EEG Bands Research Articles

Hyperscanning EEG Paradigm Applied to Remote vs. Face-To-Face Learning in Managerial Contexts: Which Is Better?

We propose a hyperscanning research design, where electroencephalographic (EEG) data were collected on an instructor and teams of learners. We compared neurophysiological measures within the frequency domain (delta, theta, alpha, and beta EEG bands) in the two conditions: face-to-face and remote settings. Data collection was carried out using wearable EEG systems. Conversational analysis was previously applied to detect comparable EEG time blocks and semantic topics. The digitalization of training can be considered a challenge but also a chance for organizations. However, if not carefully addressed, it might constitute a criticality. Limited research explored how remote, as opposed to face-to-face, training affects cognitive, (such as memory and attention), affective, and social processes in workgroups. Data showed an alpha desynchronization and, conversely, a theta and beta synchronization for the face-to-face condition. Moreover, trainees showed different patterns for beta power depending on the setting condition, with significantly increased power spectral density (PSD) in the face-to-face condition. These results highlight the relevance of neurophysiological measures in testing the e-learning process, in relation to the emotional engagement, memory encoding, and attentional processing.

Exploring the Effects of EEG-Based Alpha Neurofeedback on Working Memory Capacity in Healthy Participants

Neurofeedback, an operant conditioning neuromodulation technique, uses information from brain activities in real-time via brain-computer interface (BCI) technology. This technique has been utilized to enhance the cognitive abilities, including working memory performance, of human beings. The aims of this study are to investigate how alpha neurofeedback can improve working memory performance in healthy participants and to explore the underlying neural mechanisms in a working memory task before and after neurofeedback. Thirty-six participants divided into the NFT group and the control group participated in this study. This study was not blinded, and both the participants and the researcher were aware of their group assignments. Increasing power in the alpha EEG band was used as a neurofeedback in the eyes-open condition only in the NFT group. The data were collected before and after neurofeedback while they were performing the N-back memory task (N = 1 and N = 2). Both groups showed improvement in their working memory performance. There was an enhancement in the power of their frontal alpha and beta activities with increased working memory load (i.e., 2-back). The experimental group showed improvements in their functional connections between different brain regions at the theta level. This effect was absent in the control group. Furthermore, brain hemispheric lateralization was found during the N-back task, and there were more intra-hemisphere connections than inter-hemisphere connections of the brain. These results suggest that healthy participants can benefit from neurofeedback and from having their brain networks changed after the training.

A Two-Step Framework to Recognize Emotion Using the Combinations of Adjacent Frequency Bands of EEG

Electroencephalography (EEG)-based emotion recognition technologies can effectively help robots to perceive human behavior, which have attracted extensive attention in human–machine interaction (HMI). Due to the complexity of EEG data, current researchers tend to extract different types of hand-crafted features and connect all frequency bands for further study. However, this may result in the loss of some discriminative information of frequency band combinations and make the classification models unable to obtain the best results. In order to recognize emotions accurately, this paper designs a novel EEG-based emotion recognition framework using complementary information of frequency bands. First, after the features of the preprocessed EEG data are extracted, the combinations of all the adjacent frequency bands in different scales are obtained through permutation and reorganization. Subsequently, the improved classification method, homogeneous-collaboration-representation-based classification, is used to obtain the classification results of each combination. Finally, the circular multi-grained ensemble learning method is put forward to re-exact the characteristics of each result and merge the machine learning methods and simple majority voting for the decision fusion. In the experiment, the classification accuracies of our framework in arousal and valence on the DEAP database are 95.09% and 94.38% respectively, and that in the four classification problems on the SEED IV database is 96.37%.

