Combined EEG Research Articles

Using mobile EEG to study auditory work strain during simulated surgical procedures

Surgical personnel face various stressors in the workplace, including environmental sounds. Mobile electroencephalography (EEG) offers a promising approach for objectively measuring how individuals perceive sounds. Because surgical performance does not necessarily decrease with higher levels of distraction, EEG could help guide noise reduction strategies that are independent of performance measures. In this study, we utilized mobile EEG to explore how a realistic soundscape is perceived during simulated laparoscopic surgery. To examine the varying demands placed on personnel in different situations, we manipulated the cognitive demand during the surgical task, using a memory task. To assess responses to the soundscape, we calculated event-related potentials for distinct sound events and temporal response functions for the ongoing soundscape. Although participants reported varying degrees of demand under different conditions, no significant effects were observed on surgical task performance or EEG parameters. However, changes in surgical task performance and EEG parameters over time were noted, while subjective results remained consistent over time. These findings highlight the importance of using multiple measures to fully understand the complex relationship between sound processing and cognitive demand. Furthermore, in the context of combined EEG and audio recordings in real-life scenarios, a sparse representation of the soundscape has the advantage that it can be recorded in a data-protected way compared to more detailed representations. However, it is unclear whether information get lost with sparse representations. Our results indicate that sparse and detailed representations are equally effective in eliciting neural responses. Overall, this study marks a significant step towards objectively investigating sound processing in applied settings.

Assessment of cognitive workload using simultaneous EEG and fNIRS: A comparison of feature combinations

The assessment of cognitive workload is a critical component in evaluating mental activity. It is essential in psychology, especially in professions with heightened cognitive demands, such as aviation and surgery. Even marginal improvements in workload assessment can substantially enhance safety and overall performance. This study aimed to improve the accuracy of cognitive workload assessment by employing a combined EEG+fNIRS approach with a minimal number of features. The Tunable Q-factor Wavelet Transform (TQWT) was applied to signals from EEG and fNIRS to extract relevant features. TQWT was chosen for its ability to adapt to the oscillatory behavior of the signal, and the features extracted from the transform facilitated the differentiation between different signal classes. This study introduced six proposed approaches, with the first focusing on EEG signals, the second on fNIRS signals, and the subsequent three to six approaches involving combinations of these two signals. Features evaluation highlighted the pivotal role of channels related to the frontal region and sub-bands associated with alpha and theta rhythms in EEG signals. Additionally, the very low-frequency range and the primary undecomposed signal in fNIRS signals contributed significantly to cognitive workload classification. The classification results demonstrated the efficacy of combining EEG and fNIRS signals in cognitive workload detection, providing higher accuracy than single-signal approaches. Compared to other feature combination approaches, the proposed approach based on Canonical Correlation Analysis (CCA) with a linear SVM classifier achieved superior accuracy across all investigated cognitive workload levels, with accuracies of 99.6 %, 99.4 %, 98.9 %, and 97.5 % for the 0-back vs. 3-back, 0-back vs. 2-back, 2-back vs. 3-back, and 0-back vs. 2-back vs. 3-back comparisons, respectively.

A Within-Subject Multimodal NIRS-EEG Classifier for Infant Data.

Functional Near Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG) are commonly employed neuroimaging methods in developmental neuroscience. Since they offer complementary strengths and their simultaneous recording is relatively easy, combining them is highly desirable. However, to date, very few infant studies have been conducted with NIRS-EEG, partly because analyzing and interpreting multimodal data is challenging. In this work, we propose a framework to carry out a multivariate pattern analysis that uses an NIRS-EEG feature matrix, obtained by selecting EEG trials presented within larger NIRS blocks, and combining the corresponding features. Importantly, this classifier is intended to be sensitive enough to apply to individual-level, and not group-level data. We tested the classifier on NIRS-EEG data acquired from five newborn infants who were listening to human speech and monkey vocalizations. We evaluated how accurately the model classified stimuli when applied to EEG data alone, NIRS data alone, or combined NIRS-EEG data. For three out of five infants, the classifier achieved high and statistically significant accuracy when using features from the NIRS data alone, but even higher accuracy when using combined EEG and NIRS data, particularly from both hemoglobin components. For the other two infants, accuracies were lower overall, but for one of them the highest accuracy was still achieved when using combined EEG and NIRS data with both hemoglobin components. We discuss how classification based on joint NIRS-EEG data could be modified to fit the needs of different experimental paradigms and needs.

