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Robotic systems, such as Lokomat® have shown promising results in people with severe motor impairments, who suffered a stroke or other neurological damage. Robotic devices have also been used by people with more challenging damages, such as Spinal Cord Injury (SCI), using feedback strategies that provide information about the brain activity in real-time. This study proposes a novel Motor Imagery (MI)-based Electroencephalogram (EEG) Visual Neurofeedback (VNFB) system for Lokomat® to teach individuals how to modulate their own $\mu $ (8-12 Hz) and $\beta $ (15-20 Hz) rhythms during passive walking. Two individuals with complete SCI tested our VNFB system completing a total of 12 sessions, each on different days. For evaluation, clinical outcomes before and after the intervention and brain connectivity were analyzed. As findings, the sensitivity related to light touch and painful discrimination increased for both individuals. Furthermore, an improvement in neurogenic bladder and bowel functions was observed according to the American Spinal Injury Association Impairment Scale, Neurogenic Bladder Symptom Score, and Gastrointestinal Symptom Rating Scale. Moreover, brain connectivity between different EEG locations significantly ( ${p}\lt 0.05$ ) increased, mainly in the motor cortex. As other highlight, both SCI individuals enhanced their $\mu $ rhythm, suggesting motor learning. These results indicate that our gait training approach may have substantial clinical benefits in complete SCI individuals.

Neural responses to sensory inputs can scale with the likelihood of encountering the input. This is consistent with the predictive coding framework, in that the human brain is expected to be less responsive to predicted inputs. Typically, however, prediction is not explicitly measured. It is inferred from the probability of encountering an event. When an input is explicitly predicted, responses to predicted inputs can be enhanced. Here, we ask if this effect can be ascribed to a generic priming effect, from pre-cogitating about one of two possible inputs. Consistent with this, we find that P300s (a relatively late event-related potential measured with electroencephalography) are greater for explicitly predicted audio and visual inputs, and that this effect cannot be distinguished from a priming effect from pre-imagining audio or visual presentations. Evidence indicates that participants engaged in pre-imagining presentations, as we were able to decode online what type of presentation (audio or visual) they were imagining with a high success rate (approx. 73%), and we encouraged compliance with neuro-feedback regarding this success rate. Our data confirm that human cortex can be more responsive to inputs that have been subject to pre-cogitation—including explicit predictions. This highlights that while anticipatory processes can reduce responding to likely inputs, they can also enhance responding to explicitly predicted inputs. [ABSTRACT FROM AUTHOR]

Robotic systems, such as Lokomat® have shown promising results in people with severe motor impairments, who suffered a stroke or other neurological damage. Robotic devices have also been used by people with more challenging damages, such as Spinal Cord Injury (SCI), using feedback strategies that provide information about the brain activity in real-time. This study proposes a novel Motor Imagery (MI)-based Electroencephalogram (EEG) Visual Neurofeedback (VNFB) system for Lokomat® to teach individuals how to modulate their own $\mu $ (8-12 Hz) and $\beta $ (15-20 Hz) rhythms during passive walking. Two individuals with complete SCI tested our VNFB system completing a total of 12 sessions, each on different days. For evaluation, clinical outcomes before and after the intervention and brain connectivity were analyzed. As findings, the sensitivity related to light touch and painful discrimination increased for both individuals. Furthermore, an improvement in neurogenic bladder and bowel functions was observed according to the American Spinal Injury Association Impairment Scale, Neurogenic Bladder Symptom Score, and Gastrointestinal Symptom Rating Scale. Moreover, brain connectivity between different EEG locations significantly (${p}\lt 0.05$) increased, mainly in the motor cortex. As other highlight, both SCI individuals enhanced their $\mu $ rhythm, suggesting motor learning. These results indicate that our gait training approach may have substantial clinical benefits in complete SCI individuals. [ABSTRACT FROM AUTHOR]

