonance in left sensorimotor (SM; p

Background Prehospital detection of large vessel occlusion stroke of the anterior circulation (LVO-a) would enable direct transportation of these patients to an endovascular thrombectomy (EVT) capable hospital. The ongoing ELECTRA-STROKE study investigates the diagnostic accuracy of dry electrode electroencephalography (EEG) for LVO-a stroke in the prehospital setting. To determine which EEG features are most useful for this purpose and assess EEG data quality, EEG recordings are also performed in the emergency room (ER). Here, we report data of the first 100 patients included in the ER. Methods Patients presented to the ER with a suspected stroke or known LVO-a stroke underwent a single EEG prior to EVT. Diagnostic accuracy for LVO-a stroke of frequency band power, brain symmetry and phase synchronization measures were evaluated by calculating receiver operating characteristic curves. Optimal cut-offs were determined as the highest sensitivity at a specificity of ≥ 80%. Results EEG data were of sufficient quality for analysis in 65/100 included patients. Of these, 35/65 (54%) had an acute ischemic stroke, of whom 9/65 (14%) had an LVO-a stroke. Median onset-to-EEG-time was 266 min (IQR 121–655) and median EEG-recording-time was 3 min (IQR 3–5). The EEG feature with the highest diagnostic accuracy for LVO-a stroke was theta–alpha ratio (AUC 0.83; sensitivity 75%; specificity 81%). Combined, weighted phase lag index and relative theta power best identified LVO-a stroke (sensitivity 100%; specificity 84%). Conclusion Dry electrode EEG is a promising tool for LVO-a stroke detection, but data quality needs to be improved and validation in the prehospital setting is necessary. (TRN: NCT03699397, registered October 9 2018).

Hypothalamic hamartomas (HHs) are non-neoplastic malformations that occur in the region of the hypothalamus. HH is the leading cause of gelastic seizures in children and adolescents, where laughing is characteristically manifested. However, these patients can also experience different forms of complex or generalized tonic-clonic seizures that can obscure the diagnosis of HHs. We present a case of a 10 year-old boy that experienced several seizure types, but was subsequently diagnosed with HH after MR imaging was performed. This case highlights the complementary role of MR imaging in ascertaining seizure etiololgy when the clinical history and EEG findings are non-specific. The importance of early diagnosis with MR imaging is further underscored by the fact that patients diagnosed with HH usually develop drug resistance towards antiepileptic drugs, mandating neurosurgical assessment and intervention.

Background Angelman syndrome (AS) is a rare neurodevelopmental disorder caused by the absence of functional UBE3A in neurons. Excess low-frequency oscillations as measured with electroencephalography (EEG) have been identified as a characteristic finding, but the relationship of this EEG finding to the symptomatology of AS and its significance in the pathophysiology of AS remain unknown. Methods We used correlations and machine learning to investigate the cross-sectional and longitudinal relationship between EEG spectral power and motor, cognitive, and language skills (Bayley Scales of Infant and Toddler Development, Third Edition); adaptive behavior (Vineland Adaptive Behavior Scales, Second Edition); AS-specific symptoms (AS Clinical Severity Scale); and the age of epilepsy onset in a large sample of children (age: 1–18 years) with AS due to a chromosomal deletion of 15q11-q13 (45 individuals with 72 visits). Results We found that after accounting for age differences, participants with stronger EEG delta-band abnormality had earlier onset of epilepsy and lower performance scores across symptom domains including cognitive, motor, and communication. Combing spatial and spectral information beyond the delta frequency band increased the cross-sectional association with clinical severity on average by approximately 45%. Furthermore, we found evidence for longitudinal correlations of EEG delta-band power within several performance domains, including the mean across Bayley Scales of Infant and Toddler Development, Third Edition, scores. Conclusions Our results show an association between EEG abnormalities and symptom severity in AS, underlining the significance of the former in the pathophysiology of AS. Furthermore, our work strengthens the rationale for using EEG as a biomarker in the development of treatments for AS, a concept that may apply more generally to neurodevelopmental disorders.