A new, neuromarker-based, form of combined neurofeedback EEG/tDCS training in the reduction of occupational burnout syndrome in an anaesthetic nurse working with COVID-19 patients

Anesthesia nurses working with COVID-19 patients experience exceptional emotional and physical strain due to the nature of their work in a high-risk environment. This has negative consequences for their physical and mental health and often leads to the development of occupational burnout syndrome. The purpose of this study was to evaluate a new neuromarker-based form of combined EEG / TDCS training in reducing occupational burnout syndrome in a nurse anesthetist working with patients with COVID-19.The patient, an unmarried 30-year-old anaesthetic nurse who had been working in the profession for six years, was referred to the Reintegration and Training Center of the Polish Neuropsychological Society in late 2022. A clinical interview and analysis of her medical records established that for the past two years (from early April 2020 to mid-April 2022), during which she had worked at a closed Intensive Care Unit (ICU) for patients infected with SARS-CoV-2 and contracting COVID-19, had been extremely taxing and anxiety-producing. With each successive wave of the pandemic, when the ICU was in a critical situation full of all kinds of stressors, including numerous cardiac arrests and the deaths of those she was caring for, she became increasingly mentally and physically exhausted. In addition, she experienced two losses of consciousness, but EEG and CT scans showed no changes. Her medical condition, especially her attention deficit disorder, working memory and confusion, caused her to lose her ability to work. She went on a disability pension due to full-blown occupational burnout syndrome (ICD-10 code 273.0) with a variety of psychosomatic symptoms. Several episodes of epileptic seizures were observed during her 40-minute qEEG recording with eyes open and closed and her performance of the PsyTask. The independent component analysis (ICA) extracted, among other things, a delta that corresponded to a 3Hz seizure and was generated in the prefrontal-temporal region of the right cerebral hemisphere. Functional neuromarkers of anxiety and depression were also detected in both the raw EEG recordings and data obtained from qEEG/ERPs analysis compared to the norm from the Human Brain Index (HBI). The new, neuromarker-based, form of combined EEG/tDCS training carried out the basisof these neuromarkers, conducted in 32 treatment sessions, proved effective in reducing individual disorders, including seizure activity.The examined anaesthetic nurse recovered from anxiety and depression, as well as occupational burnout syndrome and other psychosomatic disorders after 32 sessions of the new, combined neurofeedback EEG/tDCS training, and she returned to work at the ICU. Specific patterns of qEEG and ERPs serve as neuromarkers for constructing the protocol and for monitoring the results of intervention. HBI methodology can be successfully used in neurodiagnosis and neurotherapy offered to patients with occupational burnout syndrome.

Intracerebral Dynamics of Sleep Arousals: A Combined Scalp-Intracranial EEG Study.

As an intrinsic component of sleep architecture, sleep arousals represent an intermediate state between sleep and wakefulness and are important for sleep-wake regulation. They are defined in an all-or-none manner, whereas they actually present a wide range of scalp-electroencephalography (EEG) activity patterns. It is poorly understood how these arousals differ in their mechanisms. Stereo-EEG (SEEG) provides the unique opportunity to record intracranial activities in superficial and deep structures in humans. Using combined polysomnography and SEEG, we quantitatively categorized arousals during nonrapid eye movement sleep into slow wave (SW) and non-SW arousals based on whether they co-occurred with a scalp-EEG SW event. We then investigated their intracranial correlates in up to 26 brain regions from 26 patients (12 females). Across both arousal types, intracranial theta, alpha, sigma, and beta activities increased in up to 25 regions (p < 0.05; d = 0.06-0.63), while gamma and high-frequency (HF) activities decreased in up to 18 regions across the five brain lobes (p < 0.05; d = 0.06-0.44). Intracranial delta power widely increased across five lobes during SW arousals (p < 0.05 in 22 regions; d = 0.10-0.39), while it widely decreased during non-SW arousals (p < 0.05 in 19 regions; d = 0.10-0.30). Despite these main patterns, unique activities were observed locally in some regions such as the hippocampus and middle cingulate cortex, indicating spatial heterogeneity of arousal responses. Our results suggest that non-SW arousals correspond to a higher level of brain activation than SW arousals. The decrease in HF activities could potentially explain the absence of awareness and recollection during arousals.