EEG Features for Driver’s Mental Fatigue Detection: A Preliminary Work

Mental fatigue is one of the most typical human infirmities, resulting from an overload of work and lack of sleep which can reduce one’s intellectual resources. Different EEG features have been studied for detecting mental fatigue. This paper characterizes mental fatigue through the understanding of human EEG features for safe driving behaviour and to create an overview of the potential EEG features which are related to mental fatigue. A narrative review approach is employed for describing the neural activity of the human brain in mental fatigue. Specific EEG features in relation to driving tasks, relation to different EEG band waves, pre-processing and feature extraction methods are discussed. From this preliminary work, the increase of parietal alpha power seems to characterize the driver’s mental fatigue in most of the studies. We searched public EEG repositories for identifying potential data sources for our initial study. Finally, we propose a conceptual model that has potentials for identifying mental weariness. In conclusion, future works may involve the identification of other EEG features of higher importance for generalization across study conditions

THETA AND ALPHA BANDS SPECTRAL POWER OF RESTING-STATE EEG IN GROUPS WITH DIFFERENT EFFICIENCY OF JOINT ACTIVITY IN DIADS

The aim of the study was to compare the spectral characteristics of theta and alpha frequency bands of the resting-state EEG between groups of subjects with different performance of subsequent joint sensorimotor activity in dyads. The study involved 26 men who, in 13 pairs, performed “Columns” trainings with biofeedback from EMG signals from the flexor muscles of the leading hand. According to their performance, the subjects of each pair were assigned to one of 2 groups: “winners” or “losers”. A higher spectral power of the theta rhythm of the EEG with closed eyes was found in the group of “losers” in comparison with the group of “winners” in the frontal, central and temporal zones of the cortex. The “winners” showed a higher level of spectral power of the EEG alpha rhythm with the eyes closed, especially in the alpha-2 frequency range in all 8 zones. The effectiveness of individual and joint training correlated negatively with the theta power and positively with the power of the EEG alpha rhythms in the closed-eyed state.

Hybrid EEG-fNIRS Brain Computer Interface Based on Common Spatial Pattern by Using EEG-Informed General Linear Model

Hybrid brain computer interfaces (BCI) utilizing the high temporal resolution of electroencephalography (EEG) and the high spatial resolution of near-infrared spectroscopy (fNIRS) are preferred over single-modal BCIs. However, due to the large dimensionality of the multi-class statistical features commonly used in fNIRS signals, it is easy to cause overfitting of the EEG-fNIRS hybrid BCI classifier. Therefore, a low-dimensional feature extraction method for fNIRS based on the EEG-informed fNIRS general linear model (GLM) analysis is proposed in this paper. First, a regression coefficient matrix is obtained by using the EEG-informed fNIRS GLM with a time window added, and the common spatial pattern (CSP) features of this regression coefficient matrix are extracted as the fNIRS features. Lastly, the fNIRS features were combined with the CSP features extracted from the optimal narrow band of EEG as hybrid features, and the support vector machine (SVM) method is used to classify the samples with hybrid features. The proposed method was tested on a publicly available motor imagery dataset. The classification accuracy using fNIRS signals alone reached 68.79% (oxygenated hemoglobin) and 68.62% (deoxygenated hemoglobin), and the classification accuracy of combining EEG-fNIRS features reached 79.48%, which was higher than other existing methods using the same dataset. By using this fNIRS feature extraction method, the problem of poor performance of CSP on fNIRS signals is solved, which not only enriches the processing methods of fNIRS signals, but also improves the classification accuracy of hybrid EEG-fNIRS BCI in motor imagery tasks.

Analysing the Change in Brain Waves due to Heterogeneous Road Traffic Noise Exposure Using Electroencephalography Measurements.

Road traffic is the major source of noise pollution leading to human health impacts in urban areas. This study presents the relation between changes in human brain waves due to road traffic noise exposure in heterogeneous conditions. The results are based on Electroencephalogram (EEG) data collected from 12 participants through a listening experience of traffic scenarios at 14 locations in New Delhi, India. Energetic, spectral and temporal characteristics of the noise signals are presented. The impact of noise events on spectral perturbations and changes in the relative power (RP) of EEG signals are evaluated. Traffic noise variations modulate the rate of change in α and θ EEG bands of temporal, parietal and frontal lobe of the brain. The magnitude of event-related spectral perturbation (ERSP) increases with each instantaneous increase in traffic noise, such as honking. Individual noise events impact the temporal lobe more significantly in quieter locations compared with noisy locations. Increase in loudness changes the RP of α band in frontal lobe. Increase in temporal variation due to intermittent honking increases the RP of θ bands, especially in right parietal and frontal lobe. Change in sharpness leads to variation in the RP of right parietal lobe in theta band. Whereas, inverse relation is observed between roughness and the RP of right temporal lobe in gamma band. A statistical relationship between noise indicators and EEG response is established.