Background: Recently, numerous studies on non-invasive neuro-feedback training for improving cognitive function have been explored to identify its feasibility. However, little is known about the clinical efficacy of functional near-infrared spectroscopy-based neuro-feedback (fNIRS-based NF) training in individuals with or without cognitive impairment. Method: Studies on fNIRS-based NF training were searched through Embase, Medline, PubMed, Web of Science, PsychINFO, and Google scholar and then four studies were finally selected. The overall cognitive domains, working memory, and executive function were separately pooled to investigate fNIRSbased NF training's effect size. Results: The overall effect on cognitive outcomes across four studies was large (Hedges'g = 0.682, confidence interval (CI) = 0.079-1.285) without publication bias. Moderate to large effects were found for working memory (Hedges'g = 1.143) and executive function (Hedges'g = 0.406) without publication bias. fNIRS-based NF training was beneficial in improving working memory and executive function. Conclusion: Therefore, this finding shed new light on fNIRS-based NF training as a promising treatment for improving cognitive function. [ABSTRACT FROM AUTHOR]

During the last decades, neurofeedback training for emotional self-regulation has received significant attention from scientific and clinical communities. Most studies have investigated emotions using functional magnetic resonance imaging (fMRI), including the real-time application in neurofeedback training. However, the electroencephalogram (EEG) is a more suitable tool for therapeutic application. Our study aims at establishing a method to classify discrete complex emotions (e.g., tenderness and anguish) elicited through a near-immersive scenario that can be later used for EEG-neurofeedback. EEG-based affective computing studies have mainly focused on emotion classification based on dimensions, commonly using passive elicitation through single-modality stimuli. Here, we integrated both passive and active elicitation methods. We recorded electrophysiological data during emotion-evoking trials, combining emotional self-induction with a multimodal virtual environment. We extracted correlational and time-frequency features, including frontal-alpha asymmetry (FAA), using Complex Morlet Wavelet convolution. Thinking about future real-time applications, we performed within-subject classification using 1-s windows as samples and we applied trial-specific cross-validation. We opted for a traditional machine-learning classifier with low computational complexity and sufficient validation in online settings, the Support Vector Machine. Results of individual-based cross-validation using the whole feature sets showed considerable between-subject variability. The individual accuracies ranged from 59.2 to 92.9% using time-frequency/FAA and 62.4 to 92.4% using correlational features. We found that features of the temporal, occipital, and left-frontal channels were the most discriminative between the two emotions. Our results show that the suggested pipeline is suitable for individual-based classification of discrete emotions, paving the way for future personalized EEG-neurofeedback training.

During the last decades, neurofeedback training for emotional self-regulation has received significant attention from scientific and clinical communities. Most studies have investigated emotions using functional magnetic resonance imaging (fMRI), including the real-time application in neurofeedback training. However, the electroencephalogram (EEG) is a more suitable tool for therapeutic application. Our study aims at establishing a method to classify discrete complex emotions (e.g., tenderness and anguish) elicited through a near-immersive scenario that can be later used for EEG-neurofeedback. EEG-based affective computing studies have mainly focused on emotion classification based on dimensions, commonly using passive elicitation through single-modality stimuli. Here, we integrated both passive and active elicitation methods. We recorded electrophysiological data during emotion-evoking trials, combining emotional self-induction with a multimodal virtual environment. We extracted correlational and time-frequency features, including frontal-alpha asymmetry (FAA), using Complex Morlet Wavelet convolution. Thinking about future real-time applications, we performed within-subject classification using 1-s windows as samples and we applied trial-specific cross-validation. We opted for a traditional machine-learning classifier with low computational complexity and sufficient validation in online settings, the Support Vector Machine. Results of individual-based cross-validation using the whole feature sets showed considerable between-subject variability. The individual accuracies ranged from 59.2 to 92.9% using time-frequency/FAA and 62.4 to 92.4% using correlational features. We found that features of the temporal, occipital, and left-frontal channels were the most discriminative between the two emotions. Our results show that the suggested pipeline is suitable for individual-based classification of discrete emotions, paving the way for future personalized EEG-neurofeedback training. [ABSTRACT FROM AUTHOR]

