Explore the vast range of titles available.

Depressive states in both healthy individuals and those with major depressive disorder exhibit differences primarily in symptom severity rather than symptom type, suggesting that there is a spectrum of depressive symptoms. The increasing prevalence of mild depression carries lifelong implications, emphasizing its clinical and social significance, which parallels that of moderate depression. Early intervention and psychotherapy have shown effective outcomes in subthreshold depression. Electroencephalography serves as a non-invasive, powerful tool in depression research, with many studies employing it to discover biomarkers and explore underlying mechanisms for the identification and diagnosis of depression. However, the efficacy of these biomarkers in distinguishing various depressive states in healthy individuals and in understanding the associated mechanisms remains uncertain. In our study, we examined the power spectrum density and the region-based phase-locking value in healthy individuals with various depressive states during their resting state. We found significant differences in neural activity, even among healthy individuals. Participants were categorized into high, middle, and low depressive state groups based on their response to a questionnaire, and eyes-open resting-state electroencephalography was conducted. We observed significant differences among the different depressive state groups in theta- and beta-band power, as well as correlations in the theta–beta ratio in the frontal lobe and phase-locking connections in the frontal, parietal, and temporal lobes. Standardized low-resolution electromagnetic tomography analysis for source localization comparing the differences in resting-state networks among the three depressive state groups showed significant differences in the frontal and temporal lobes. We anticipate that our study will contribute to the development of effective biomarkers for the early detection and prevention of depression.

Negative association was found between the frontal theta/beta ratio and mind wandering in participants with high schizotypal traits, while no such association was found in participants with low schizotypal traits. These findings provide insights into the neural mechanism of mind wandering in individuals with high schizotypal traits.

Persons with their eye closed and without any means of communication is said to be in a completely locked-in state (CLIS) while when they could still open their eyes actively or passively and have some means of communication are said to be in locked-in state (LIS). Two patients in CLIS without any means of communication, and one patient in the transition from LIS to CLIS with means of communication, who have Amyotrophic Lateral Sclerosis were followed at a regular interval for more than 1 year. During each visit, resting-state EEG was recorded before the brain–computer interface (BCI) based communication sessions. The resting-state EEG of the patients was analyzed to elucidate the evolution of their EEG spectrum over time with the disease’s progression to provide future BCI-research with the relevant information to classify changes in EEG evolution. Comparison of power spectral density (PSD) of these patients revealed a significant difference in the PSD’s of patients in CLIS without any means of communication and the patient in the transition from LIS to CLIS with means of communication. The EEG of patients without any means of communication is devoid of alpha, beta, and higher frequencies than the patient in transition who still had means of communication. The results show that the change in the EEG frequency spectrum may serve as an indicator of the communication ability of such patients.

This study compared the participants’ physiological responses and subjective evaluations of air scented with different concentrations of common rush (Juncus effusus L. var. decipiens Buchen.) (30 g and 15 g, with fresh air as a control). We asked 20 participants to complete a series of visual discrimination tasks while inhaling two different air samples. We evaluated (1) brain activity, (2) autonomic nervous activity, and (3) blood pressure and pulse rate, (4) in combination with self-evaluation. In addition, we quantified the concentrations of volatile organic compounds. The participants reported the scent to be sour, pungent, and smelly; this impression was likely caused by hexanal and acetic acid. Although the self-evaluations showed that participants did not enjoy the scent, their alpha amplitudes of electroencephalogram and parasympathetic nervous activity were increased, suggesting that participants were relaxed in this atmosphere. Moreover, a lower concentration resulted in a greater induction of relaxation. While the air was not pleasant-smelling, the volatile organic compounds present had a positive psychophysiological impact.

Brain functional changes could be observed in people after an experience of virtual reality (VR). The present study investigated cyber sickness and changes of brain regional activity using electroencephalogram (EEG)-based source localization, before and after a VR experience involving a smartphone-assisted head mount display. Thirty participants (mean age = 25 years old) were recruited. All were physically healthy and had no ophthalmological diseases. Their corrected vision was better than 20/20. Resting state EEG and the simulator sickness questionnaire (SSQ) were measured before and after the VR experience. Source activity of each frequency band was calculated using the sLORETA program. After the VR experience, the SSQ total score and sub scores (nausea, oculomotor symptoms, and disorientation) were significantly increased, and brain source activations were significantly increased: alpha1 activity in the cuneus and alpha2 activity in the cuneus and posterior cingulate gyrus (PCG). The change of SSQ score (after–before) showed significant negative correlation with the change of PCG activation (after–before) in the alpha2 band. The study demonstrated increased cyber sickness and increased alpha band power in the cuneus and PCG after the VR experience. Reduced PCG activation in alpha band may be associated with the symptom severity of cyber sickness.