10 Minutes Frontal 40 Hz tACS-Effects on Working Memory Tested by Luck-Vogel Task.

Working memory is a cognitive process that involves short-term active maintenance, flexible updating, and processing of goal- or task-relevant information. All frequency bands are involved in working memory. The activities of the theta and gamma frequency bands in the frontoparietal network are highly involved in working memory processes; theta oscillations play a role in the temporal organization of working memory items, and gamma oscillations influence the maintenance of information in working memory. Transcranial alternating current stimulation (tACS) results in frequency-specific modulation of endogenous oscillations and has shown promising results in cognitive neuroscience. The electrophysiological and behavioral changes induced by the modulation of endogenous gamma frequency in the prefrontal cortex using tACS have not been extensively studied in the context of working memory. Therefore, we aimed to investigate the effects of frontal gamma-tACS on working memory outcomes. We hypothesized that a 10-min gamma tACS administered over the frontal cortex would significantly improve working memory outcomes. Young healthy participants performed Luck-Vogel cognitive behavioral tasks with simultaneous pre- and post-intervention EEG recording (Sham versus 40 Hz tACS). Data from forty-one participants: sham (15 participants) and tACS (26 participants), were used for the statistical and behavioral analysis. The relative changes in behavioral outcomes and EEG due to the intervention were analyzed. The results show that tACS caused an increase in the power spectral density in the high beta and low gamma EEG bands and a decrease in left-right coherence. On the other hand, tACS had no significant effect on success rates and response times. Conclusion: 10 min of frontal 40 Hz tACS was not sufficient to produce detectable behavioral effects on working memory, whereas electrophysiological changes were evident. The limitations of the current stimulation protocol and future directions are discussed in detail in the following sections.

Effectiveness of Anodal otDCS Following with Anodal tDCS Rather than tDCS Alone for Increasing of Relative Power of Intrinsic Matched EEG Bands in Rat Brains.

This study sought to determine whether (1) evidence is available of interactions between anodal tDCS and oscillated tDCS stimulation patterns to increase the power of endogenous brain oscillations and (2) the frequency matching the applied anodal otDCS's frequency and the brain's dominant intrinsic frequency influence power shifting during stimulation pattern sessions by both anodal DCS and anodal oscillated DCS. Rats received different anodal tDCS and otDCS stimulation patterns using 8.5 Hz and 13 Hz state-related dominant intrinsic frequencies of anodal otDCS. The rats were divided into groups with specific stimulation patterns: group A: tDCS-otDCS (8.5 Hz)-otDCS (13 Hz); group B: otDCS (8.5 Hz)-tDCS-otDCS (13 Hz); group C: otDCS (13 Hz)-tDCS-otDCS (8.5 Hz). Acute relative power changes (i.e., following 10 min stimulation sessions) in six frequency bands-delta (1.5-4 Hz), theta (4-7 Hz), alpha-1 (7-10 Hz), alpha-2 (10-12 Hz), beta-1 (12-15 Hz) and beta-2 (15-20 Hz)-were compared using three factors and repeated ANOVA measurement. For each stimulation, tDCS increased theta power band and, above bands alpha and beta, a drop in delta power was observed. Anodal otDCS had a mild increasing power effect in both matched intrinsic and delta bands. In group pattern stimulations, increased power of endogenous frequencies matched exogenous otDCS frequencies-8.5 Hz or 13 Hz-with more potent effects in upper bands. The power was markedly more potent with the otDCS-tDCS stimulation pattern than the tDCS-otDCS pattern. The findings suggest that the otDCS-tDCS pattern stimulation increased the power in matched intrinsic oscillations and, significantly, in the above bands in an ascending order. We provide evidence for the successful corporation between otDCS (as frequency-matched guidance) and tDCS (as a power generator) rather than tDCS alone when stimulating a desired brain intrinsic band (herein, tES specificity).