Recognizing the situation awareness of forklift operators based on EEG techniques in a field experiment.

Lack of situation awareness (SA) is the primary cause of human errors when operating forklifts, so determining the SA level of the forklift operator is crucial to the safety of forklift operations. An EEG recognition approach of forklift operator SA in actual settings was presented in order to address the issues with invasiveness, subjectivity, and intermittency of existing measuring methods. In this paper, we conducted a field experiment that mimicked a typical forklift operation scenario to verify the differences in EEG states of forklift operators with different SA levels and investigate the correlation of multi-band combination features of each brain region of forklift operators with SA. Based on the sensitive EEG combination indexes, Support Vector Mechanism was used to construct a forklift operator SA recognition model. The results revealed that there were differences between forklift operators with high and low SA in the θ, α, and β frequency bands in zones F, C, P, and O; combined EEG indicators θ/β, (α + θ)/(α + β), and θ/(α + β) in zones F, P, and C were significantly correlated with SA; the recognition accuracy of the model reached 88.64% in the case of combined EEG indicators of zones C & F & P as input. It could provide a reference for SA measurement, contributing to the improvement of SA.

Awareness of errors is reduced by sleep loss.

The ability to detect and subsequently correct errors is important in preventing the detrimental consequences of sleep loss. The Error Related Negativity (ERN), and the error positivity (Pe) are established neural correlates of error processing. Previous work has shown sleep loss reduces ERN and Pe, indicating sleep loss impairs error-monitoring processes. However, no previous work has examined behavioral error awareness, in conjunction with EEG measures, under sleep loss conditions, and studies of sleep restriction are lacking. Using combined behavioral and EEG measures, we report two studies investigating the impact of total sleep deprivation (TSD) and sleep restriction (SR) on error awareness. Fourteen healthy participants completed the Error Awareness Task under conditions of TSD and 27 completed the same task under conditions of SR. It was found that TSD did not influence behavioral error awareness or ERN or Pe amplitude, however, SR reduced behavioral error awareness, increased the time taken to detect errors, and reduced Pe amplitude. Findings indicate individuals who are chronically sleep restricted are at risk for reduced recognition of errors. Reduced error awareness may be one factor contributing to the increased accidents and injuries seen in contexts where sleep loss is prevalent.

Dynamical irreversibility, high‐order interactions, and whole‐brain modeling of gamma oscillations in neurodegeneration

AbstractBackgroundAbnormal gamma oscillations (γ) have been systematically reported in preclinical animal models of Alzheimer’s disease (AD) and human AD patients. However, little is known about the underlying mechanisms, non‐linear dynamics, and high‐order interactions of γ in dementia.MethodTo bridge this gap, we combined EEG and fMRI with three novel approaches to offer a comprehensive pathophysiological characterization of γ oscillations: We assessed the irreversibility of large‐scale brain dynamics to provide a precise signature capable of distinguishing AD at the global, local and network levels and at different oscillatory regimes (study 1). A high‐order functional connectivity (HOFC) multivariate information theory approach capturing interactions between 3 or more regions across spatiotemporal scales was compared with standard connectivity metrics for dementia characterization (study 2). A whole‐brain semi‐empirical model with a perturbational approach combined EEG, structural connectivity, and atrophy to explain the disrupted high‐order interactions observed in AD (study 3).ResultsStudy 1: AD was associated with a breakdown of temporal irreversibility at the global, local, and network levels at γ and other oscillations. The irreversibility of brain dynamics provided higher accuracy and more distinctive information than classical neurocognitive measures when differentiating AD from controls. Study 2: EEG‐HOFC revealed hypoconnectivity in γ between frontal, limbic, and sensory regions in AD, and large effect sizes, compared to standard pairwise metrics, proving a more accurate and parsimonious characterization of AD and frontotemporal dementia (FTD) patients. Study 3: whole‐brain modeling showed that a combined mechanism involving reduced structural integration (DTI) and hypo‐excitability (altered excitation/inhibition balance) triggered the altered high‐order interaction γ dynamics observed in AD and FTD. A perturbational approach identified physiopathological regions (SFG, MTG, precuneus) deviating the trajectories from disease to a healthy brain state.ConclusionThe present results offer new evidence regarding the pathophysiological link, mechanistic processes, and non‐linear whole‐brain dynamics between altered resting‐state γ rhythms and the characterization of AD and FTD, opening new avenues for dementia research at different levels.