Neuronal excitation enables identifying the features of an electroencephalogram (EEG) signal for motor imagery detection. We propose a novel feature extraction algorithm supported by short-term cepstrum-based inverse filtration of neuronal excitation of the active synapse. The maximum power of the estimated neuronal excitation is subjected to a two-class Bayesian probabilistic classifier. The feature extraction algorithm with the Bayesian probabilistic classifier significantly improves the brain–computer interface performance compared with that of other conventional methods of EEG signal processing such as wavelet with a Bayesian classifier, autocorrelation and CSP filter with a naïve Bayes classifier over the BCI competition II and IV datasets. Consequently, this neuronal excitation feature allows the authors to develop a motor imagery neuro-feedback system; the performance of which achieves 87.2% average classification accuracy, which is 14% greater than that of the wavelet-based algorithm and 6.2% greater than that of the TRSP-based algorithm, with 53 ms of processing time allotted for each instruction in a real-time experiment. However, brain signal variation across different subjects and sessions significantly impairs decision accuracy. Our neuronal excitation base feature extraction algorithm minimizes these variations in classification accuracy. [ABSTRACT FROM AUTHOR]

A high-resolution neurostimulator is the essential component of many bidirectional neural interfaces. In practice, the effective resolution of fully integrated neurostimulator designs is often hindered by the transistor mismatch, especially in submicrometer CMOS processes. In this article, we present a new circuit technique called redundant crossfire (RXF) to address this challenge. It is derived from our redundant sensing (RS) framework, which aims at engineering information redundancy into the system architecture to enhance its effective resolution. RXF involves combining (i.e., crossfiring) the outputs of two or more current drivers to form a redundant structure that, when properly configured, can produce accurate current pulses with an effective super-resolution beyond the limitation commonly permitted by the physical constraints. Unlike any previous works, the proposed technique achieves high-accuracy by directly exploiting the random transistor mismatch with an excessively large mismatch ratio of 10%–20%. The effectiveness of RXF is verified through both Monte Carlo simulations and measurement results of a fully integrated neurostimulator chip. Equipped with a 5-bit current digital-to-analog converter (IDAC) and two 4-bit current multipliers, the stimulator achieves an effective resolution of 9.75 bits in a 1.1-mA full range. An application of the fabricated chip is to deliver neuro-feedback to a human amputee through peripheral nerves where the amplitude of stimulation pulses is accurately controlled to encode the tactile response’s intensity. [ABSTRACT FROM AUTHOR]

Attention Deficit Disorder with or without Hyperactivity (ADHD is a neurodevelopmental disorder which affects the day-to-day functioning of children and adults with this condition. Pharmacological treatment can reduce the symptoms associated with ADHD, but it has some limitations. The objective of this symposium is to determine the effects of non-pharmacological approaches on ADHD symptoms. Results indicate that the following intervention are promising approaches: cognitive behavioral therapy (CBT), mindfulness-based interventions (MBI), yoga, cognitive and metacognitive intervention, neurofeedback and parental training programs. Current research advocates multimodal approaches in conjunction with school or work accommodations integrating innovative technologies., (Copyright © 2023 L'Encéphale, Paris. All rights reserved.)

Introduction: Social anxiety is regarded as a debilitating disorder So that it can disturbs many aspects of human life. Objective: The objective of this study was to determine the Neuro-feedback and Mindfulness-Based Stress Reduction on Social Anxiety Disorder. Method: The research design was Qousi-experimental with pre-test, post-test with two groups. In this study, research population was all of students at Payam Noor University. The method of sampling was accessible. By means of sampling, 20 students were chosen as research sample and randomly divided into 10 groups of neuro-feedback and Mindfulness. The instrument of the study was Libowitz social phobia and MMPI inventory. The Statistical method was Co-variance analysis and dependent t-test. The data was analyzed by co-variance analysis and pair t-test. Results: The result shows that there is significant relationship (p≤0.05) between neuro-feedback and Mindfulness on social anxiety. Statistical analysis indicates that there is no significant relationship (p≥0.05) between neuro-feedback remedy and Mindfulness based stress reduction in reducing the anxiety symptoms. Conclusion: since the neuro-feedback can cause the anxiety reduction so neuro feed and Mindfulness-Based Stress Reduction can be used as modern methods in psychology that emphasis on biological basis for healing the Anxiety disorder.