This work presents a new on-line adaptive filter, which is based on a similarity analysis between standard electrode locations, in order to reduce artifacts and common interferences throughout electroencephalography (EEG) signals, but preserving the useful information. Standard deviation and Concordance Correlation Coefficient (CCC) between target electrodes and its correspondent neighbor electrodes are analyzed on sliding windows to select those neighbors that are highly correlated. Afterwards, a model based on CCC is applied to provide higher values of weight to those correlated electrodes with lower similarity to the target electrode. The approach was applied to brain computer-interfaces (BCIs) based on Canonical Correlation Analysis (CCA) to recognize 40 targets of steady-state visual evoked potential (SSVEP), providing an accuracy (ACC) of 86.44 ± 2.81%. In addition, also using this approach, features of low frequency were selected in the pre-processing stage of another BCI to recognize gait planning. In this case, the recognition was significantly (p < 0.01) improved for most of the subjects (ACC ¿ 74.79%), when compared with other BCIs based on Common Spatial Pattern, Filter Bank - Common Spatial Pattern, and Riemannian Geometry., This research was partially supported by the Spanish Government, Ministry of Economy and Competitiveness (grants NeuroMOD, DPI2015-68664-C4-1-R, and ESSENTIAL, DPI2015-72638-EXP). Authors would like to thank CNPq (304192/2016-3), CAPES (88887.095626/2015-01), and FAPES (72982608) from Brazil, Emerging Leaders in the America’s Program (ELAP, Canada), and SENESCYT (Ecuador) for supporting this research

Background Gilles de la Tourette Syndrome (GTS) is a neurodevelopmental disorder with a peak of symptom severity around late childhood and early adolescence. Previous findings in adult GTS suggest that changes in perception-action integration, as conceptualized in the theory of event coding framework, are central for the understanding of GTS. However, the neural mechanisms underlying these processes in adolescence are elusive. Methods A total of 59 children/adolescents aged 9 to 18 years (n = 32 with GTS, n = 27 typically developing youths) were examined using a perception-action integration task (event file task) derived from the theory of event coding. Event-related electroencephalogram recordings (theta and beta band activity) were analyzed using electroencephalogram–beamforming methods. Results Behavioral data showed robust event file binding effects in both groups without group differences. Neurophysiological data showed that theta and beta band activity were involved in event file integration in both groups. However, the functional neuroanatomical organization was markedly different for theta band activity between the groups. The typically developing group mainly relied on superior frontal regions, whereas the GTS group engaged parietal and inferior frontal regions. A more consistent functional neuroanatomical activation pattern was observed for the beta band, engaging inferior parietal and temporal regions in both groups. Conclusions Perception-action integration processes lag behind in persisting GTS but not in the GTS population as a whole, underscoring differences in developmental trajectories and the importance of longitudinal investigations for the understanding of GTS. The findings corroborate known differences in the functional/structural brain organization in GTS and suggest an important role of theta band activity in these patients.

The affective state of an individual can be determined using physiological parameters; an important metric that can then be extracted is attention. Looking more closely at compact EEGs, algorithms have been implemented in such devices that can measure the attention and other affective states of the user. No information about these algorithms is available; are these feature classification algorithms accurate? An experiment was conducted with 23 subjects who utilized a pedagogical agent to learn the syntax of the programming language Java while having their attention measured by the NeuroSky MindWave Mobile 2. Using a concurrent validity approach, the attention values measured were compared to band powers, as well as measures of task performance. The results of the experiment were in part successful and supportive of the claim that the EEG device’s attention algorithm does in fact represent a user’s attention accurately. The results of the analysis based on raw data captured from the device were consistent with previous literature. Inconclusive results were obtained relating to task performance and attention.

