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Over the years, research in neuroscience-driven marketing has progressively delved into the conscious and subconscious behaviors of consumers. Existing Electroencephalography (EEG)-based studies related to consumer preferences toward products are not comprehensive. Due to non-stationarity issues of EEG, a significant variance is observed in inter-trial and inter-session EEG signals of a subject, which leads to challenges in building a universal consumer preference model across diverse subjects, sessions, and tasks. Transfer learning mitigates this challenge by utilizing data or knowledge from similar subjects, sessions, or tasks to improve the learning process for a new subject, session, or task, thereby enhancing overall model performance. Moreover, high-dimensional EEG features often lead to poor classification results. Therefore, selecting meaningful or refined features is of utmost importance for classification. Therefore, we propose a robust EEG-based neuromarketing framework combining deep transfer learning, spatial attention model, and deep neural networks. The proposed framework predicts the consumer choices (in terms of “likes” and “dislikes”) for e-commerce products. Initially, the knowledge distillation is performed from the pre-trained network to the proposed model, and the model is trained on the connectivity features of EEG. Next, the attention-based features are extracted from high-level connectivity features using the spatial attention model (Convolutional Block Attention Module: CBAM). CBAM extracts the attention feature maps along channel and spatial dimensions for adaptive feature refinement. The refined features improve the classification accuracy. Finally, the attention-based features are passed to the 2D CNN-based deep learning model to evaluate consumer choices. The proposed model achieves 95.60% classification accuracy with the experimental dataset. The proposed model achieves a significant improvement of 2.60% over the existing neuromarketin, Validerad;2024;Nivå 2;2024-03-18 (hanlid);Full text license: CC BY-NC-ND

Over the years, research in neuroscience-driven marketing has progressively delved into the conscious and subconscious behaviors of consumers. Existing Electroencephalography (EEG)-based studies related to consumer preferences toward products are not comprehensive. Due to non-stationarity issues of EEG, a significant variance is observed in inter-trial and inter-session EEG signals of a subject, which leads to challenges in building a universal consumer preference model across diverse subjects, sessions, and tasks. Transfer learning mitigates this challenge by utilizing data or knowledge from similar subjects, sessions, or tasks to improve the learning process for a new subject, session, or task, thereby enhancing overall model performance. Moreover, high-dimensional EEG features often lead to poor classification results. Therefore, selecting meaningful or refined features is of utmost importance for classification. Therefore, we propose a robust EEG-based neuromarketing framework combining deep transfer learning, spatial attention model, and deep neural networks. The proposed framework predicts the consumer choices (in terms of “likes” and “dislikes”) for e-commerce products. Initially, the knowledge distillation is performed from the pre-trained network to the proposed model, and the model is trained on the connectivity features of EEG. Next, the attention-based features are extracted from high-level connectivity features using the spatial attention model (Convolutional Block Attention Module: CBAM). CBAM extracts the attention feature maps along channel and spatial dimensions for adaptive feature refinement. The refined features improve the classification accuracy. Finally, the attention-based features are passed to the 2D CNN-based deep learning model to evaluate consumer choices. The proposed model achieves 95.60% classification accuracy with the experimental dataset. The proposed model achieves a significant improvement of 2.60% over the existing neuromarketin, Validerad;2024;Nivå 2;2024-03-18 (hanlid);Full text license: CC BY-NC-ND

Deep learning has the potential to be used to improve and streamline EEG analysis. At the present, classifiers and supervised learning dominate the field. Supervised learning depends on target labels which most often are created by human experts manually classifying data. A problem with supervised learning is intra- and interrater agreement which in some instances are far from perfect. This can affect the training and make evaluation more difficult. This thesis includes three papers where self-supervised deep neural networks were developed. In self-supervised learning, the input data to the networks themselves contain structures that are used as targets for the training and no labeling is necessary. In paper I, deep neural networks were trained to increase the number of-, or to recreate missing EEG-channels. The performance was at least on the same level as that of spherical interpolation, but unlike in the case of interpolation, missing data does not have to be identified manually first. Papers II and III involved developing deep neural networks for clustering analysis. The networks produced two-dimensional representations of EEG data and the training strategy was based on the principle of t-distributed stochastic neighbor embedding (t-SNE). In paper II, comparisons were made to parametric t-SNE and EEG-features obtained from time-frequency methods. The deep neural networks produced more distinct clustering when tested on data annotated for epileptiform discharges, seizure activity, or sleep-wakefulness. In paper III, the newly developed method was used to compare annotations of epileptiform discharges. Two experts performed independent annotations and classifiers were trained on these, using supervised learning, which in turn produced new annotations. The agreement when comparing two sets of annotations was not larger between the two experts than between an expert and a classifier. The analysis showed that differences in the annotations by the experts infl, Funding: The work was funded by Saad Nagi (grants RÖ-974228, RÖ-962769, and RÖ-941377), Magnus Thordstein (grant RÖ-986017), and Håkan Olaus-son (grants LIO-936017 and RÖ-941359).

Electroencephalography (EEG) stands as a pioneering tool at the intersection of neuroscience and technology, offering unprecedented insights into human emotions. Through this comprehensive review, we explore the challenges and opportunities associated with EEG-based emotion recognition. While recent literature suggests promising high accuracy rates, these claims necessitate critical scrutiny for their authenticity and applicability. The article highlights the significant challenges in generalizing findings from a multitude of EEG devices and data sources, as well as the difficulties in data collection. Furthermore, the disparity between controlled laboratory settings and genuine emotional experiences presents a paradox within the paradigm of emotion research. We advocate for a balanced approach, emphasizing the importance of critical evaluation, methodological standardization, and acknowledging the dynamism of emotions for a more holistic understanding of the human emotional landscape., Postprint (published version)

Entre 2013 et 2019, l’auteur s’est lancé dans une initiative ambitieuse, créant et publiant chaque semaine un nouveau concept de produit. L’objectif principal était d’obtenir une rétroaction participative du public. Malgré un fort engouement médiatique, cette approche n’a pas réussi à identifier de manière adéquate les fonctionnalités les plus et les moins appréciées de chaque concept. Pour résoudre cette problématique, l’auteur a élaboré et testé une nouvelle méthode novatrice combinant l’animation 3D avec l’utilisation de logiciels de rétroaction neurale. Cette recherche a abouti à la création d’une maquette et d’une vidéo animée présentant un nouveau concept : le bac de collecte des matières résiduelles autonome Nestor. Quatre-vingt-quatre entrevues semi-dirigées ont suivi avec des clients potentiels. Lors de chaque présentation, les participants ont porté des casques électroencéphalographiques (EEG) pour enregistrer leurs activités neuro-électriques. Trois programmes d’interprétation des données neurales ont été utilisés pour générer des données de rétroaction, couvrant le niveau d’intérêt et d’excitation, l’intensité des expressions faciales et la détection d’émotions en temps réel. À la fin de chaque présentation, un questionnaire écrit a été administré aux participants pour comparer les perceptions entre les différents modes de présentation et les types de rétroaction, qu'elles soient écrites ou neurales. Les résultats ont montré des tendances intéressantes. La rétroaction neurale, combinée au questionnaire, a permis de mieux comprendre les fonctions du concept les plus et les moins appréciées, ainsi que les émotions ressenties par les participants. Cette approche a révélé des nuances entre les groupes Maquette et Animation, soulignant la valeur ajoutée des programmes de rétroaction neurale dans le processus de développement de nouveaux produits (DNP). En résumé, cette thèse exploratoire représente une avancée significative dans le domaine du développement de, Between 2013 and 2019, the author embarked on an ambitious initiative, creating and publishing a new product concept every week. The primary objective was to obtain participative feedback from the public. Despite considerable media enthusiasm, this approach failed to adequately identify the most and least appreciated features of each concept. To address this issue, the author developed and tested an innovative method that combines 3D animation with the use of three neural feedback software. This research led to the creation of a mock-up and an animated video featuring a new concept: the autonomous waste collection bin Nestor. Eighty-four semi-structured interviews followed with potential clients. During each presentation, participants wore electroencephalographic (EEG) headsets to record their neuro-electric activities. Three neural data interpretation programs were used to generate feedback data, covering levels of interest and excitement, facial expression intensity, and real-time emotion detection. At the end of each presentation, a written questionnaire was administered to participants to compare perceptions between different presentation modes and types of feedback, whether written or neural. The results showed interesting trends. Neural feedback, combined with the questionnaire, provided insights into the most and least appreciated functions of the concept, as well as the emotions experienced by participants. This approach revealed nuances between the Mock-up and Animation groups, highlighting the added value of neural feedback programs in the NPD process. In summary, this exploratory thesis represents a significant advancement in the field of product development by introducing innovative tools to understand and improve consumer reactions to new concepts. It opens promising perspectives by suggesting the use of neural feedback and the potential integration of artificial intelligence in the design of new products, making them more tailored to the need