ADHD is one of common neuropsychological disorders in childhood and continuing to adulthood and disturbs various dimensions of individual’s life such as personal affairs, social interactions, professional life, executive performance like active memory and concentration etc. There are numerous works on treatment of this type of disorder. Considering the lack of studies on neuro-feedback in adults, this research was conducted. For this purpose, this survey tries to determine effectiveness of neuro feedback in reduction of attention and concentration deficit symptoms in ADHD adults. This is experimental research pre-test and post-test as well as control group. Statistical population includes adults with ADHD referring to Atiye Clinic in Tehran. Two groups of experimental and control, each one composed of 8 people, were selected using purposive sampling. Research tools were Beck Stress Questionnaire, Beck Depression Questionnaire, Barkely Adults ADHD Questionnaire, IVA Test, and CNSVS test. The data were analyzed using SPSS software and Mann Whitney statistical test. Also, independent t-test, Wilcoxon, and Kruskal Wallis were applied for further results. Findings showed that neuro-feedback significantly reduced attention and concentration deficit symptoms in ADHD adult experimental group compared to control one. Therefore, neuro-feedback could be suggested for reduction of attention deficit symptoms and reduction of ADHD in adults.

Electroencephalogram (EEG) has widely been used to monitor subjects/patients’ mental states. Using the monitor results as feedback, neuro-feedback enables patients to learn to regulate their physiological and psychological states so that improvements in physical and psychological subjects/patients’ states could be achieved. By analyzing EEG components generated by motor imagery, a mind-controlled game based on motor imagery is developed, including the design of BCI and the design of the video game. In the game, neuro-feedback is realized to in a visual manner, through which the users could learn to improve attention span. Based on motor imagery, EEG signal is classified into two categories, the left and right hand motor imagery. The accuracy of classification is up to 70%. The bandpower analysis results show that users’ attention level improves during the experiment. In this neuro-feedback game system, EEG signal is not only used for monitoring but also used for game control. The game provides an attention state measurements for users. With the neuro-feedback in the BCI, the user and the game form a close loop interactively. The proposed BCI video game could not only be used for entertainment and relaxation purpose, but attention-span training purpose. [ABSTRACT FROM AUTHOR]

Background: Dementia is a disease that is constantly evolving in older people. Its diverse symptoms appear with varying degrees of severity affecting the daily life of those who suffer from it. The rate in which dementia progresses depends on different aspects of the treatment, chosen to try to control and slow down the development of the illness. Objective: The aim of this study is to assess the effectiveness of cognitive training through a Brain Computer Interface (BCI) and the NeuronUp platform in two age groups whose MMSE is between 18-23 MCI (mild dementia). Method: 32 subjects took part in the study. There were 22 subjects in Group 1 (61-69 years of age) and 10 subjects in Group 2 (70-81 years of age). The criterium for the selection of the groups was to identify the age range with greater improvements due to the training. In order to estimate neuropsychological performance, the subjects were evaluated with the Luria-DNA neuropsychological battery before and after training. This design enables us to evaluate five cognitive areas: visuospatial, spoken language, memory, intellectual processes and attention. Results: After training, Group 1 showed significant improvements in almost all the variables measured when compared with Group 2. This reveals a significant increase in cognitive ability, the degree of which depends on the age. Conclusion: People with mild dementia may delay cognitive impairment with a suitable cognitive training program. [ABSTRACT FROM AUTHOR]