In the field of neurobiology of language, neuroimaging studies are generally based on stimulation paradigms consisting of at least two different conditions. Designing those paradigms can be very time-consuming and this traditional approach is necessarily data-limited. In contrast, in computational linguistics analyses are often based on large text corpora, which allow a vast variety of hypotheses to be tested by repeatedly re-evaluating the data set. Furthermore, text corpora also allow exploratory data analysis in order to generate new hypotheses. By drawing on the advantages of both fields, neuroimaging and corpus linguistics, we here present a unified approach combining continuous natural speech and MEG to generate a corpus of speech-evoked neuronal activity.

Metamemory involves the ability to correctly judge the accuracy of our memories. The retrieval of memories can be improved using transcranial electrical stimulation (tES) during sleep, but evidence for improvements to metamemory sensitivity is limited. Applying tES can enhance sleep-dependent memory consolidation, which along with metamemory requires the coordination of activity across distributed neural systems, suggesting that examining functional connectivity is important for understanding these processes. Nevertheless, little research has examined how functional connectivity modulations relate to overnight changes in metamemory sensitivity. Here, we developed a closed-loop short-duration tES method, time-locked to up-states of ongoing slow-wave oscillations, to cue specific memory replays in humans. We measured electroencephalographic (EEG) coherence changes following stimulation pulses, and characterized network alterations with graph theoretic metrics. Using machine learning techniques, we show that pulsed tES elicited network changes in multiple frequency bands, including increased connectivity in the theta band and increased efficiency in the spindle band. Additionally, stimulation-induced changes in beta-band path length were predictive of overnight changes in metamemory sensitivity. These findings add new insights into the growing literature investigating increases in memory performance through brain stimulation during sleep, and highlight the importance of examining functional connectivity to explain its effects., Author Summary Numerous studies have demonstrated a clear link between sleep and memory—namely, memories are consolidated during sleep, leading to more stable and long-lasting representations. We have previously shown that tagging episodes with specific patterns of brain stimulation during encoding and replaying those patterns during sleep can enhance this consolidation process to improve confidence and decision-making of memories (metamemory). Here, we extend this work to examine network-level brain changes that occur following stimulation during sleep that predict metamemory improvements. Using graph theoretic and machine-learning methods, we found that stimulation-induced changes in beta-band path length predicted overnight improvements in metamemory. This novel finding sheds new light on the neural mechanisms of memory consolidation and suggests potential applications for improving metamemory.

BackgroundBrain network can be well used in emotion analysis to analyze the brain state of subjects. A novel dynamic brain network in arousal is proposed to analyze brain states and emotion with Electroencephalography (EEG) signals. New MethodTime factors is integrated to construct a dynamic brain network under high and low arousal conditions. The transfer entropy is adopted in the dynamic brain network. In order to ensure the authenticity of dynamics and connections, surrogate data are used for testing and analysis. Channel norm information features are proposed to optimize the data and evaluate the level of activity of the brain. ResultsThe frontal lobe, temporal lobe, and parietal lobe provide the most information about emotion arousal. The corresponding stimulation state is not maintained at all times. The number of active brain networks under high arousal conditions is generally higher than those under low arousal conditions. More consecutive networks show high activity under high arousal conditions among these active brain networks. The results of the significance analysis of the features indicates that there is a significant difference between high and low arousal. Comparison with Existing Method(s)Compared with traditional methods, the method proposed in this paper can analyze the changes of subjects' brain state over time in more detail. The proposed features can be used to quantify the brain network for accurate analysis. ConclusionsThe proposed dynamic brain network bridges the research gaps in lacking time resolution and arousal conditions in emotion analysis. We can clearly get the dynamic changes of the overall and local details of the brain under high and low arousal conditions. Furthermore, the active segments and brain regions of the subjects were quantified and evaluated by channel norm information.This method can be used to realize the feature extraction and dynamic analysis of the arousal dimension of emotional EEG, further explore the emotional dimension model, and also play an auxiliary role in emotional analysis.