Abstract 348: Machine Learning Approaches for Early Outcome Risk Stratification Within 6 Hours of Cardiac Arrest

Background: Formal prognostication should be delayed at least 72 hours after cardiac arrest, but early stratification of risk severity provides important information for medical decision making, research enrollment, and precision medicine. Hypothesis: EEG-derived indices are valid biomarkers of severity of injury after cardiac arrest and contribute to early identification of risk. Aims: To assess the relative impact of early clinical data and processed EEG indices in the first 6 hours after recovery of spontaneous circulation (ROSC). Methods: Data from the first 6 hours after ROSC in the International Cardiac Arrest Registry (INTCAR) from Maine Medical Center including demographics, cardiac arrest data, admission vital signs and laboratory results and processed EEG indices at 6 hours after ROSC (bispectral index or BIS6 and suppression ratio or SR6) were extracted. Cerebral Performance Category (CPC) scores at hospital discharge and 6-month follow-up were dichotomized into good (CPC = 1-2) and poor (CPC = 3-5) outcomes. Three models were trained, one with INTCAR data, a second with EEG data, and the third combining INTCAR and BIS6-SR6. Six machine learning algorithms were applied: Catboost, random forest, Xgboost, Adaboost, support vector machine and logistic regression. Variable importance was analyzed by the decrease of impurity of random forest. Results: Among 913 included patients, median age was 59 years (95%CI 23-85), 623 (68%) were male, initial rhythm was shockable in 405 (44%) patients, and average time to ROSC was 23 (95%CI 3 - 73) minutes. Survival to hospital discharge occurred in 41% (364/893), and poor outcome occurred in 72.7% at 6-month follow-up (607/835). For CPC at discharge, area-under-the curve (AUC) was 0.85 for the combined INTCAR and EEG data, greater than INTCAR (AUC = 0.80) or EEG (AUC = 0.77). For the CPC at follow-up, the AUC was 0.88 for the combined data, greater than INTCAR (AUC = 0.83) or EEG (AUC = 0.79). Variable ranking analysis showed that BIS6 and SR6 were the two most informative features for both outcomes. Conclusions: The processed EEG variables BIS6 and SR6 significantly contribute to risk identification in the first 6 hours after ROSC, and combined with early clinical data provide reliable risk stratification.

A Combined Virtual Electrode-Based ESA and CNN Method for MI-EEG Signal Feature Extraction and Classification.

A Brain-Computer Interface (BCI) is a medium for communication between the human brain and computers, which does not rely on other human neural tissues, but only decodes Electroencephalography (EEG) signals and converts them into commands to control external devices. Motor Imagery (MI) is an important BCI paradigm that generates a spontaneous EEG signal without external stimulation by imagining limb movements to strengthen the brain's compensatory function, and it has a promising future in the field of computer-aided diagnosis and rehabilitation technology for brain diseases. However, there are a series of technical difficulties in the research of motor imagery-based brain-computer interface (MI-BCI) systems, such as: large individual differences in subjects and poor performance of the cross-subject classification model; a low signal-to-noise ratio of EEG signals and poor classification accuracy; and the poor online performance of the MI-BCI system. To address the above problems, this paper proposed a combined virtual electrode-based EEG Source Analysis (ESA) and Convolutional Neural Network (CNN) method for MI-EEG signal feature extraction and classification. The outcomes reveal that the online MI-BCI system developed based on this method can improve the decoding ability of multi-task MI-EEG after training, it can learn generalized features from multiple subjects in cross-subject experiments and has some adaptability to the individual differences of new subjects, and it can decode the EEG intent online and realize the brain control function of the intelligent cart, which provides a new idea for the research of an online MI-BCI system.

The Influence of Electromyographic on Electroencephalogram-Based Monitoring: Putting the Forearm on the Forehead.