Mind-wandering (MW), task-unrelated thought, has been examined by researchers in an increasing number of articles using models to predict whether subjects are in MW, using numerous physiological variables. However, these models are not applicable in general situations. Moreover, they output only binary classification. The current study suggests that the combination of electroencephalogram (EEG) variables and non-linear regression modeling can be a good indicator of MW intensity. We recorded EEGs of 50 subjects during the performance of a Sustained Attention to Response Task, including a thought sampling probe that inquired the focus of attention. We calculated the power and coherence value and prepared 35 patterns of variable combinations and applied Support Vector machine Regression (SVR) to them. Finally, we chose four SVR models: two of them non-linear models and the others linear models; two of the four models are composed of a limited number of electrodes to satisfy model usefulness. Examination using the held-out data indicated that all models had robust predictive precision and provided significantly better estimations than a linear regression model using single electrode EEG variables. Furthermore, in limited electrode condition, non-linear SVR model showed significantly better precision than linear SVR model. The method proposed in this study helps investigations into MW in various little-examined situations. Further, by measuring MW with a high temporal resolution EEG, unclear aspects of MW, such as time series variation, are expected to be revealed. Furthermore, our suggestion that a few electrodes can also predict MW contributes to the development of neuro-feedback studies.

Motor imagery involves cortical networks similar to those activated by real movements, but the extent to which the basal ganglia are recruited is not yet clear. Gamma and beta oscillations in the subthalamic nucleus (STN) vary with the effort of sustained muscle activity. We recorded local field potentials in Parkinson's disease patients and investigated if similar changes can be observed during imagined gripping at three different ‘forces’. We found that beta activity decreased significantly only for imagined grips at the two stronger force levels. Additionally, gamma power significantly scaled with increasing imagined force. Thus, in combination, these two spectral features can provide information about the intended force of an imaginary grip even in the absence of sensory feedback. Modulations in the two frequency bands during imaginary movement may explain the rehabilitating benefit of motor imagery to improve motor performance. The results also suggest that STN LFPs may provide useful information for brain-machine interfaces. [ABSTRACT FROM AUTHOR]

Analytical psychology shares with many other psychotherapies the important task of repairing the consequences of developmental trauma. The majority of analytic patients come from compromised early developmental backgrounds: they may have experienced neglect, abuse, or failures of empathic resonance from their carers. Functional brain imagery techniques including Quantitative Electroencephalogram (QEEG), and functional Magnetic Resonance Imagery (fMRI), allow us to track mental processes in ways beyond verbal reportage and introspection. This independent perspective is useful for developing new psychodynamic hypotheses, testing current ones, providing diagnostic markers, and monitoring treatment progress. Jung, with the Word Association Test, grasped these principles 100 years ago. Brain imaging techniques have contributed to powerful recent advances in our understanding of neurodevelopmental processes in the first three years of life. If adequate nurturance is compromised, a range of difficulties may emerge. This has important implications for how we understand and treat our psychotherapy clients. The paper provides an overview of functional brain imaging and advances in developmental neuropsychology, and looks at applications of some of these findings (including neurofeedback) in the Jungian psychotherapy domain. [ABSTRACT FROM AUTHOR]

The aim of this paper is to develop a preliminary neurofeedback system to improve the mood of the subjects using audio signals by enhancing their alpha brainwaves. Assessment of the effect of music on the human subjects is performed using three methods; subjective assessment of mood with the help of a questionnaire, the effect on brain by analysing EEG signals, and the effect on body by physiological assessment. In this study, two experiments have been designed. The first experiment was to determine the short-term effect of music on soothing human subjects, whereas the second experiment was to determine its long-term effect. Two types of music were used in the first experiment, the favourite music selected by the participants and a relaxing music with alpha wave binaural beats. The research findings showed that the relaxing music has a better soothing effect on the participants psychologically and physiologically. However, the one-way analysis of variance (ANOVA) results showed that the short-term soothing effect of both favourite music and relaxing music was not significant in changing the mean alpha absolute power and mean physiological measures (blood pressure and heart rate) at the significance level of 0.05. The second experiment was somewhat similar to an alpha neurofeedback training whereby the participants trained their brains to produce more alpha brainwaves by listening to the relaxing music with alpha wave binaural beats for a duration of 30 min daily. The results showed that the relaxing music has a long-term psychological and physiological effect on soothing the participants, as can be observed from the increase in alpha power and decrease in physiological measures after each session of training. The training was found to be effective in increasing the alpha power significantly [F(2,12) = 11.5458 and p = 0.0016], but no significant reduction in physiological measures was observed at the significance level of 0.05. [ABSTRACT FROM AUTHOR]