Supplementation With Iron Syrup or Iron-Containing Multiple Micronutrient Powders Alters Resting Brain Activity in Bangladeshi Children

BackgroundAnemia and iron deficiency have been associated with poor child cognitive development. A key rationale for the prevention of anemia using supplementation with iron has been the benefits to neurodevelopment. However, little causal evidence exists for these gains. ObjectivesWe aimed to examine effects of supplementation with iron or multiple micronutrient powders (MNPs) on brain activity measures using resting electroencephalography (EEG). MethodsChildren included in this neurocognitive substudy were randomly selected from the Benefits and Risks of Iron Supplementation in Children study, a double-blind, double-dummy, individually randomized, parallel-group trial in Bangladesh, in which children, starting at 8 mo of age, received 3 mo of daily iron syrup, MNPs, or placebo. Resting brain activity was recorded using EEG immediately after intervention (month 3) and after a further 9-month follow-up (month 12). We derived EEG band power measures for delta, theta, alpha, and beta frequency bands. Linear regression models were used to compare the effect of each intervention with that of placebo on the outcomes. ResultsData from 412 children at month 3 and 374 at month 12 were analyzed. At baseline, 43.9% were anemic and 26.7% were iron deficient. Immediately after intervention, iron syrup, but not MNPs, increased the mu alpha-band power, a measure that is associated with maturity and the production of motor actions (iron vs. placebo: mean difference = 0.30; 95% CI: 0.11, 0.50 μV2; P = 0.003; false discovery rate adjusted P = 0.015). Despite effects on hemoglobin and iron status, effects were not observed on the posterior alpha, beta, delta, and theta bands, nor were effects sustained at the 9-month follow-up. ConclusionsThe effect size for immediate effects on the mu alpha-band power is comparable in magnitude with psychosocial stimulation interventions and poverty reduction strategies. However, overall, we did not find evidence for long-lasting changes in resting EEG power spectra from iron interventions in young Bangladeshi children. This trial was registered at www.anzctr.org.au as ACTRN12617000660381.

Characterizing The State Of Heart Rate Autonomic Regulation And EEG In Elderly Women With Falls Syndrome

Objective — The goal of our study was to evaluate the contribution of the central and autonomic nervous systems to the maintenance of postural balance in the elderly. Material and Methods ― This study included 120 healthy women 60-74 YO living in the community. They were distributed among two subgroups: (1) fallers (n=60) and (2) non-fallers (n=60). Fallers had falls in the past 12 months, while non-fallers had none. Walk tests were performed using Balance Master® posturography machine (NeuroCom, Natus, USA). The state of the central nervous system was assessed by Geodesic 300 EEG system (GSN; Electrical Geodesics, Inc.; Eugene, OR, USA). The state of the autonomic nervous system was assessed by the VNS-Spectrum (Neurosoft, Russia) via cardiointervalography. Results ― In Sit to Stand test, the postural sway velocity was higher (p=0.047) and the walking speed (p=0.008) in Tandem Walk test was lower in fallers, compared with non-fallers. The results of the Step Quick Turn test showed that the movement times of the left and right legs were longer for the fallers vs. non-fallers (p=0.044 and p=0.036, respectively), and postural sway values during turning left or right were higher in fallers as well (p=0.001 and p=0.003, correspondingly). At the same time, Step Up and Over test revealed that lift-up index values were higher in fallers vs. non-fallers (p=0.016). There were no statistically significant differences between the groups in the state of the autonomic nervous system. The absolute spectral power of the EEG was lower in the frontal region of the right hemisphere in the alpha and beta EEG bands in the study group, compared with the control. Conclusion ― A deterioration in the quality of performing complex motor actions and a decrease in postural control in elderly women with falls syndrome were which may have caused their postural instability. Our results confirmed the need for early assessment of the dynamic components of postural control to prevent the development of falls as a geriatric syndrome.