Anxiety disorders are characterized by the heightened anticipation of future threat. People with anxiety can go to extremes to avoid feared situations, and fear can spread to related situations. For example, being mocked by a friend may over time lead to someone becoming anxious to engage in social situations in general. In this thesis, I aim to elucidate the mechanisms that drive the anticipation and generalization of future threats by utilizing insights from episodic memory. First, I examined how memory for unique experiences can generalize. Across two experiments, I show that we can memorize an event in high levels of detail. However, with repeated experience we also learn what to expect. This knowledge biases our memory towards what we think is generally true, making it less accurate. This bias is enhanced when we experience high levels of stress. This means that for feared situations, your memory may be more negative than it was because you expected a negative outcome. Beyond memory of your past experiences, episodic memory also enables the imagination of potential future events. In two further experiments, we first showed that while non-anxious people had better memory for imagined emotional future events, anxious people did not and tended to forget more details. Second, we showed that when people imagined positive future events similar to a feared situation they were about to engage in, their memory for that feared event was more positive than when they imagined random positive events. Together, these findings suggest that in anxiety emotional (future) memories may become more generalized and biased towards negative expectations, but that positive future thinking can help reduce negative expectations and ease engaging in feared experiences.

Children form stereotyped expectations about the appropriateness of certain emotions for men versus women during the preschool years, based on cues from their social environments. Although ample research has examined the development of gender stereotypes in children, little is known about the neural responses that underlie the processing of gender-stereotyped emotions in children. Therefore, the current study examined whether 3-year-olds differ in the neural processing of emotional stimuli that violate gender stereotypes (i.e., male faces with fearful or happy expressions) or confirm gender stereotypes (i.e., female faces with fearful or happy expressions), and whether boys and girls differ in their neural processing of the violation and confirmation of gender stereotypes. Data from 72 3-year-olds (±6 months, 43% boy) were obtained from the YOUth Cohort Study. Electroencephalography data were obtained when children passively viewed male and female faces displaying neutral, happy, or fearful facial expressions. This study provided first indications that happy male faces elicited larger P1 amplitudes than happy female faces in preschool children, which might reflect increased attentional processing of stimuli that violate gender stereotypes. Moreover, there was preliminary evidence that girls had larger negative central (Nc) responses, associated with salience processing, toward female happy faces than male happy faces, whereas boys had larger Nc responses toward male happy faces than female happy faces. No gender differences were found in the processing of neutral and fearful facial expressions. Our results indicate that electroencephalography measurements can provide insights into preschoolers? gender-stereotype knowledge about emotions, potentially by looking at the early occipital and late fronto-central responses.

Linguistic categories can impact visual perception. For instance, learning that two objects have different names can enhance their discriminability. Previous studies have identified a typical pattern of categorical perception, characterized by faster discrimination of stimuli from different categories, a neural mismatch response during early visual processing (100–200 ms), and effects restricted to the right visual field. However, it remains unclear whether language affects perception online or through long‐term changes to mental representations in memory. To address this, we tested the impact of newly learned object categories with and without memory consolidation during sleep. We replicated the canonical pattern of categorical perception for categories that underwent consolidation. Without consolidation, linguistic categories still influenced early visual processing but with distinct neural dynamics. Therefore, we found evidence of both transient and long‐term effects of language on perception and conclude that memory consolidation plays a crucial role in shaping how linguistic categories modulate perception., Peer Reviewed

2023 Acuerdos transformativos CRUE, Recent psychology and neuroscience studies have used tactile stimuli in patients, concluding after their experiments that touch is a sense tightly linked to emotions. In parallel, a new way of seeing flms, 4D cinema, has added new stimuli to the traditional audiovisual via, including the tactile vibration. In this work, we have studied the brain activity of audience while viewing a scene flmed and directed by us and with an emotional content, under two diferent conditions: 1) image+sound, 2) image+sound+vibro-tactile stimulation. We have designed a glove where pulse trains are generated in coin motors at specifc moments and recorded 35 viewers’ electroencephalograms (EEGs) to evaluate the impact of the vibro-tactile stimulation during the flm projection. Hotelling’s T-squared results show higher brain intensity if the tactile stimulus is received during the viewing than if no tactile stimulus is injected. Condition 1 participants showed activation in left and right orbitofrontal areas, whereas Condition 2 they also showed activities in right superior frontal and right-medial frontal areas. We conclude that the addition of vibrotactile stimulus increases the brain activity in areas linked with attentional processes, while producing a higher intensity in those related to emotional processes., CRUE-CSIC, Depto. de Ciencias de la Comunicación Aplicada, Fac. de Ciencias de la Información, TRUE, pub, APC financiada por la UCM

Prediction plays a critical role in comprehending human language. Many theoretical and computational models have attempted to characterize how we use context to facilitate language processing in noisy environments either with or without relying on predictive processing. Despite these attempts, we do not yet have a complete understanding of the role of prediction in language processing. Predictive coding models have recently gained popularity as potential architectures for the role of prediction during language comprehension. These models suggest that predictions about bottom-up inputs are continuously generated from higher cortical levels to lower levels in a hierarchical manner – i.e., a particular level generates predictions about the next lower level. As bottom-up input is encounter by each level of processing, prediction error is computed by comparing the input with the top-down prediction. The goal of this dissertation was to assess whether predictive coding models can account for the time course of information retrieval during predictive language processing. Specifically, the studies described examine the time course of pre-activations of lexical and sub-lexical features in both monolinguals and bilinguals using a combination of decoding electroencephalogram (EEG) with machine-learning classifiers and mass univariate event-related potential analysis. Chapter 1 describes an experiment that compared three frequently used models for signal classification – support vector machines (SVM), linear discriminant analysis (LDA), and random forest (RF) to determine which is best-suited for analyzing word pair prediction paradigms. Results showed that SVM was the best performing classifier of the three within two data sets from separate visual word priming paradigms. Chapter 2 describes an experiment which used EEG decoding with SVM classifiers and mass univariate ERP analyses to identify the time course of information retrieval prior to the onset of accurately predicted

Principal component analysis (PCA) has been widely employed for dimensionality reduction prior to multivariate pattern classification (decoding) in EEG research. The goal of the present study was to provide an evaluation of the effectiveness of PCA on decoding accuracy (using support vector machines) across a broad range of experimental paradigms. We evaluated several different PCA variations, including group-based and subject-based component decomposition and the application of Varimax rotation or no rotation. We also varied the numbers of PCs that were retained for the decoding analysis. We evaluated the resulting decoding accuracy for seven common event-related potential components (N170, mismatch negativity, N2pc, P3b, N400, lateralized readiness potential, and error-related negativity). We also examined more challenging decoding tasks, including decoding of face identity, facial expression, stimulus location, and stimulus orientation. The datasets also varied in the number and density of electrode sites. Our findings indicated that none of the PCA approaches consistently improved decoding performance related to no PCA, and the application of PCA frequently reduced decoding performance. Researchers should therefore be cautious about using PCA prior to decoding EEG data from similar experimental paradigms, populations, and recording setups.