Current clinical phenomenological diagnosis in psychiatry neither captures biologically homologous disease entities nor allows for individualized treatment prescriptions based on neurobiology. In this report, we studied two large samples of cases with schizophrenia, schizoaffective, and bipolar I disorder with psychosis, presentations with clinical features of hallucinations, delusions, thought disorder, affective, or negative symptoms. A biomarker approach to subtyping psychosis cases (called psychosis Biotypes) captured neurobiological homology that was missed by conventional clinical diagnoses. Two samples (called “B-SNIP1” with 711 psychosis and 274 healthy persons, and the “replication sample” with 717 psychosis and 198 healthy persons) showed that 44 individual biomarkers, drawn from general cognition (BACS), motor inhibitory (stop signal), saccadic system (pro- and anti-saccades), and auditory EEG/ERP (paired-stimuli and oddball) tasks of psychosis-relevant brain functions were replicable (r’s from .96–.99) and temporally stable (r’s from .76–.95). Using numerical taxonomy (k-means clustering) with nine groups of integrated biomarker characteristics (called bio-factors) yielded three Biotypes that were virtually identical between the two samples and showed highly similar case assignments to subgroups based on cross-validations (88.5%–89%). Biotypes-1 and -2 shared poor cognition. Biotype-1 was further characterized by low neural response magnitudes, while Biotype-2 was further characterized by overactive neural responses and poor sensory motor inhibition. Biotype-3 was nearly normal on all bio-factors. Construct validation of Biotype EEG/ERP neurophysiology using measures of intrinsic neural activity and auditory steady state stimulation highlighted the robustness of these outcomes. Psychosis Biotypes may yield meaningful neurobiological targets for treatments and etiological investigations.

Financial support was provided by the Office of Naval Research Code 32 (Mine Countermeasures, Acoustics Phenomenology and Modeling Group), and funded by ONR grants N00014-18-1-2062, N00014-19-1-1223, N00014-18-1-2069, and N00014-20-1-2709. Auditory neuroscience in dolphins has largely focused on auditory brainstem responses; however, such measures reveal little about the cognitive processes dolphins employ during echolocation and acoustic communication. The few previous studies of mid- and long-latency auditory-evoked potentials (AEPs) in dolphins report different latencies, polarities, and magnitudes. These inconsistencies may be due to any number of differences in methodology, but these studies do not make it clear which methodological differences may account for the disparities. The present study evaluates how electrode placement and pre-processing methods affect mid- and long-latency AEPs in (Tursiops truncatus). AEPs were measured when reference electrodes were placed on the skin surface over the forehead, the external auditory meatus, or the dorsal surface anterior to the dorsal fin. Data were pre-processed with or without a digital 50-Hz low-pass filter, and the use of independent component analysis to isolate signal components related to neural processes from other signals. Results suggest that a meatus reference electrode provides the highest quality AEP signals for analyses in sensor space, whereas a dorsal reference yielded nominal improvements in component space. These results provide guidance for measuring cortical AEPs in dolphins, supporting future studies of their cognitive auditory processing. Publisher PDF