Monitoring the electroencephalogram (EEG) during general anesthesia can help to safely navigate the patient through the procedure by avoiding too deep or light anesthetic levels. In daily clinical practice, the EEG is recorded from the forehead and available neuromonitoring systems translate the EEG information into an index inversely correlating with the anesthetic level. Electrode placement on the forehead can lead to an influence of electromyographic (EMG) activity on the recorded signal in patients without neuromuscular blockade (NMB). A separation of EEG and EMG in the clinical setting is difficult because both signals share an overlapping frequency range. Previous research showed that indices decreased when EMG was absent in awake volunteers with NMB. Here, we investigated to what extent the indices changed, when EEG recorded during surgery with NMB agents was superimposed with EMG. We recorded EMG from the flexor muscles of the forearm of 18 healthy volunteers with a CONOX monitor during different activity settings, that is, during contraction using a grip strengthener and during active diversion (relaxed arm). Both the forehead and forearm muscles are striated muscles. The recorded EMG was normalized by z -scoring and added to the EEG in different amplification steps. The EEG was recorded during anesthesia with NMB. We replayed these combined EEG and EMG signals to different neuromonitoring systems, that is, bispectral index (BIS), CONOX with qCON and qNOX, and entropy module with state entropy (SE) and response entropy (RE). We used the Friedman test and a Tukey-Kramer post hoc correction for statistical analysis. The indices of all neuromonitoring systems significantly increased when the EEG was superimposed with the contraction EMG and with high EMG amplitudes, the monitors returned invalid values, representative of artifact contamination. When replaying the EEG being superimposed with "relaxed" EMG, the qCON and BIS showed significant increases, but not SE and RE. For SE and RE, we observed an increased number of invalid values. With our approach, we could show that EMG activity during contraction and resting state can influence the neuromonitoring systems. This knowledge may help to improve EEG-based patient monitoring in the future and help the anesthesiologist to use the neuromonitoring systems with more knowledge regarding their function.

Altered neural oscillations underlying visuospatial processing in cerebral visual impairment.

Visuospatial processing deficits are commonly observed in individuals with cerebral visual impairment, even in cases where visual acuity and visual field functions are intact. Cerebral visual impairment is a brain-based visual disorder associated with the maldevelopment of central visual pathways and structures. However, the neurophysiological basis underlying higher-order perceptual impairments in this condition has not been clearly identified, which in turn poses limits on developing rehabilitative interventions. Using combined eye tracking and EEG recordings, we assessed the profile and performance of visual search on a naturalistic virtual reality-based task. Participants with cerebral visual impairment and controls with neurotypical development were instructed to search, locate and fixate on a specific target placed among surrounding distractors at two levels of task difficulty. We analysed evoked (phase-locked) and induced (non-phase-locked) components of broadband (4-55 Hz) neural oscillations to uncover the neurophysiological basis of visuospatial processing. We found that visual search performance in cerebral visual impairment was impaired compared to controls (as indexed by outcomes of success rate, reaction time and gaze error). Analysis of neural oscillations revealed markedly reduced early-onset evoked theta [4-6 Hz] activity (within 0.5 s) regardless of task difficulty. Moreover, while induced alpha activity increased with task difficulty in controls, this modulation was absent in the cerebral visual impairment group identifying a potential neural correlate related to deficits with visual search and distractor suppression. Finally, cerebral visual impairment participants also showed a sustained induced gamma response [30-45 Hz]. We conclude that impaired visual search performance in cerebral visual impairment is associated with substantial alterations across a wide range of neural oscillation frequencies. This includes both evoked and induced components suggesting the involvement of feedforward and feedback processing as well as local and distributed levels of neural processing.

Product Styling Cognition Based on Kansei Engineering Theory and Implicit Measurement

Effective product styling designs must increasingly address users’ emotional requirements. This study introduces a product styling design method combining electroencephalography (EEG) and eye tracking for multimodal measurement based on the Kansei engineering theory. The feasibility of determining a target image using a similarity calculation model is verified. An experimental paradigm based on implicit measures is presented for product styling cognition research. This paradigm involves determining the target image, sample selection, target image matching experiments, and product styling cognition experiments. Based on the combined EEG and eye-tracking measurements, insights into product-form cognition are deduced to provide a scientific basis for product-form innovation design. Notably, variations in event-related potential during user cognition of product styling are more evident in the product-styling perception phase than in the evaluation phase. In the styling perception phase, samples with “high match” with the target image elicit more pronounced EEG responses than those with “low match”. These findings demonstrate the viability of understanding product-form cognition through multimodal implicit measurements, addressing issues such as the pronounced subjectivity inherent in traditional methods. Furthermore, this approach provides a pioneering technique for Kansei engineering research and offers a methodology for multimodal implicit measurements of product innovation design.