BACKGROUND: Chronic pain is a leading cause of disability worldwide. Fibromyalgia is a particularly debilitating form of widespread chronic pain. Fibromyalgia remains poorly understood, and treatment options are limited or moderately effective at best. Here, we present a protocol for a mechanistic study investigating the effects of psychedelic-assisted-therapy in a fibromyalgia population. The principal focus of this trial is the central mechanism(s) of psilocybin-therapy i.e., in the brain and on associated mental schemata, primarily captured by electroencephalography (EEG) recordings of the acute psychedelic state, plus pre and post Magnetic Resonance Imaging (MRI). METHODS: Twenty participants with fibromyalgia will complete 8 study visits over 8 weeks. This will include two dosing sessions where participants will receive psilocybin at least once, with doses varying up to 25mg. Our primary outcomes are 1) Lempel-Ziv complexity (LZc) recorded acutely using EEG, and the 2) the (Brief Experiential Avoidance Questionnaire (BEAQ) measured at baseline and primary endpoint. Secondary outcomes will aim to capture broad aspects of the pain experience and related features through neuroimaging, self-report measures, behavioural paradigms, and qualitative interviews. Pain Symptomatology will be measured using the Brief Pain Inventory Interference Subscale (BPI-IS), physical and mental health-related function will be measured using the 36-Item Short Form Health Survey (SF-36). Further neurobiological investigations will include functional MRI (fMRI) and diffusion tensor imaging (changes from baseline to primary endpoint), and acute changes in pre- vs post-acute spontaneous brain activity - plus event-related potential functional plasticity markers, captured via EEG. DISCUSSION: The results of this study will provide valuable insight into the brain mechanisms involved in the action of psilocybin-therapy for fibromyalgia with potential implications for the therapeutic action of

OBJECTIVE: ADHD is a prevalent neurodevelopmental disorder characterized by symptoms of inattention and hyperactivity-impulsivity. Impairments in executive functioning (EF) are central to models of ADHD, while alpha-band spectral power event-related decreases (ERD) have emerged as a putative electroencephalography (EEG) biomarker of EF in ADHD. Little is known about the roles of EF and alpha ERD and their interactions with symptoms of ADHD. METHOD: We estimated network models of ADHD symptoms and integrated alpha ERD measures into the symptom network. RESULTS: EF emerges as a bridge network node connecting alpha ERD and the hyperactivity/impulsivity and inattention symptoms. We found that EF most closely relates to a subset of symptoms, namely the motoric symptoms, seat (difficulty staying seated), and runs (running or climbing excessively). CONCLUSIONS: EF functions as a bridge node connecting alpha ERD and the ADHD symptom network. Motoric-type symptoms and EF deficits may constitute important nodes in the interplay between behavior/symptoms, cognition, and neurophysiological markers of ADHD.

Musculoskeletal trauma often leads to lasting psychological impacts stemming from concerns of future injuries. Often referred to as kinesiophobia or re-injury anxiety, such concerns have been shown to hinder return to physical activity and are believed to increase the risk for secondary injuries. Screening for re-injury anxiety is currently restricted to subjective questionnaires, which are prone to self-report bias. We introduce a novel approach to objectively identify electrocortical activity associated with the threat of destabilising perturbations. We aimed to explore its feasibility among non-injured persons, with potential future implementation for screening of re-injury anxiety. Twenty-three participants stood blindfolded on a translational balance perturbation platform. Consecutive auditory stimuli were provided as low (neutral stimulus [CS–]) or high (conditioned stimulus [CS+]) tones. For the main experimental protocol (Protocol I), half of the high tones were followed by a perturbation in one of eight unpredictable directions. A separate validation protocol (Protocol II) requiring voluntary squatting without perturbations was performed with 12 participants. Event-related potentials (ERP) were computed from electroencephalography recordings and significant time-domain components were detected using an interval-wise testing procedure. High-amplitude early contingent negative variation (CNV) waves were significantly greater for CS+ compared with CS– trials in all channels for Protocol I (> 521-800ms), most prominently over frontal and central midline locations (P ≤ 0.001). For Protocol II, shorter frontal ERP components were observed (541-609ms). Our test paradigm revealed electrocortical activation possibly associated with movement-related fear. Exploring the discriminative validity of the paradigm among individuals with and without self-reported re-injury anxiety is warranted.

The paper provides brief overview of Brain-Computer Interface (BCI), highlighting BCI-based assisting technologies for disabled persons in healthcare applications as one of the key priorities. A proposed solution for implementation OpenBCI-based hardware and software in research on computer typing assistance system described, including general view of experimental test bench and software approach description. An experimental verification of a possible computer typing assistance system is presented, as well as basic test results obtained along with their interpretation and discussion.

Biometric technology has gained immense popularity as an effective solution for enhancing cybersecurity, specifically in countering financial fraud and security threats. EEG-based authentication is unique among biometric authentication methods due to its unparalleled confidentiality and non-replicability. This study explores the feasibility of using motor imagery tasks and rest conditions for human authentication. Ten physically fit subjects, aged between 20 and 28 years, participated voluntarily in the study. The subjects perform imaginary tasks involving their left and right-hand movement. Each task lasted for two minutes, separated by a one-minute break, and EEG data were collected using the EPOC+ device, which features 14-channel electrodes. The sampling frequency was set at 128 Hz. To extract relevant frequency information, Butterworth bandpass filters were employed to extract the alpha (8-13Hz), beta (14-30Hz), and gamma (30-42Hz) frequency bands. Linear features, such as power spectral density (PSD), were obtained using the Welch Method and the Burg Method, while Spectral Entropy was used to extract non-linear features. Statistical features mean, median, standard deviation, minimum, and maximum were derived from the PSD, and spectral entropy was used as input for the classifiers. Multiple classifiers, including k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), Decision Tree and Naive Bayes, were employed for the classification task. The Welch method combined with the Support Vector Machine classifier achieved a higher classification accuracy of 96.83% for the beta waves from channels C3, C4, O1, and O2, corresponding to the frontal and occipital lobes. Interestingly, the rest conditions exhibited a higher classification accuracy of 96.83% compared to the motor-imagery tasks, which achieved 96.04%. The utilization of motor imagery tasks and rest conditions, along with the application of advanced classification techniques, holds promise for the developme

BackgroundSleep disturbances are a prevalent and complex comorbidity in neurodevelopmental disorders (NDDs). Dup15q syndrome (duplications of 15q11.2-13.1) is a genetic disorder highly penetrant for NDDs such as autism and intellectual disability and it is frequently accompanied by significant disruptions in sleep patterns. The 15q critical region harbors genes crucial for brain development, notably UBE3A and a cluster of gamma-aminobutyric acid type A receptor (GABAAR) genes. We previously described an electrophysiological biomarker of the syndrome, marked by heightened beta oscillations (12-30 Hz) in individuals with Dup15q syndrome, akin to electroencephalogram (EEG) alterations induced by allosteric modulation of GABAARs. Those with Dup15q syndrome exhibited increased beta oscillations during the awake resting state and during sleep, and they showed profoundly abnormal NREM sleep. This study aims to assess the translational validity of these EEG signatures and to delve into their neurobiological underpinnings by quantifying sleep physiology in chromosome-engineered mice with maternal (matDp/ + mice) or paternal (patDp/ + mice) inheritance of the full 15q11.2-13.1-equivalent duplication, and mice with duplication of just the UBE3A gene (Ube3a overexpression mice; Ube3a OE mice) and comparing the sleep metrics with their respective wildtype (WT) littermate controls.MethodsWe collected 48-h EEG/EMG recordings from 35 (23 male, 12 female) 12-24-week-old matDp/ + , patDp/ + , Ube3a OE mice, and their WT littermate controls. We quantified baseline sleep, sleep fragmentation, spectral power dynamics during sleep states, and recovery following sleep deprivation. Within each group, distinctions between Dup15q mutant mice and WT littermate controls were evaluated using analysis of variance (ANOVA) and student's t-test. The impact of genotype and time was discerned through repeated measures ANOVA, and significance was established at p < 0.05.ResultsOur study r