Exploring the classification performance of different EEG bands for anesthesia monitoring

Exploring the classification performance of different EEG bands for anesthesia monitoring

Granger-Causality-Based Multi-Frequency Band EEG Graph Feature Extraction and Fusion for Emotion Recognition.

Graph convolutional neural networks (GCN) have attracted much attention in the task of electroencephalogram (EEG) emotion recognition. However, most features of current GCNs do not take full advantage of the causal connection between the EEG signals in different frequency bands during the process of constructing the adjacency matrix. Based on the causal connectivity between the EEG channels obtained by Granger causality (GC) analysis, this paper proposes a multi-frequency band EEG graph feature extraction and fusion method for EEG emotion recognition. First, the original GC matrices between the EEG signals at each frequency band are calculated via GC analysis, and then they are adaptively converted to asymmetric binary GC matrices through an optimal threshold. Then, a kind of novel GC-based GCN feature (GC-GCN) is constructed by using differential entropy features and the binary GC matrices as the node values and adjacency matrices, respectively. Finally, on the basis of the GC-GCN features, a new multi-frequency band feature fusion method (GC-F-GCN) is proposed, which integrates the graph information of the EEG signals at different frequency bands for the same node. The experimental results demonstrate that the proposed GC-F-GCN method achieves better recognition performance than the state-of-the-art GCN methods, for which average accuracies of 97.91%, 98.46%, and 98.15% were achieved for the arousal, valence, and arousal-valence classifications, respectively.

Electroencephalogram-based prediction and detection of responsiveness to noxious stimulation in critical care patients: a retrospective single-centre analysis

BackgroundMonitoring of pain and nociception in critical care patients unable to self-report pain remains a challenge, as clinical signs are neither sensitive nor specific. Available technical approaches are limited by various constraints. We investigated the electroencephalogram (EEG) for correlates that precede or coincide with behavioural nociceptive responses to noxious stimulation. MethodsIn this retrospective study, we analysed frontal EEG recordings of 64 critical care patients who were tracheally intubated and ventilated before, during, and after tracheal suctioning. We investigated EEG power bands for correlates preceding or coinciding with behavioural responses (Behavioural Pain Scale ≥7). We applied the Mann–Whitney U-test to calculate corresponding P-values. ResultsStrong behavioural responses were preceded by higher normalised power in the 2.5–5 Hz band (+17.1%; P<0.001) and lower normalised power in the 0.1–1.5 Hz band (–10.5%; P=0.029). After the intervention, strong behavioural responses were associated with higher normalised EEG power in the 2.5–5 Hz band (+16.6%; P=0.021) and lower normalised power in the 8–12 Hz band (–51.2%; P=0.037) ConclusionsWe observed correlates in EEG band power that precede and coincide with behavioural responses to noxious stimulation. Based on previous findings, some of the power bands could be linked to processing of nociception, arousal, or sedation effects. The power bands more closely related to nociception and arousal could be used to improve monitoring of nociception and to optimise analgesic management in critical care patients. Clinical trial registrationDRKS00011206.

Utilizing artificial intelligence and electroencephalography to assess expertise on a simulated neurosurgical task