Introduction: Social anxiety is regarded as a debilitating disorder So that it can disturbs many aspects of human life. Objective: The objective of this study was to determine the Neuro-feedback and Mindfulness-Based Stress Reduction on Social Anxiety Disorder. Method: The research design was Qousi-experimental with pre-test, post-test with two groups. In this study, research population was all of students at Payam Noor University. The method of sampling was accessible. By means of sampling, 20 students were chosen as research sample and randomly divided into 10 groups of neuro-feedback and Mindfulness. The instrument of the study was Libowitz social phobia and MMPI inventory. The Statistical method was Co-variance analysis and dependent t-test. The data was analyzed by co-variance analysis and pair t-test. Results: The result shows that there is significant relationship (p≤0.05) between neurofeedback and Mindfulness on social anxiety. Statistical analysis indicates that there is no significant relationship (p≥0.05) between neuro-feedback remedy and Mindfulness based stress reduction in reducing the anxiety symptoms. Conclusion: since the neuro-feedback can cause the anxiety reduction so neuro feed and Mindfulness-Based Stress Reduction can be used as modern methods in psychology that emphasis on biological basis for healing the Anxiety disorder. [ABSTRACT FROM AUTHOR]

Functional Connectivity has demonstrated to be a key concept for unraveling how the brain balances functional segregation and integration properties while processing information. This work presents a set of open-source tools that significantly increase computational efficiency of some well-known connectivity indices and Graph-Theory measures. PLV, PLI, ImC, and wPLI as Phase Synchronization measures, Mutual Information as an information theory based measure, and Generalized Synchronization indices are computed much more efficiently than prior open-source available implementations. Furthermore, network theory related measures like Strength, Shortest Path Length, Clustering Coefficient, and Betweenness Centrality are also implemented showing computational times up to thousands of times faster than most well-known implementations. Altogether, this work significantly expands what can be computed in feasible times, even enabling whole-head real-time network analysis of brain function. [ABSTRACT FROM AUTHOR]

Goal: Motor imagery-related mu/beta rhythms, which can be voluntarily modulated by subjects, have been widely used in EEG-based brain computer interfaces (BCIs). Moreover, it has been suggested that motor imagery-specific EEG differences can be enhanced by feedback training. However, the differences observed in the EEGs of naive subjects are typically not sufficient to provide reliable EEG control and thus result in unintended feedback. Such feedback can frustrate subjects and impede training. In this study, a hybrid BCI paradigm combining motor imagery and steady-state visually evoked potentials (SSVEPs) has been proposed to provide effective continuous feedback for motor imagery training. Methods: During the initial training sessions, subjects must focus on flickering buttons to evoke SSVEPs as they perform motor imagery tasks. The output/feedback of the hybrid BCI is based on hybrid features consisting of motor imagery- and SSVEP-related brain signals. In this context, the SSVEP plays a more important role than motor imagery in generating feedback. As the training progresses, the subjects can gradually decrease their visual attention to the flickering buttons, provided that the feedback is still effective. In this case, the feedback is mainly based on motor imagery. Results: Our experimental results demonstrate that subjects generate distinguishable brain patterns of hand motor imagery after only five training sessions lasting approximately 1.5 h each. Conclusion: The proposed hybrid feedback paradigm can be used to enhance motor imagery training. Significance: This hybrid BCI system with feedback can effectively identify the intentions of the subjects. [ABSTRACT FROM PUBLISHER]