Stuttering is often thought of as simply an impairment in speech production. However, some studies have indicated that people who stutter (PWS) also experience temporal processing impairments which affect perception of speech. In particular, previous behavioral and electrophysiology (EEG) studies have demonstrated time delays in processing speech stimuli in PWS. Most research to this date has only examined these timing delays at the level of the cerebral cortex, which represents the later stages of processing. Very few studies have examined delays at the level of the brainstem, and no study has looked at processing in both the cortex and the brainstem. This study recruited adults who stutter (AWS) and adults who do not stutter (AWNS) to examine how each group processes speech at both subcortical and cortical levels. Participants completed a perceptual test to determine how well they perceived speech and underwent EEG testing to measure cortical and subcortical electrical activity while listening to speech stimuli. Compared to AWNS, AWS showed poorer neural representations of the speech stimulus in the brainstem and delays at the cortical level. Perceptual testing in AWS also seemed to show a poorer perception of phoneme boundaries in words compared to AWNS. Our research suggests that temporal processing deficits are a factor in stuttering and that these deficits arise at early levels of the auditory system. These findings might call for an update of current speech therapy methods to address the timing delays that AWS experience in speech processing.

Stress is revealed by the inability of individuals to cope with their environment, which is frequently evidenced by a failure to achieve their full potential in tasks or goals. This study aims to assess the feasibility of estimating the level of stress that the user is perceiving related to a specific task through an electroencephalograpic (EEG) system. This system is integrated with a Serious Game consisting of a multi-level stress driving tool, and Deep Learning (DL) neural networks are used for classification. The game involves controlling a vehicle to dodge obstacles, with the number of obstacles increasing based on complexity. Assuming that there is a direct correlation between the difficulty level of the game and the stress level of the user, a recurrent neural network (RNN) with a structure based on gated recurrent units (GRU) was used to classify the different levels of stress. The results show that the RNN model is able to predict stress levels above current state-of-the-art with up to 94% accuracy in some cases, suggesting that the use of EEG systems in combination with Serious Games and DL represents a promising technique in the prediction and classification of mental stress levels.

Although previous research demonstrated socioeconomic status (SES) differences in the neurodevelopment of auditory systems throughout childhood, it remains unclear whether such differences persist into adulthood, especially in the absence of attentional or language demands. Here, we administered a passive auditory oddball paradigm with tones to 73 young adults from diverse childhood socioeconomic backgrounds to examine the mismatch negativity (MMN) event related potential (ERP) component as a neural index of auditory perception. As subjective ratings of childhood social status may reflect experiences not fully captured by objective childhood SES, we asked participants about both the highest level of parent education (i.e., objective childhood SES) and their subjective family social status when they were 10 years old. We did not find any links between childhood parent education or subjective family social status and MMN amplitude or latency in adulthood. This research contributes to our understanding of how childhood SES relates to auditory perception, a foundational building block for the auditory system, in young adults.

Musculoskeletal trauma often leads to lasting psychological impacts stemming from concerns of future injuries. Often referred to as kinesiophobia or re-injury anxiety, such concerns have been shown to hinder return to physical activity and are believed to increase the risk for secondary injuries. Screening for re-injury anxiety is currently restricted to subjective questionnaires, which are prone to self-report bias. We introduce a novel approach to objectively identify electrocortical activity associated with the threat of destabilising perturbations. We aimed to explore its feasibility among non-injured persons, with potential future implementation for screening of re-injury anxiety. Twenty-three participants stood blindfolded on a translational balance perturbation platform. Consecutive auditory stimuli were provided as low (neutral stimulus [CS–]) or high (conditioned stimulus [CS+]) tones. For the main experimental protocol (Protocol I), half of the high tones were followed by a perturbation in one of eight unpredictable directions. A separate validation protocol (Protocol II) requiring voluntary squatting without perturbations was performed with 12 participants. Event-related potentials (ERP) were computed from electroencephalography recordings and significant time-domain components were detected using an interval-wise testing procedure. High-amplitude early contingent negative variation (CNV) waves were significantly greater for CS+ compared with CS– trials in all channels for Protocol I (> 521-800ms), most prominently over frontal and central midline locations (P ≤ 0.001). For Protocol II, shorter frontal ERP components were observed (541-609ms). Our test paradigm revealed electrocortical activation possibly associated with movement-related fear. Exploring the discriminative validity of the paradigm among individuals with and without self-reported re-injury anxiety is warranted.

The female reproductive years are characterized by fluctuations in ovarian hormones across the menstrual cycle, which have the potential to modulate neurophysiological and behavioral dynamics. Menstrually-related mood disorders (MRMDs) comprise cognitive-affective or somatic symptoms that are thought to be triggered by the rapid fluctuations in ovarian hormones in the luteal phase of the menstrual cycle. MRMDs include premenstrual syndrome (PMS), premenstrual dysphoric disorder (PMDD), and premenstrual exacerbation (PME) of other psychiatric disorders. Electroencephalography (EEG) non-invasively records in vivo synchronous activity from populations of neurons with high temporal resolution. The present overview sought to systematically review the current state of task-related and resting-state EEG investigations on MRMDs. Preliminary evidence indicates lower alpha asymmetry at rest being associated with MRMDs, while one study points to the effect being lutealphase specific. Moreover, higher luteal spontaneous frontal brain activity (slow/fast wave ratio as measured by the delta/beta power ratio) has been observed in persons with MRMDs, while sleep architecture results point to potential circadian rhythm disturbances. In this review, we discuss the quality of study designs as well as future perspectives and challenges of supplementing the diagnostic and scientific toolbox for MRMDs with EEG.

The female reproductive years are characterized by fluctuations in ovarian hormones across the menstrual cycle, which have the potential to modulate neurophysiological and behavioral dynamics. Menstrually-related mood disorders (MRMDs) comprise cognitive-affective or somatic symptoms that are thought to be triggered by the rapid fluctuations in ovarian hormones in the luteal phase of the menstrual cycle. MRMDs include premenstrual syndrome (PMS), premenstrual dysphoric disorder (PMDD), and premenstrual exacerbation (PME) of other psychiatric disorders. Electroencephalography (EEG) non-invasively records in vivo synchronous activity from populations of neurons with high temporal resolution. The present overview sought to systematically review the current state of task-related and resting-state EEG investigations on MRMDs. Preliminary evidence indicates lower alpha asymmetry at rest being associated with MRMDs, while one study points to the effect being lutealphase specific. Moreover, higher luteal spontaneous frontal brain activity (slow/fast wave ratio as measured by the delta/beta power ratio) has been observed in persons with MRMDs, while sleep architecture results point to potential circadian rhythm disturbances. In this review, we discuss the quality of study designs as well as future perspectives and challenges of supplementing the diagnostic and scientific toolbox for MRMDs with EEG.