Virtual reality surgical simulators have facilitated surgical education by providing a safe training environment. Electroencephalography (EEG) has been employed to assess neuroelectric activity during surgical performance. Machine learning (ML) has been applied to analyze EEG data split into frequency bands. Although EEG is widely used in fields requiring expert performance, it has yet been used to classify surgical expertise. Thus, the goals of this study were to (a) develop an ML model to accurately differentiate skilled and less-skilled performance using EEG data recorded during a simulated surgery, (b) explore the relative importance of each EEG bandwidth to expertise, and (c) analyze differences in EEG band powers between skilled and less-skilled individuals. We hypothesized that EEG recordings during a virtual reality surgery task would accurately predict the expertise level of the participant. Twenty-one participants performed three simulated brain tumor resection procedures on the NeuroVR™ platform (CAE Healthcare, Montreal, Canada) while EEG data was recorded. Participants were divided into 2 groups. The skilled group was composed of five neurosurgeons and five senior neurosurgical residents (PGY4-6), and the less-skilled group was composed of six junior residents (PGY1-3) and five medical students. A total of 13 metrics from EEG frequency bands and ratios (e.g., alpha, theta/beta ratio) were generated. Seven ML model types were trained using EEG activity to differentiate between skilled and less-skilled groups. The artificial neural network achieved the highest testing accuracy of 100% (AUROC = 1.0). Model interpretation via Shapley analysis identified low alpha (8–10 Hz) as the most important metric for classifying expertise. Skilled surgeons displayed higher (p = 0.044) low-alpha than the less-skilled group. Furthermore, skilled surgeons displayed significantly lower TBR (p = 0.048) and significantly higher beta (13–30 Hz, p = 0.049), beta 1 (15–18 Hz, p = 0.014), and beta 2 (19–22 Hz, p = 0.015), thus establishing these metrics as important markers of expertise. ACGME Core CompetenciesPractice-Based Learning and Improvement.

Electroencephalogram pattern association with drug-related cues in a long-duration virtual reality environment in patients with methamphetamine use disorder.

The cognitive processing of drug-related cues and the subsequent dysregulation of behaviour play a central role in the pathophysiology of substance use disorders. Prior studies are limited by small sample sizes and a lack of immersion in stimulus presentation. In the present study, we recruited patients with methamphetamine use disorder (MUD; N= 1099) from four compulsory isolated detoxification centres and healthy control participants (N= 305). With a 12-min-long virtual reality (VR) protocol stimulus, we discovered that patients showed a decrease in electroencephalogram(EEG) power across alpha to gamma bands in anterior scalp regions under methamphetamine-related VR stimuli (e.g. a glass pipe and medical tubing) compared with the control stimuli (e.g. balls and cubes). Analysis of variance (ANOVA) showed that the interaction effects of stimuli type and group were significant in five EEG bands. Using generalised linear models, we classified the stimuli type (i.e. drug-related vs. drug-unrelated cues) in MUD patients with an f1 score of 90% on an out-of-sample testing set. The decreases of EEG between drug-related cues and drug-unrelated cues in delta, theta and alpha frequency bands are more frequently seen in patients than in healthy controls, perhaps reflecting general arousal and attenuated impulsive control. Our results suggest that EEG responses elicited by long-duration methamphetamine-related VR cues showed a specific signature, which may have future clinical implications.

Wavelet based machine learning models for classification of human emotions using EEG signal

Humans have the ability to portray different expressions contrary to the emotional state of mind. Therefore, it is difficult to judge the human's real emotional state simply by judging the physical appearance. Although researchers are working on facial expressions analysis, voice recognition, gesture recognition accuracy levels of such analysis are much less and the results are not reliable. Classifying the human emotions with machine learning models and extracting discrete wavelet features of Electroencephalogram (EEG) is proposed. The EEG data from Database for Emotion Analysis using Physiological signal (DEAP) online datasets is used for analysis and consists of peripheral biological signals as well as EEG recordings. EEG signal is collected from 32 subjects while watching 40 1-min-long music videos. Each video clip is rated by the participants in terms of the level of Valence, Arousal, Dominance. In the proposed work we have considered a significant band of EEG with a reduced frontal electrode (Fp1, F3, F4, Fp2) to get a comparable good result. The accuracy obtained from K- nearest neighbour (KNN), Fine KNN and Support Vector Machine (SVM) are 92.5%, 90% and 90% respectively for Valence, Arousal and Dominance.

Electroencephalography functional connectivity-A biomarker for painful polyneuropathy.