Objective Recent research has explored the use of continuous EEG (cEEG) monitoring for prognostication of spontaneous cardiac arrest (SCA). However, there is limited literature on the long‐term (post‐hospital discharge) electrographic findings among SCA survivors and their clinical correlates. Our study aims to fill this critical knowledge gap. Methods We retrospectively used our EEG database to identify adults (≥18 years) with SCA history who underwent an outpatient laboratory‐based EEG between 01/01/2011 and 12/31/2018. After electronic medical records (EMR) review, patients with epilepsy history and unclear/poorly documented SCA history were excluded. Outpatient EEGs were reviewed by authors. Acute EEG findings were extracted from the EEG database and EMR. In addition, we extracted data on acute and long‐term neuroimaging findings (CT/MRI), post‐SCA seizures, and anti‐seizure medications (ASM) status. Descriptive analysis and Fisher's exact test were performed. Results We included 32 SCA survivors (50% women; mean age = 52.1 ± 13.6 years) in the study. During a median clinical follow‐up of 28.2 months, 3 patients suffered only clinical seizures, 3 only chronic post‐hypoxic myoclonus, and 5 had both [11 (34.4%) in total]. Interictal epileptiform discharges (IEDs) were noted in one‐third of the patients, which localized to vertex and frontocentral regions in all but one patient. Five (15.6%) of them did not suffer a clinical seizure despite the presence of EAs. Patients who developed epilepsy were significantly more likely to have abnormal neuroimaging findings [10/11 (90.9%)] during the follow‐up compared to the rest of the patients [OR = 25 (95% CI 2.6–>100, P = .002)]. Half of the study cohort was taking ASM at the last follow‐up. Significance Our small study reveals a signature location of IEDs in SCA survivors. Neuroimaging abnormalities seem to be a better indicator of epilepsy development, while EEG may reveal markers of potential epileptogenicity in the absence of clinical seizures. Future, larger studies are needed to confirm our findings.

Brain waves, determined by electrical and magnetic brain recordings (e.g., EEG and MEG), and fluctuating behavioral responses, determined by response time or accuracy measures, are frequently taken to support discrete perception. For example, it has been proposed that humans experience only one conscious percept per brain wave (e.g., during one alpha cycle). However, the proposed link between brain waves and discrete perception is typically rather vague. More importantly, there are many models and aspects of discrete perception and it is often not apparent in what theoretical framework brain wave findings are interpreted and to what specific aspects of discrete perception they relate. Here, we review different approaches to discrete perception and highlight issues with particular interpretations. We then discuss how certain findings on brain waves may relate to certain aspects of discrete perception. The main purpose of this meta-contribution is to give a short overview of discrete models of perception and to illustrate the need to make explicit what aspects of discrete theories are addressed by what aspects of brain wave findings.

Introduction: Several computer-aided diagnosis systems for depression are suggested for use by clinicians to authorize the diagnosis. EEG may be used as an objective analysis tool for identifying depression in the initial stage to avoid it from reaching a severe and permanent state. However, artifact contamination reduces the accuracy in EEG signal processing systems. Methods: This work proposes a novel denoising method based on Empirical Mode Decomposition (EMD) ( with Detrended Fluctuation Analysis (DFA) and wavelet packet transform. Initially, real EEG recordings corresponding to depression patients are decomposed into various mode functions by applying EMD. Then, DFA is used as the mode selection criteria. Further Wavelet Packets Decomposition (WPD)-based evaluation is applied to extract the cleaner signal. Results: Simulations were conducted on real EEG databases for depression to demonstrate the effects of the proposed techniques. To conclude the efficacy of the proposed technique, SNR and MAE were identified. The obtained results indicated improved signal-to-noise ratio and lower values of MAE for the combined EMD-DFA-WPD technique. Additionally, Random Forest and SVM (Support Vector Machine)-based classification revealed the improved accuracy of 98.51% and 98.10% for the proposed denoising technique. Whereas the accuracy of the EMD- DFA is 98.01% and 95.81% and EMD combined with DWT technique equaled 98.0% and 97.21% for the EMD- DFA technique for RF and SVM, respectively, compared to the proposed method. Furthermore, the classification performance for both classifiers was compared with and without denoising to highlight the effects of the proposed technique. Conclusion: Proposed denoising system results in better classification of depressed and healthy individuals resulting in a better diagnosing system. These results can be further analyzed using other approaches as a solution to the mode mixing problem of the EMD approach.