Musculoskeletal trauma often leads to lasting psychological impacts stemming from concerns of future injuries. Often referred to as kinesiophobia or re-injury anxiety, such concerns have been shown to hinder return to physical activity and are believed to increase the risk for secondary injuries. Screening for re-injury anxiety is currently restricted to subjective questionnaires, which are prone to self-report bias. We introduce a novel approach to objectively identify electrocortical activity associated with the threat of destabilising perturbations. We aimed to explore its feasibility among non-injured persons, with potential future implementation for screening of re-injury anxiety. Twenty-three participants stood blindfolded on a translational balance perturbation platform. Consecutive auditory stimuli were provided as low (neutral stimulus [CS–]) or high (conditioned stimulus [CS+]) tones. For the main experimental protocol (Protocol I), half of the high tones were followed by a perturbation in one of eight unpredictable directions. A separate validation protocol (Protocol II) requiring voluntary squatting without perturbations was performed with 12 participants. Event-related potentials (ERP) were computed from electroencephalography recordings and significant time-domain components were detected using an interval-wise testing procedure. High-amplitude early contingent negative variation (CNV) waves were significantly greater for CS+ compared with CS– trials in all channels for Protocol I (> 521-800ms), most prominently over frontal and central midline locations (P ≤ 0.001). For Protocol II, shorter frontal ERP components were observed (541-609ms). Our test paradigm revealed electrocortical activation possibly associated with movement-related fear. Exploring the discriminative validity of the paradigm among individuals with and without self-reported re-injury anxiety is warranted.

The recent commercialisation of the first disease-modifying drugs for Alzheimer's disease emphasises the need for consensus recommendations on the rational use of biomarkers to diagnose people with suspected neurocognitive disorders in memory clinics. Most available recommendations and guidelines are either disease-centred or biomarker-centred. A European multidisciplinary taskforce consisting of 22 experts from 11 European scientific societies set out to define the first patient-centred diagnostic workflow that aims to prioritise testing for available biomarkers in individuals attending memory clinics. After an extensive literature review, we used a Delphi consensus procedure to identify 11 clinical syndromes, based on clinical history and examination, neuropsychology, blood tests, structural imaging, and, in some cases, EEG. We recommend first-line and, if needed, second-line testing for biomarkers according to the patient's clinical profile and the results of previous biomarker findings. This diagnostic workflow will promote consistency in the diagnosis of neurocognitive disorders across European countries.

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It is well established that the Self has a unique representation in the social brain, as evident from the Self-Referential Effect (SRE). However, the timing and neural mechanisms underlying the representation of individuals with varying degrees of closeness and emotional relevance to the Self remain unclear. Twenty-two participants read 260 personality traits and decided whether they described themselves, a close friend, or an admired celebrity. A strong Self-Referential Effect (SRE) was found at behavioral, ERP, and neuroimaging levels. Three anterior ERP components were identified as sensitive to social information: a P200 (250-350 ms) responding to famous others' traits, a P600 (500-700 ms) responding to self-trait processing, and a late positivity (800-950 ms) responding to self-trait processing and close traits. Source reconstructions revealed partially overlapping but distinct neural sources for each individual. The right precuneus (bodily self) and inferior frontal areas (inner voice) were active only during self-processing, while the right medial prefrontal cortex (BA10) was consistently active across tasks, showing a robust SRE. These findings provide insights into the neural mechanisms underlying the representation of the Self in social contexts.

1. Premessa: storia e paradigmi. – 2. Un’esemplificazione sulla validità delle asserzioni: cosa occorre per corroborare una metodologia neuroscientifica? – 3. Tecniche e misure neuroscientifiche per il giudizio. – 4. Infine, la prospettiva Neurolaw

Electroencephalografic (EEG) data are complex multi-dimensional time-series which are very useful in many different applications, i.e., from diagnostics of epilepsy to driving brain-computer interface systems. Their classification is still a challenging task, due to the inherent within- and between-subject variability as well as their low signal-to-noise ratio. On the other hand, the reconstruction of raw EEG data is even more difficult because of the high temporal resolution of these signals. Recent literature has proposed numerous machine and deep learning models that could classify, e.g., different types of movements, with an accuracy in the range 70% to 80% (with 4 classes). On the other hand, a limited number of works targetted the reconstruction problem, with very limited results. In this work, we propose vEEGNet, a DL architecture with two modules, i.e., an unsupervised module based on variational autoencoders to extract a latent representation of the multi-channel EEG data, and a supervised module based on a feed-forward neural network to classify different movements. Furthermore, to build the encoder and the decoder of VAE we exploited the well-known EEGNet network, specifically designed since 2016 to process EEG data. We implemented two slightly different architectures of vEEGNet, thus showing state of the art classification performance, and the ability to reconstruct both low frequency and middle-range components of the raw EEG. Although preliminary, this work is promising as we found out that the low-frequency reconstructed signals are consistent with the so-called motor-related cortical potentials, very specific and well-known motor-related EEG patterns and we could improve over previous literature by reconstructing faster EEG components, too. Further investigations are needed to explore the potentialities of vEEGNet in reconstructing the full EEG data, to generate new samples, and to study the relationship between classification and reconstruction perf

Stroke is a devastating and common medical condition, which one in four people will experience in their lifetime. Strokes often occur suddenly and without any prior signs, and are hard to notice as they occur invisibly within the brain; only the appearance of outward symptoms give away the ongoing attack. This, combined with the fact that the damage done by the stroke worsens the longer it goes untreated, makes more affordable and reliable stroke detection methods highly desirable. The goal of our project was to examine the feasibility of detecting stroke in patients using only brain activity data collected with easily used and relatively low cost electroencephalography (EEG) machines. To accomplish this, EEG datasets from patients with and without stroke were collected, standardized, and preprocessed using different methods. The different variations of this data was then used to train machine learning models, and the ability of each trained model to identify EEG recordings of stroke patients was tested. It was found that the method of preprocessing of the data was critical for the model to achieve good results. Using certain preprocessing methods, and with a sufficient amount of data, the model was able to correctly differentiate stroke and non-stroke EEG recordings in the dataset with 100% accuracy. However, a number of issues which may have had an effect on the result were identified, including the lack of relevant publicly available datasets, the small size of the used datasets, and potential issues in the standardization process., Stroke är ett förödande medicinskt tillstånd, som en av fyra genomgår någon gång under sin livstid. Stroke-attacker inträffar ofta plötsligt och utan några varningstecken, och är svåra att upptäcka på grund av att de inträffar osynligt inuti hjärnan; det är enbart när yttre symptom framträder som det är möjligt att detektera att en person har stroke. Detta, i kombination med att skadan förvärras desto längre attacken går obehandlad, gör mer prisvärda och pålitliga metoder för strokedetektion mycket eftertraktade. Målet med projektet var att undersöka möjligheten att detektera stroke i patienter med enbart hjärnaktivitetsdata insamlad med hjälp av lättanvända och relativt billiga Elektroencefalografimaskiner (EEG). För att åstadkomma detta insamlades EEG-dataset från patienter med och utan stroke, som standardiserades och förbehandlades med olika metoder. De olika variationerna av denna data användes sedan för att träna maskininlärningsmodeller, och dessas förmåga att identifiera EEG-inspelningar från strokepatienter testades. Det kunde konstateras att valet av förbehandlingsmetod var kritiskt för att modellen skulle uppnå goda resultat. Med hjälp av rätt förbehandlingsmetoder, och med ett tillräckligt stort antal datapunkter, kunde modellen särskilja stroke och icke-stroke EEG-inspelningar i vårt dataset med 100% noggrannhet. Emellertid identifierades ett antal eventuella problem som kan ha påverkat resultatet, såsom bristen på offentligt tillgängliga relevanta dataset, det lilla antalet patienter i de använda dataseten, och potentiella problem i standardiseringsprocessen.