We present an automated algorithm for unified rejection and repair of bad trials in magnetoencephalography (MEG) and electroencephalography (EEG) signals. Our method capitalizes on cross-validation in conjunction with a robust evaluation metric to estimate the optimal peak-to-peak threshold – a quantity commonly used for identifying bad trials in M/EEG. This approach is then extended to a more sophisticated algorithm which estimates this threshold for each sensor yielding trial-wise bad sensors. Depending on the number of bad sensors, the trial is then repaired by interpolation or by excluding it from subsequent analysis. All steps of the algorithm are fully automated thus lending itself to the name Autoreject. In order to assess the practical significance of the algorithm, we conducted extensive validation and comparisons with state-of-the-art methods on four public datasets containing MEG and EEG recordings from more than 200 subjects. The comparisons include purely qualitative efforts as well as quantitatively benchmarking against human supervised and semi-automated preprocessing pipelines. The algorithm allowed us to automate the preprocessing of MEG data from the Human Connectome Project (HCP) going up to the computation of the evoked responses. The automated nature of our method minimizes the burden of human inspection, hence supporting scalability and reliability demanded by data analysis in modern neuroscience. [Display omitted] •A strategy for artifact rejection in M/EEG using peak-to-peak thresholds is proposed•The thresholds are estimated using cross-validation with a robust error metric•The method detects and repairs outlier data segments for each sensor•Comparison with competing methods on 200 subjects with ground truth responses

Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not only limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of publications over the past two decades, further indicates the consistent improvements, as well as breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG) based BCI system is been deliberated. Secondly, a considerable number of popular BCI applications are reviewed in terms of its electrophysiological control signals, feature extraction, classification algorithms as well as the performance evaluation metrics. Finally, the challenges to the recent BCI system are discussed, and the possible solutions to mitigate the issues are recommended.

Epilepsy is a chronic brain condition characterized by an uncontrollable electric blast in the brain, manifested as Epilepsy seizures. More than one percent of the world population is affected by Epilepsy seizures. This framework employs a few corporal boundaries, such as temperature, pulse, and movement limits. The device measures the electrical activity of the brain by using an EEG sensor and alerts the patient according to the electrical brain activity. This paper focuses on the development of early detection of seizures by EEG to alert the patient to changes in his/her body.

Affective computing, a subcategory of artificial intelligence, detects, processes, interprets, and mimics human emotions. Thanks to the continued advancement of portable non-invasive human sensor technologies, like brain–computer interfaces (BCI), emotion recognition has piqued the interest of academics from a variety of domains. Facial expressions, speech, behavior (gesture/posture), and physiological signals can all be used to identify human emotions. However, the first three may be ineffectual because people may hide their true emotions consciously or unconsciously (so-called social masking). Physiological signals can provide more accurate and objective emotion recognition. Electroencephalogram (EEG) signals respond in real time and are more sensitive to changes in affective states than peripheral neurophysiological signals. Thus, EEG signals can reveal important features of emotional states. Recently, several EEG-based BCI emotion recognition techniques have been developed. In addition, rapid advances in machine and deep learning have enabled machines or computers to understand, recognize, and analyze emotions. This study reviews emotion recognition methods that rely on multi-channel EEG signal-based BCIs and provides an overview of what has been accomplished in this area. It also provides an overview of the datasets and methods used to elicit emotional states. According to the usual emotional recognition pathway, we review various EEG feature extraction, feature selection/reduction, machine learning methods (e.g., k-nearest neighbor), support vector machine, decision tree, artificial neural network, random forest, and naive Bayes) and deep learning methods (e.g., convolutional and recurrent neural networks with long short term memory). In addition, EEG rhythms that are strongly linked to emotions as well as the relationship between distinct brain areas and emotions are discussed. We also discuss several human emotion recognition studies, published between 2015 and 2021, that use EEG data and compare different machine and deep learning algorithms. Finally, this review suggests several challenges and future research directions in the recognition and classification of human emotional states using EEG.

The brain–computer interface (BCI) is an emerging technology that has the potential to revolutionize the world, with numerous applications ranging from healthcare to human augmentation. Electroencephalogram (EEG) motor imagery (MI) is among the most common BCI paradigms that have been used extensively in smart healthcare applications such as post-stroke rehabilitation and mobile assistive robots. In recent years, the contribution of deep learning (DL) has had a phenomenal impact on MI-EEG-based BCI. In this work, we systematically review the DL-based research for MI-EEG classification from the past ten years. This article first explains the procedure for selecting the studies and then gives an overview of BCI, EEG, and MI systems. The DL-based techniques applied in MI classification are then analyzed and discussed from four main perspectives: preprocessing, input formulation, deep learning architecture, and performance evaluation. In the discussion section, three major questions about DL-based MI classification are addressed: (1) Is preprocessing required for DL-based techniques? (2) What input formulations are best for DL-based techniques? (3) What are the current trends in DL-based techniques? Moreover, this work summarizes MI-EEG-based applications, extensively explores public MI-EEG datasets, and gives an overall visualization of the performance attained for each dataset based on the reviewed articles. Finally, current challenges and future directions are discussed.