During task‐switching paradigms, both event‐related potentials and time‐frequency analyses show switch and mixing effects at frontal and parietal sites. Switch and mixing effects are associated with increased power in broad frontoparietal networks, typically stronger in the theta band (~4–8 Hz). However, it is not yet known whether mixing and switch costs rely upon common or distinct networks. In this study, we examine proactive and reactive control networks linked to task switching and mixing effects, and whether strength of connectivity in these networks is associated with behavioural outcomes. Participants (n = 197) completed a cued‐trials task‐switching paradigm with concurrent electroencephalography, after substantial task practice to establish strong cue‐stimulus–response representations. We used inter‐site phase clustering, a measure of functional connectivity across electrode sites, to establish cross‐site connectivity from a frontal and a parietal seed. Distinct theta networks were activated during proactive and reactive control periods. During the preparation interval, mixing effects were associated with connectivity from the frontal seed to parietal sites, and switch effects with connectivity from the parietal seed to occipital sites. Lateralised occipital connectivity was common to both switch and mixing effects. After target onset, frontal and parietal seeds showed a similar pattern of connectivity across trial types. These findings are consistent with distinct and common proactive control networks and common reactive networks in highly practised task‐switching performers., During task‐switching paradigms, both event‐related potentials and time‐frequency analyses show switch and mixing effects at frontal and parietal sites. However, it is not yet known whether mixing and switch costs rely upon common or distinct networks. During the preparation interval, mixing effects were associated with connectivity from the frontal seed to parietal sites, and switch effects with connectivity from the parietal seed to occipital sites.

Background:The pathology underlying exploding head syndrome, a parasomnia causing a loud sound/sense of explosion, is not well understood. Kappa rhythm is a type of electroencephalogram alpha band activity with maximum potential between contralateral temporal electrodes We report a case of preceding kappa activity before exploding head syndrome attacks., Case report:A 57-year-old woman complained of explosive sounds for 2 months; a loud sound would transpire every day before sleep onset. She was diagnosed with exploding head syndrome. During polysomnography and the multiple sleep latency test, the exploding head syndrome attacks occurred six times. A kappa wave with activity disappearing a few seconds before most exploding head syndrome attacks was observed. The alpha band power in T3-T4 derivation gradually waxed followed by termination around the attacks., Conclusion:This case demonstrated that the dynamics of kappa activity precede exploding head syndrome attacks. Finding ways to modulate electroencephalogram oscillation could elucidate their causality and lead to therapeutic intervention.

Dementia has become a global public health issue. The current study is focused on diagnosing dementia with Electro Encephalography (EEG). The detection of the advancement of the disease is carried out by detecting the abnormal behavior in EEG measurements. Assessment and evaluation of EEG abnormalities is conducted for all the subjects in order to detect dementia. EEG feature analysis, namely dominant frequency, dominant frequency variability, and frequency prevalence, is done for abnormal and normal subjects and the results are compared. For dementia with Lewy bodies, in 85% of the epochs, the dominant frequency is present in the delta range whereas for normal subjects it lies in the alpha range. The dominant frequency variability in 75% of the epochs is above 4Hz for dementia with Lewy bodies, and in normal subjects at 72% of the epochs, the dominant frequency variability is less than 2Hz. It is observed that these features are sufficient to diagnose dementia with Lewy bodies. The classification of Lewy body dementia is done by using a feed-forward artificial neural network wich proved to have a 94.4% classification accuracy. The classification with the proposed feed-forward neural network has better accuracy, sensitivity, and specificity than the already known methods.