In this thesis, a sophisticated EEG-based framework for real-time robotic control is proposed. The EEG signals are captured through the brain mechanism of inner speech, or ”the voice inside one’s head,” which serves as an experimental paradigm for EEG signal acquisition. A comprehensive brain-computer interface (BCI) is developed, consisting of different stages. These stages include the acquisition of EEG signals, preprocessing techniques to filter and enhance the signals, and robust classification algorithms to interpret the signals into corresponding commands. The final stage involves the integration of these commands into the robot’s control system, enabling movement control. This framework aims to demonstrate the feasibility and efficiency of using inner speech based EEG signals for intuitive and effective robotic manipulation. The four different commands for the robot to move are left, right, forward and backward. The architecture is tested both in simulation and real measurement environment. The empirical results provided a feasible proof of concept, demonstrating that the robot can be controlled via EEG signals, with a bias towards the third classification category. This category corresponds to the forward motion, achieving an accuracy rate of approximately 40%.

The female reproductive years are characterized by fluctuations in ovarian hormones across the menstrual cycle, which have the potential to modulate neurophysiological and behavioral dynamics. Menstrually-related mood disorders (MRMDs) comprise cognitive-affective or somatic symptoms that are thought to be triggered by the rapid fluctuations in ovarian hormones in the luteal phase of the menstrual cycle. MRMDs include premenstrual syndrome (PMS), premenstrual dysphoric disorder (PMDD), and premenstrual exacerbation (PME) of other psychiatric disorders. Electroencephalography (EEG) non-invasively records in vivo synchronous activity from populations of neurons with high temporal resolution. The present overview sought to systematically review the current state of task-related and resting-state EEG investigations on MRMDs. Preliminary evidence indicates lower alpha asymmetry at rest being associated with MRMDs, while one study points to the effect being lutealphase specific. Moreover, higher luteal spontaneous frontal brain activity (slow/fast wave ratio as measured by the delta/beta power ratio) has been observed in persons with MRMDs, while sleep architecture results point to potential circadian rhythm disturbances. In this review, we discuss the quality of study designs as well as future perspectives and challenges of supplementing the diagnostic and scientific toolbox for MRMDs with EEG.

Stroke is a devastating and common medical condition, which one in four people will experience in their lifetime. Strokes often occur suddenly and without any prior signs, and are hard to notice as they occur invisibly within the brain; only the appearance of outward symptoms give away the ongoing attack. This, combined with the fact that the damage done by the stroke worsens the longer it goes untreated, makes more affordable and reliable stroke detection methods highly desirable. The goal of our project was to examine the feasibility of detecting stroke in patients using only brain activity data collected with easily used and relatively low cost electroencephalography (EEG) machines. To accomplish this, EEG datasets from patients with and without stroke were collected, standardized, and preprocessed using different methods. The different variations of this data was then used to train machine learning models, and the ability of each trained model to identify EEG recordings of stroke patients was tested. It was found that the method of preprocessing of the data was critical for the model to achieve good results. Using certain preprocessing methods, and with a sufficient amount of data, the model was able to correctly differentiate stroke and non-stroke EEG recordings in the dataset with 100% accuracy. However, a number of issues which may have had an effect on the result were identified, including the lack of relevant publicly available datasets, the small size of the used datasets, and potential issues in the standardization process., Stroke är ett förödande medicinskt tillstånd, som en av fyra genomgår någon gång under sin livstid. Stroke-attacker inträffar ofta plötsligt och utan några varningstecken, och är svåra att upptäcka på grund av att de inträffar osynligt inuti hjärnan; det är enbart när yttre symptom framträder som det är möjligt att detektera att en person har stroke. Detta, i kombination med att skadan förvärras desto längre attacken går obehandlad, gör mer prisvärda och pålitliga metoder för strokedetektion mycket eftertraktade. Målet med projektet var att undersöka möjligheten att detektera stroke i patienter med enbart hjärnaktivitetsdata insamlad med hjälp av lättanvända och relativt billiga Elektroencefalografimaskiner (EEG). För att åstadkomma detta insamlades EEG-dataset från patienter med och utan stroke, som standardiserades och förbehandlades med olika metoder. De olika variationerna av denna data användes sedan för att träna maskininlärningsmodeller, och dessas förmåga att identifiera EEG-inspelningar från strokepatienter testades. Det kunde konstateras att valet av förbehandlingsmetod var kritiskt för att modellen skulle uppnå goda resultat. Med hjälp av rätt förbehandlingsmetoder, och med ett tillräckligt stort antal datapunkter, kunde modellen särskilja stroke och icke-stroke EEG-inspelningar i vårt dataset med 100% noggrannhet. Emellertid identifierades ett antal eventuella problem som kan ha påverkat resultatet, såsom bristen på offentligt tillgängliga relevanta dataset, det lilla antalet patienter i de använda dataseten, och potentiella problem i standardiseringsprocessen.

Deep learning has the potential to be used to improve and streamline EEG analysis. At the present, classifiers and supervised learning dominate the field. Supervised learning depends on target labels which most often are created by human experts manually classifying data. A problem with supervised learning is intra- and interrater agreement which in some instances are far from perfect. This can affect the training and make evaluation more difficult. This thesis includes three papers where self-supervised deep neural networks were developed. In self-supervised learning, the input data to the networks themselves contain structures that are used as targets for the training and no labeling is necessary. In paper I, deep neural networks were trained to increase the number of-, or to recreate missing EEG-channels. The performance was at least on the same level as that of spherical interpolation, but unlike in the case of interpolation, missing data does not have to be identified manually first. Papers II and III involved developing deep neural networks for clustering analysis. The networks produced two-dimensional representations of EEG data and the training strategy was based on the principle of t-distributed stochastic neighbor embedding (t-SNE). In paper II, comparisons were made to parametric t-SNE and EEG-features obtained from time-frequency methods. The deep neural networks produced more distinct clustering when tested on data annotated for epileptiform discharges, seizure activity, or sleep-wakefulness. In paper III, the newly developed method was used to compare annotations of epileptiform discharges. Two experts performed independent annotations and classifiers were trained on these, using supervised learning, which in turn produced new annotations. The agreement when comparing two sets of annotations was not larger between the two experts than between an expert and a classifier. The analysis showed that differences in the annotations by the experts infl, Funding: The work was funded by Saad Nagi (grants RÖ-974228, RÖ-962769, and RÖ-941377), Magnus Thordstein (grant RÖ-986017), and Håkan Olaus-son (grants LIO-936017 and RÖ-941359).

2023 Medical Technologies Congress, TIPTEKNO 2023 -- 10 November 2023 through 12 November 2023 -- 195703, This study proposes a highly accurate and fast algorithm for the diagnosis of attention deficit hyperactivity disorder (ADHD), which will reduce reliance on time-consuming subjective assessments, the findings of which are likely to be mistaken with other neurodevelopmental diseases. Time, frequency and nonlinear features were extracted from electroencephalographic (EEG) signals which recording based on visual attention task obtained from 61 ADHD and 60 healthy participants. In this study, Least Absolute Shrinkage and Selection Operator (LASSO) was used to find reliable features; and four machine learning classifiers such as support vector machine (SVM), k-nearest neighbors (KNN), decision tree and ensemble learning were evaluated for classifying ADHD and healthy children. The results were indicated that using LASSO with SVM can be useful for classifying ADHD and the highest average accuracy was reached in this study was 96.3%. In addition, the features selected with LASSO had shown that signals from the temporal, parietal, and occipital lobes might have the possible biomarkers for ADHD, at least in tasks that require visual attention. © 2023 IEEE.