Advanced analysis of electroencephalography (EEG) data has become an essential tool in brain research. Based solely on resting state EEG signals, a data-driven, predictive and explanatory approach is presented to discriminate painful from non-painful diabetic polyneuropathy (DPN) patients. Three minutes long, 64 electrode resting-state recordings were obtained from 180 DPN patients. The analysis consisted of a mixture of traditional, explanatory and machine learning analyses. First, the 10 functional bivariate connections best differentiating between painful and non-painful patients in each EEG band were identified and the relevant receiver operating characteristic was calculated. Later, those connections were correlated with selected clinical parameters. Predictive analysis indicated that theta and beta bands contain most of the information required for discrimination between painful and non-painful polyneuropathy patients, with area under the receiver operating characteristic curve values of 0.93 for theta and 0.89 for beta bands. Assessing statistical differences between the average magnitude of functional connectivity values and clinical pain parameters revealed that painful DPN patients had significantly higher cortical functional connectivity than non-painful ones (p= 0.008 for theta and p= 0.001 for alpha bands). Moreover, intra-band analysis of individual significant functional connections revealed a positive correlation with average reported pain in the previous 3months in all frequency bands. Resting state EEG functional connectivity can serve as a highly accurate biomarker for the presence or absence of pain in DPN patients. This highlights the importance of the brain, in addition to the peripheral lesions, in generating the clinical pain picture. This tool can probably be extended to other pain syndromes.

Electrophysiological markers of memory consolidation in the human brain when memories are reactivated during sleep

Human accomplishments depend on learning, and effective learning depends on consolidation. Consolidation is the process whereby new memories are gradually stored in an enduring way in the brain so that they can be available when needed. For factual or event knowledge, consolidation is thought to progress during sleep as well as during waking states and to be mediated by interactions between hippocampal and neocortical networks. However, consolidation is difficult to observe directly but rather is inferred through behavioral observations. Here, we investigated overnight memory change by measuring electrical activity in and near the hippocampus. Electroencephalographic (EEG) recordings were made in five patients from electrodes implanted to determine whether a surgical treatment could relieve their seizure disorders. One night, while each patient slept in a hospital monitoring room, we recorded electrophysiological responses to 10 to 20 specific sounds that were presented very quietly, to avoid arousal. Half of the sounds had been associated with objects and their precise spatial locations that patients learned before sleep. After sleep, we found systematic improvements in spatial recall, replicating prior results. We assume that when the sounds were presented during sleep, they reactivated and strengthened corresponding spatial memories. Notably, the sounds also elicited oscillatory intracranial EEG activity, including increases in theta, sigma, and gamma EEG bands. Gamma responses, in particular, were consistently associated with the degree of improvement in spatial memory exhibited after sleep. We thus conclude that this electrophysiological activity in the hippocampus and adjacent medial temporal cortex reflects sleep-based enhancement of memory storage.

Modulating break types induces divergent low band EEG processes during post-break improvement: A power spectral analysis.

Conventional wisdom suggests mid-task rest as a potential approach to relieve the time-on-task (TOT) effect while accumulating evidence indicated that acute exercise might also effectively restore mental fatigue. However, few studies have explored the neural mechanism underlying these different break types, and the results were scattered. This study provided one of the first looks at how different types of fatigue-recovery break exerted influence on the cognitive processes by evaluating the corresponding behavioral improvement and neural response (EEG power spectral) in a sustained attention task. Specifically, 19 participants performed three sessions of psychomotor vigilance tasks (PVT), with one session including a continuous 30-min PVT while the other two sessions additionally inserted a 15-min mid-task cycling and rest break, respectively. For behavioral performance, both types of break could restore objective vigilance transiently, while subjective feeling was only maintained after mid-task rest. Moreover, divergent patterns of EEG change were observed during post-break improvement. In detail, relative theta decreased and delta increased immediately after mid-task exercise, while decreased delta was found near the end of the rest-inserted task. Meanwhile, theta and delta could serve as neurological indicators to predict the reaction time change for exercise and rest intervention, respectively. In sum, our findings provided novel evidence to demonstrate divergent neural patterns following the mid-task exercise and rest intervention to counter TOT effects, which might lead to new insights into the nascent field of neuroergonomics for mental fatigue restoration.