Working memory (WM) has been defined as the active maintenance and flexible updating of goal-relevant information in a form that has limited capacity and resists interference. Complex measures of WM recruit multiple subprocesses, making it difficult to isolate specific contributions of putatively independent subsystems. The present study was designed to determine whether neurophysiological indicators of proposed subprocesses of WM predict WM performance. We recruited 200 individuals defined by care-seeking status and measured neural responses using electroencephalography (EEG), while participants performed four WM tasks. We extracted spectral and time-domain EEG features from each task to quantify each of the hypothesized WM subprocesses: maintenance (storage of content), goal maintenance, and updating. We then used EEG measures of each subprocess as predictors of task performance to evaluate their contribution to WM. Significant predictors of WM capacity included contralateral delay activity and frontal theta, features typically associated with maintenance (storage of content) processes. In contrast, significant predictors of reaction time and its variability included contingent negative variation and the P3b, features typically associated with goal maintenance and updating. Broadly, these results suggest two principal dimensions that contribute to WM performance, tonic processes during maintenance contributing to capacity, and phasic processes during stimulus processing that contribute to response speed and variability. The analyses additionally highlight that reliability of features across tasks was greater (and comparable to that of WM performance) for features associated with stimulus processing (P3b and alpha), than with maintenance (gamma, theta and cross-frequency coupling).

Contains fulltext : 231382.pdf (Publisher’s version ) (Open Access) Results of several neuroimaging studies support the functional equivalence model, which states that motor imagery (MI) and motor execution (ME) involve the same processes, except for the final execution component. In contrast, the motor-cognitive model implies that MI additionally involves frontal executive control processes. However, according to some authors MI may actually be more comparable to motor preparation (MP). In the current electroencephalographic study, a version of the discrete sequence production paradigm was employed in which human participants initially had to prepare a sequence of five finger movements that subsequently had to be executed, imagined, or withheld. MI, ME, and MP were compared by computing event-related (de)-synchronization in the theta, alpha/mu, and beta bands. Results revealed a major increase in frontal theta power during MI as compared to ME and MP. At the end of the examined intervals, a posterior reduction in alpha power was present during ME and MP, but not during MI. Finally, above sensorimotor areas a decrease in beta power was observed that was most pronounced in the case of ME. The increase of frontal theta activity during MI may reflect increased effort, while the absence of a reduction in posterior alpha power suggests no major involvement of visuospatial attention and/or visual imagery. The present findings favor the motor-cognitive model, as it predicts extra involvement of frontal executive processes during MI. 16 p.

Brain source localization has been consistently implemented over the recent years to elucidate complex brain operations, pairing the high temporal resolution of the EEG with the high spatial estimation of the estimated sources. This review paper aims to present the basic principles of Electrical source imaging (ESI) in the context of the recent progress for solving the forward and the inverse problems, and highlight the advantages and limitations of the different approaches. As such, a synthesis of the current state-of-the-art methodological aspects is provided, offering a complete overview of the present advances with regard to the ESI solutions. Moreover, the new dimensions for the analysis of the brain processes are indicated in terms of clinical and cognitive ESI applications, while the prevailing challenges and limitations are thoroughly discussed, providing insights for future approaches that could help to alleviate methodological and technical shortcomings.

A small physical change in the eye influences the entire neural information process along the visual pathway, causing perceptual errors and behavioral changes. Astigmatism, a refractive error in which visual images do not evenly focus on the retina, modulates visual perception, and the accompanying neural processes in the brain. However, studies on the neural representation of visual stimuli in astigmatism are scarce. We investigated the relationship between retinal input distortions and neural bias in astigmatism and how modulated neural information causes a perceptual error. We induced astigmatism by placing a cylindrical lens on the dominant eye of human participants, while they reported the orientations of the presented Gabor patches. The simultaneously recorded electroencephalogram activity revealed that stimulus orientation information estimated from the multivariate electroencephalogram activity was biased away from the neural representation of the astigmatic axis and predictive of behavioral bias. The representational neural dynamics underlying the perceptual error revealed the temporal state transition; it was transiently dynamic and unstable (approximately 350 ms from stimulus onset) that soon stabilized. The biased stimulus orientation information represented by the spatially distributed electroencephalogram activity mediated the distorted retinal images and biased orientation perception in induced astigmatism., In this study, we investigated the neural sources of perceptual error induced by the astigmatic distortion of the retinal image. We found a systematic distortion in the neural orientation representation estimated from the multivariate analysis of the electroencephalogram recordings, which supports the observed patterns of perceptual error.