Objective. A key challenge of virtual reality (VR) applications is to maintain a reliable human-avatar mapping. Users may lose the sense of controlling (sense of agency), owning (sense of body ownership), or being located (sense of self-location) inside the virtual body when they perceive erroneous interaction, i.e. Break-in-embodiment (BiE). However, the way to detect such an inadequate event is currently limited to questionnaires or spontaneous reports from users. The ability to implicitly detect BiE in real-time enables us to adjust human-avatar mapping without interruption. Approach. We propose and empirically demonstrate a novel Brain Computer Interface (BCI) approach that monitors the occurrence of BiE based on the users' brain oscillatory activity in real-time to adjust the human-avatar mapping in VR. We collected EEG data of 37 participants while they performed reaching movements with their avatar with different magnitude of distortion. Main results. Our BCI approach seamlessly predicts occurrence of BiE in varying magnitude of erroneous interaction. The mapping has been customized by BCI-reinforcement learning (RL) closed-loop system to prevent BiE from occurring. Furthermore, a non-personalized BCI decoder generalizes to new users, enabling "Plug-and-Play" ErrP-based non-invasive BCI. The proposed VR system allows customization of human-avatar mapping without personalized BCI decoders or spontaneous reports. Significance. We anticipate that our newly developed VR-BCI can be useful to maintain an engaging avatar-based interaction and a compelling immersive experience while detecting when users notice a problem and seamlessly correcting it.

2023 Medical Technologies Congress, TIPTEKNO 2023 -- 10 November 2023 through 12 November 2023 -- 195703, This study proposes a highly accurate and fast algorithm for the diagnosis of attention deficit hyperactivity disorder (ADHD), which will reduce reliance on time-consuming subjective assessments, the findings of which are likely to be mistaken with other neurodevelopmental diseases. Time, frequency and nonlinear features were extracted from electroencephalographic (EEG) signals which recording based on visual attention task obtained from 61 ADHD and 60 healthy participants. In this study, Least Absolute Shrinkage and Selection Operator (LASSO) was used to find reliable features; and four machine learning classifiers such as support vector machine (SVM), k-nearest neighbors (KNN), decision tree and ensemble learning were evaluated for classifying ADHD and healthy children. The results were indicated that using LASSO with SVM can be useful for classifying ADHD and the highest average accuracy was reached in this study was 96.3%. In addition, the features selected with LASSO had shown that signals from the temporal, parietal, and occipital lobes might have the possible biomarkers for ADHD, at least in tasks that require visual attention. © 2023 IEEE.

Aquesta tesi realitza una recerca exhaustiva sobre els paràmetres de densitat espectral (DSA) en dispositius de monitoratge de la profunditat anestèsica (DoA), posant l’accent en la seva aplicació en el dispositiu de monitoratge CONOX® (Quantium Medical S.L.), considerant les seves pròpies particularitats. L’objectiu és desenvolupar una solució personalitzada considerant les inter-variabilitats entre pacients, per a finalment proporcionar una eina útil per a l’avaluació de la pràctica anestèsica i la pèrdua de consciència (LOC) en un entorn quirúrgic. La metodologia seguida inclou una anàlisi de les profunditats de byte (1B, 2B i 3B) i diversos valors alfa en el promitjat exponencial, per a determinar el millor ajust per a les dades de l’EEG i els interessos de l’empresa, que va donar com a resultat la selecció d’1 byte de profunditat i un promitjat amb una alfa de 0.2. A més, es proposen i analitzen 11 equacions per a millorar el reescalat de l’EEG sense processar, proporcionant una solució depenent de l’edat per a quatre rangs establerts entre 16 i 93 anys, la qual cosa facilita una visualització òptima de l’espectrograma, seleccionant tres equacions per a cada rang d’edat; una equació per defecte per a la majoria dels pacients, una equació de saturació vermella per a pacients amb baixa energia d’EEG, i una equació de saturació blava per a pacients amb alta energia d’EEG, permetent pivotar entre equacions en temps real quan sigui necessari., Esta tesis realiza una investigación exhaustiva sobre los parámetros del arreglo de densidad espectral (DSA) en dispositivos de monitorización de la profundidad anestésica (DoA), haciendo un especial énfasis en su aplicación en el dispositivo de monitorización CONOX® (Quantium Medical, S.L.), considerando sus propias particularidades. El objetivo es desarrollar una solución personalizada para la optimización de la visualización del espectrograma de la señal del electroencefalograma (EEG), considerando las inter-variabilidades del paciente, para finalmente proporcionar una herramienta útil para la evaluación de la práctica anestésica y la pérdida de consciencia (LOC) en un entorno quirúrgico. La metodología seguida incluye un análisis de las profundidades de bytes (1B, 2B, 2B) y varios valores de alfa en el promediado exponencial, para determinar el mejor ajuste para los datos del EEG y la empresa, que dio como resultado la selección de 1 profundidad de byte y un alfa de promediado de 0.2. Además, se postulan y analizan 11 ecuaciones para mejorar el re-escalado del EEG sin procesar, proporcionando una solución dependiente de la edad para cuatro rangos que abarcan desde los 16 años hasta los 93, lo que facilitaría una visualización óptima del espectrograma, seleccionando tres de las ecuaciones propuestas para cada rango de edad; una ecuación por defecto para la mayoría de casos, una ecuación de saturación roja para pacientes con baja energía de EEG, y una ecuación de saturación azul para pacientes con alta energía de EEG, permitiendo pivotar entre ecuaciones durante el tiempo real cuando sea necesario., This thesis undertakes a thorough investigation on the Density Spectral Array (DSA) parameters in Depth of Anesthesia (DoA) monitoring devices, emphasizing its application on the CONOX® (Quantium Medical S.L) monitoring device, by considering its own particularities. The aim is to develop a personalized solution for the optimization of the spectrogram visualization of the electroencephalogram (EEG) signal, considering patient inter-variabilities, to finally provide a useful tool for the assessment of the anesthesia practice and Loss of Consciousness (LOC) on a surgical environment. The followed methodology includes an analysis of 1B, 2B and 3B byte depths and several alpha values in exponential averaging, to determine the best fit for the EEG data and the company’s interests, which resulted in the selection of 1 byte depth and an averaging alpha of 0.2. Furthermore, 11 equations are postulated and analyzed to improve the rescaling of the raw EEG, providing an age-dependent solution for four age ranges covering from 16 years old to 93, which facilitates an optimal visualization of the spectrogram, by selecting three equations for each age range; a default equation for majority of cases, a red saturating equation for low EEG energy patients, and a blue saturating equation for high EEG energy patient, allowing to pivot between equations during real time when required.

Available online 28 April 2023, The present study examined the effect of both morphological complexity and semantic transparency in Chinese compound word recognition. Using a visual lexical decision task, our electrophysiological results showed that transparent and opaque compounds induced stronger Left Anterior Negativity (LAN) than monomorphemic words. This result suggests that Chinese compounds might be decomposed into their constituent morphemes at the lemma level, whereas monomorphemic words are accessed as a whole-word lemma directly from the form level. In addition, transparent and opaque compounds produced a similar N400 as each other, suggesting that transparency did not show an effect on the involvement of constituent morphemes during access to the whole- word lemma. Two behavioral experiments additionally showed similar patterns to the EEG results. These find- ings support morphological decomposition for compounds at the lemma level as proposed by the full-parsing model, and no evidence is found to support the role of transparency during Chinese compound word recognition.