Emotion regulation in adolescents with major depression – Evidence from a combined EEG and eye-tracking study

BackgroundAdolescent major depression (MD) is characterized by deficits in emotion regulation (ER). Little is known about the neurophysiological correlates that are associated with these deficits. Moreover, the additional examination of visual attention during ER would allow a more in-depth understanding of ER deficits but has not yet been applied simultaneously. MethodsN = 33 adolescents with MD and n = 35 healthy controls (HCs) aged 12–18 years performed an ER task during which they either a) down-regulated their negative affective response to negative images via cognitive reappraisal or b) attended the images without changing their affective response. During the task, the Late Positive Potential (LPP), gaze fixations on emotional image aspects, and self-reported affective responses were collected simultaneously. ResultsCompared to HCs, adolescents with MD demonstrated reduced ER success based on self-report but did not differ in LPP amplitudes. Participants in both groups showed increased amplitudes in the middle LPP window when they reappraised negative pictures compared to when they attended them. Only in the HC group, increased LPP amplitudes during reappraisal were paralleled by more positive affective responses. LimitationThe applied stimuli were part of picture databases and might therefore have limited self-relevance. ConclusionsIncreased LPP amplitude during ER in both groups might be specific to adolescence and might suggest that ER at this age is challenging and requires a high amount of cognitive resources. These findings provide an important starting point for future interventional studies in youth MD.

Prediction of antidepressant responses to non-invasive brain stimulation using frontal electroencephalogram signals: Cross-dataset comparisons and validation

Background10-Hz repetitive transcranial magnetic stimulation(rTMS) and intermittent theta-burst stimulation(iTBS) over left prefrontal cortex are FDA-approved, effective options for treatment-resistant depression (TRD). Optimal prediction models for iTBS and rTMS remain elusive. Therefore, our primary objective was to compare prediction accuracy between classification by frontal theta activity alone and machine learning(ML) models by linear and non-linear frontal signals. The second objective was to study an optimal ML model for predicting responses to rTMS and iTBS. MethodsTwo rTMS and iTBS datasets (n = 163) were used: one randomized controlled trial dataset (RCTD; n = 96) and one outpatient dataset (OPD; n = 67). Frontal theta and non-linear EEG features that reflect trend, stability, and complexity were extracted. Pretreatment frontal EEG and ML algorithms, including classical support vector machine(SVM), random forest(RF), XGBoost, and CatBoost, were analyzed. Responses were defined as ≥50 % depression improvement after treatment. Response rates between those with and without pretreatment prediction in another independent outpatient cohort (n = 208) were compared. ResultsPrediction accuracy using combined EEG features by SVM was better than frontal theta by logistic regression. The accuracy for OPD patients significantly dropped using the RCTD-trained SVM model. Modern ML models, especially RF (rTMS = 83.3 %, iTBS = 88.9 %, p-value(ACC > NIR) < 0.05 for iTBS), performed significantly above chance and had higher accuracy than SVM using both selected features (p < 0.05, FDR corrected for multiple comparisons) or all EEG features. Response rates among those receiving prediction before treatment were significantly higher than those without prediction (p = 0.035). ConclusionThe first study combining linear and non-linear EEG features could accurately predict responses to left PFC iTBS. The bootstraps-based ML model (i.e., RF) had the best predictive accuracy for rTMS and iTBS.

Changes in the speed of visual processing between foveola and perifovea: a combined behavioral and EEG investigation

Changes in the speed of visual processing between foveola and perifovea: a combined behavioral and EEG investigation

Electrocortical activity correlated with locomotor adaptation during split-belt treadmill walking.