Background: There is growing evidence that cognitive training (CT) can improve the cognitive functioning of the elderly. CT may be influenced by cultural and linguistic factors, but research examining CT programs has mostly been conducted on Western populations. We have developed an innovative electroencephalography (EEG)-based brain-computer interface (BCI) CT program that has shown preliminary efficacy in improving cognition in 32 healthy English-speaking elderly adults in Singapore. In this second pilot trial, we examine the acceptability, safety, and preliminary efficacy of our BCI CT program in healthy Chinese-speaking Singaporean elderly. Methods: Thirty-nine elderly participants were randomized into intervention (n=21) and waitlist control (n=18) arms. Intervention consisted of 24 half-hour sessions with our BCI-based CT training system to be completed in 8 weeks; the control arm received the same intervention after an initial 8-week waiting period. At the end of the training, a usability and acceptability questionnaire was administered. Efficacy was measured using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), which was translated and culturally adapted for the Chinese-speaking local population. Users were asked about any adverse events experienced after each session as a safety measure. Results: The training was deemed easily usable and acceptable by senior users. The median difference in the change scores pre- and post-training of the modified RBANS total score was 8.0 (95% confidence interval [CI]: 0.0-16.0, P=0.042) higher in the intervention arm than waitlist control, while the mean difference was 9.0 (95% CI: 1.7-16.2, P=0.017). Ten (30.3%) participants reported a total of 16 adverse events - all of which were graded "mild" except for one graded "moderate". Conclusion: Our BCI training system shows potential in improving cognition in both English- and Chinese-speaking elderly, and deserves further evaluation in a Phase III trial. Overall, participants responded positively on the usability and acceptability questionnaire. [ABSTRACT FROM AUTHOR]

The investigation of the reproducibility in functional MRI (fMRI) is an important step in the quantification and analysis of paradigm-related brain activation. This article reports on reproducibility of cortical activation characterized by repeated fMRI runs (10 times) during the performance of a motor imagery and a passive auditory stimulation as a control task. Two parameters, the size of activation and BOLD signal contrast, were measured from regions-of-interest for 10 subjects across different threshold conditions. The variability of these parameters was normalized with respect to the mean obtained from 10 runs, and represented as the intrasession variability. It was found that the variability was significantly lower in the measurement of BOLD signal contrast as compared to the measurement of the size of activation. The variability of the activation volume measurement was greater in the motor imagery task than in the auditory tasks across all thresholds. This task-dependent difference was not apparent from the measurement of the BOLD signal contrast. The presence of threshold dependence in the variability measurement was also examined, but no such dependency was found. The results suggest that a measurement of BOLD signal itself is a more reliable indicator of paradigm-related brain activation during repeated fMRI scans. [ABSTRACT FROM AUTHOR]

Attention deficit hyperactivity disorder (ADHD) is a mental disorder most notable in children. The disease may affect the ability to focus and cause a physical and mental restlessness and risky behavior. Recommended treatment consists of stimulant administration and behavioral therapy. However, medicating children is problematic since there are indications that brain development is affected by ADHD medication agents. Therefore, behavioral therapy is the preferred approach in ADHD treatment for children. In order to monitor and optimize the success of such behavioral therapies, neuro-feedback methods can be used. The most notable technology used in such methods is Electroencephalography (EEG). In this article, an overview of the pathology of ADHD, EEG and its usage as a diagnostic and therapeutic tool in the context of ADHD is given. Based on that knowledge, novel EEG measurement modes, new development principles, and system on chip implementations are presented and discussed. [ABSTRACT FROM AUTHOR]

Introduction: Depression-stimulated aggression is a very common and problematic disorder that puts a huge burden on the families that have depressed patients. Therefore, the diagnosis and effective treatment of this disorder is very important. The brain wave of patients suffering from this disorder was compared before and after successful treatment and the differences were interpreted. Although brainwaves have been studied abundantly studied in the domain of depression, there was no study conducted to assess depression and aggression simultaneously. Methods: Twenty-three patients diagnosed with the standard of aggression and major depression, as determined by a psychiatrist, were successfully treated by medication (a clinical trial published in 2017). Brain recording of single-channel brainwaves was performed from the C-point before and after treatment, and frequency bands were measured by quantitative analysis of brain waves and compared before and after treatment. Results: Significant changes in brainwaves included the increase in Theta / Beta, SMR, Beta, Theta, Delta, and Wide Band after treatment. Other cases did not change significantly and were similar before and after the treatment. Conclusion: Based on the findings of the present study, brain wave pattern indicates significant changes which, overall, confirms the decrease in arousal and increase in attention and concentration after the treatment of depression-stimulated aggression. These changes, if approved in more extensive studies, can be helpful in designing a neuro-feedback treatment protocol. [ABSTRACT FROM AUTHOR]