Introduction: Migraine is associated with enhanced visual sensitivity during and outside attacks. Processing of visual information is a highly non-linear process involving complex interactions across (sub)cortical networks. In this exploratory study, we combined electroencephalography with bi-sinusoidal light stimulation to assess non-linear features of visual processing in participants with migraine. Methods: Twenty participants with migraine (10 with aura, 10 without aura) and ten non-headache controls were measured (outside attacks). Participants received bi-sinusoidal 13 + 23 Hz red light visual stimulation. Electroencephalography spectral power and multi-spectral phase coherence were compared between groups at the driving stimulation frequencies together with multiples and combinations of these frequencies (harmonic and intermodulation frequencies) caused by non-linearities. Results: Only at the driving frequency of 13 Hz higher spectral power was found in migraine with aura participants compared with those with migraine without aura and controls. Differences in phase coherence were present for 2nd, 4th, and 5th-order non-linearities in those with migraine (migraine with and without aura) compared with controls. Bi-sinusoidal light stimulation revealed evident non-linearities in the brain’s electroencephalography response up to the 5th order with reduced phase coherence for higher order interactions in interictal participants with migraine. Discussion: Insight into interictal non-linear visual processing may help understand brain dynamics underlying migraine attack susceptibility. Future research is needed to determine the clinical value of the results., Biomechatronics & Human-Machine Control

Photosensitivity is a neurological disorder in which a person’s brain produces epileptic discharges, known as Photoparoxysmal Responses (PPRs), when it receives certain visual stimuli. The current standardized diagnosis process used in hospitals consists of submitting the subject to the Intermittent Photic Stimulation process and attempting to trigger these phenomena. The brain activity is measured by an Electroencephalogram (EEG), and the clinical specialists manually look for the PPRs that were provoked during the session. Due to the nature of this disorder, long EEG recordings may contain very few PPR segments, meaning that a highly imbalanced dataset is available. To tackle this problem, this research focused on applying Data Augmentation (DA) to create synthetic PPR segments from the real ones, improving the balance of the dataset and, thus, the global performance of the Machine Learning techniques applied for automatic PPR detection. K-Nearest Neighbors and a One-Hidden-Dense-Layer Neural Network were employed to evaluate the performance of this DA stage. The results showed that DA is able to improve the models, making them more robust and more able to generalize. A comparison with the results obtained from a previous experiment also showed a performance improvement of around 20% for the Accuracy and Specificity measurements without Sensitivity suffering any losses. This project is currently being carried out with subjects at Burgos University Hospital, Spain., This research was funded by the Spanish Ministry of Economics and Industry, Grant PID2020-112726RB-I00, by the Spanish Research Agency (AEI, Spain) under Grant agreement RED2018-102312-T (IA-Biomed), and by the Ministry of Science and Innovation under CERVERA Excellence Network project CER-20211003 (IBERUS) and Missions Science and Innovation project MIG-20211008 (INMERBOT), as well as by Principado de Asturias, Grant SV-PA-21-AYUD/2021/50994.

Clinical and electroencephalogram (EEG) features in frontal lobe epilepsy (FLE) vary considerably among patients, making the diagnosis a challenge. The objective of this study was to describe interictal and ictal EEG activity, identifying variables that could help to differentiate and diagnose frontal lobe epilepsy cases. A prospective cross-sectional study from patients with frontal interictal epileptiform discharges (IED) referred to the Vall d’Hebron University Hospital (Barcelona, Spain) after a clinical event compatible with epileptic seizures was designed. The interictal and ictal activity were analyzed to provide a detailed EEG description of the cases, using different statistical analyses. The morphological seizure pattern at the ictal onset remained globally unchanged over time in seizures arising from the frontal lobe for each patient. Isolated sharp waves were the most frequent waveforms in the expression of IED. Frontal lobe seizures are frequently short and sometimes appear grouped in clusters within the same recording. Often the ictal expression of the electrical activity in frontal lobe seizure is subtle and challenging to interpret. A description of the main findings is summarized to identify seizures arising from the frontal lobe and avoid false negatives findings in EEG interpretations.

Deep learning has the potential to be used to improve and streamline EEG analysis. At the present, classifiers and supervised learning dominate the field. Supervised learning depends on target labels which most often are created by human experts manually classifying data. A problem with supervised learning is intra- and interrater agreement which in some instances are far from perfect. This can affect the training and make evaluation more difficult. This thesis includes three papers where self-supervised deep neural networks were developed. In self-supervised learning, the input data to the networks themselves contain structures that are used as targets for the training and no labeling is necessary. In paper I, deep neural networks were trained to increase the number of-, or to recreate missing EEG-channels. The performance was at least on the same level as that of spherical interpolation, but unlike in the case of interpolation, missing data does not have to be identified manually first. Papers II and III involved developing deep neural networks for clustering analysis. The networks produced two-dimensional representations of EEG data and the training strategy was based on the principle of t-distributed stochastic neighbor embedding (t-SNE). In paper II, comparisons were made to parametric t-SNE and EEG-features obtained from time-frequency methods. The deep neural networks produced more distinct clustering when tested on data annotated for epileptiform discharges, seizure activity, or sleep-wakefulness. In paper III, the newly developed method was used to compare annotations of epileptiform discharges. Two experts performed independent annotations and classifiers were trained on these, using supervised learning, which in turn produced new annotations. The agreement when comparing two sets of annotations was not larger between the two experts than between an expert and a classifier. The analysis showed that differences in the annotations by the experts infl, Funding: The work was funded by Saad Nagi (grants RÖ-974228, RÖ-962769, and RÖ-941377), Magnus Thordstein (grant RÖ-986017), and Håkan Olaus-son (grants LIO-936017 and RÖ-941359).

Deep learning has the potential to be used to improve and streamline EEG analysis. At the present, classifiers and supervised learning dominate the field. Supervised learning depends on target labels which most often are created by human experts manually classifying data. A problem with supervised learning is intra- and interrater agreement which in some instances are far from perfect. This can affect the training and make evaluation more difficult. This thesis includes three papers where self-supervised deep neural networks were developed. In self-supervised learning, the input data to the networks themselves contain structures that are used as targets for the training and no labeling is necessary. In paper I, deep neural networks were trained to increase the number of-, or to recreate missing EEG-channels. The performance was at least on the same level as that of spherical interpolation, but unlike in the case of interpolation, missing data does not have to be identified manually first. Papers II and III involved developing deep neural networks for clustering analysis. The networks produced two-dimensional representations of EEG data and the training strategy was based on the principle of t-distributed stochastic neighbor embedding (t-SNE). In paper II, comparisons were made to parametric t-SNE and EEG-features obtained from time-frequency methods. The deep neural networks produced more distinct clustering when tested on data annotated for epileptiform discharges, seizure activity, or sleep-wakefulness. In paper III, the newly developed method was used to compare annotations of epileptiform discharges. Two experts performed independent annotations and classifiers were trained on these, using supervised learning, which in turn produced new annotations. The agreement when comparing two sets of annotations was not larger between the two experts than between an expert and a classifier. The analysis showed that differences in the annotations by the experts infl, Funding: The work was funded by Saad Nagi (grants RÖ-974228, RÖ-962769, and RÖ-941377), Magnus Thordstein (grant RÖ-986017), and Håkan Olaus-son (grants LIO-936017 and RÖ-941359).

The female reproductive years are characterized by fluctuations in ovarian hormones across the menstrual cycle, which have the potential to modulate neurophysiological and behavioral dynamics. Menstrually-related mood disorders (MRMDs) comprise cognitive-affective or somatic symptoms that are thought to be triggered by the rapid fluctuations in ovarian hormones in the luteal phase of the menstrual cycle. MRMDs include premenstrual syndrome (PMS), premenstrual dysphoric disorder (PMDD), and premenstrual exacerbation (PME) of other psychiatric disorders. Electroencephalography (EEG) non-invasively records in vivo synchronous activity from populations of neurons with high temporal resolution. The present overview sought to systematically review the current state of task-related and resting-state EEG investigations on MRMDs. Preliminary evidence indicates lower alpha asymmetry at rest being associated with MRMDs, while one study points to the effect being lutealphase specific. Moreover, higher luteal spontaneous frontal brain activity (slow/fast wave ratio as measured by the delta/beta power ratio) has been observed in persons with MRMDs, while sleep architecture results point to potential circadian rhythm disturbances. In this review, we discuss the quality of study designs as well as future perspectives and challenges of supplementing the diagnostic and scientific toolbox for MRMDs with EEG.