Locomotor adaptation is crucial for daily gait adjustments to changing environmental demands and obstacle avoidance. Mobile brain imaging with high-density electroencephalography (EEG) now permits quantification of electrocortical dynamics during human locomotion. To determine the brain areas involved in human locomotor adaptation, we recorded high-density EEG from healthy, young adults during split-belt treadmill walking. We incorporated a dual-electrode EEG system and neck electromyography to decrease motion and muscle artefacts. Voluntary movement preparation and execution have been linked to alpha (8-13Hz) and beta band (13-30Hz) desynchronizations in the sensorimotor and posterior parietal cortices, whereas theta band (4-7Hz) modulations in the anterior cingulate have been correlated with movement error monitoring. We hypothesized that relative to normal walking, split-belt walking would elicit: (1) decreases in alpha and beta band power in sensorimotor and posterior parietal cortices, reflecting enhanced motor flexibility; and (2) increases in theta band power in anterior cingulate cortex, reflecting instability and balance errors that will diminish with practice. We found electrocortical activity in multiple regions that was associated with stages of gait adaptation. Data indicated that sensorimotor and posterior parietal cortices had decreased alpha and beta band spectral power during early adaptation to split-belt treadmill walking that gradually returned to pre-adaptation levels by the end of the adaptation period. Our findings emphasize that multiple brain areas are involved in adjusting gait under changing environmental demands during human walking. Future studies could use these findings on healthy, young participants to identify dysfunctional supraspinal mechanisms that may be impairing gait adaptation. KEY POINTS: Identifying the location and time course of electrical changes in the brain correlating with gait adaptation increases our understanding of brain function and provides targets for brain stimulation interventions. Using high-density EEG in combination with 3D biomechanics, we found changes in neural oscillations localized near the sensorimotor, posterior parietal and cingulate cortices during split-belt treadmill adaptation. These findings suggest that multiple cortical mechanisms may be associated with locomotor adaptation, and their temporal dynamics can be quantified using mobile EEG. Results from this study can serve as a reference model to examine brain dynamics in individuals with movement disorders that cause gait asymmetry and reduced gait adaptation.

Emotion regulation training for adolescents with major depression: Evidence from an experimental randomized-controlled trial with combined EEG and eye-tracking

Emotion regulation training for adolescents with major depression: Evidence from an experimental randomized-controlled trial with combined EEG and eye-tracking

Emotion Recognition Using a Novel Granger Causality Quantifier and Combined Electrodes of EEG.

Electroencephalogram (EEG) connectivity patterns can reflect neural correlates of emotion. However, the necessity of evaluating bulky data for multi-channel measurements increases the computational cost of the EEG network. To date, several approaches have been presented to pick the optimal cerebral channels, mainly depending on available data. Consequently, the risk of low data stability and reliability has increased by reducing the number of channels. Alternatively, this study suggests an electrode combination approach in which the brain is divided into six areas. After extracting EEG frequency bands, an innovative Granger causality-based measure was introduced to quantify brain connectivity patterns. The feature was subsequently subjected to a classification module to recognize valence-arousal dimensional emotions. A Database for Emotion Analysis Using Physiological Signals (DEAP) was used as a benchmark database to evaluate the scheme. The experimental results revealed a maximum accuracy of 89.55%. Additionally, EEG-based connectivity in the beta-frequency band was able to effectively classify dimensional emotions. In sum, combined EEG electrodes can efficiently replicate 32-channel EEG information.

Pre-processed and combined EEG data for epileptic seizure classification using deep learning

Epilepsy is a neurological disease that affects nearly 60 million people around the world. Electroencephalography (EEG) and Machine Learning (ML) can be used to detect epileptic seizures, where ML algorithms depend on hand crafted features. Recently, deep learning (DL) methods became popular due to automatic feature learning. However, current DL based approaches mainly focuses on different DL architecture design, especially deeper DL networks. Only few works experiment modified EEG data as an input to DL models. In this work, we propose modified, pre-processed and combined EEG signals as an input to different DL models for epilepsy seizure classification rather than using raw EEG signals. A variety of pre-processed and combined EEG signals have been evaluated with three different DL architectures: A simple Deep Neural Network (DNN) model and a moderately complex 1D-Convolutional Neural Network (CNN) model and a complex 1D-CNN model. We perform several experiments for two-class and three-class epileptic seizure classification on UCI-Bonn dataset using the proposed EEG signals: (1)Original EEG signal of UCI-Bonn dataset, (2)standardized EEG signal, (3)original EEG signal combined with squared EEG signal, and then standardized, (4)original EEG signal combined with differentiated EEG signal, and then standardized, and (5)original EEG signal combined with magnitude of Fast Fourier Transform (FFT) of EEG signal, and then standardized (original + FFT). Extensive evaluations and comparisons show that the best results are achieved with original + FFT combination on all DL architectures. Using original + FFT EEG signal, competitive results can be achieved with simple and moderately complex DL models, by utilizing less computational resources.