Channel EEG Data Research Articles

Default at fault? Exploring neural correlates of default mode network in children with ADHD, their unaffected siblings versus neurotypical controls: A quantitative EEG study

BackgroundSustained activation of default mode network has been implicated for momentary lapses of attention and higher errors during performance of cognitive tasks in attention deficit hyperactive disorder (ADHD) children. Despite emerging evidence indicating the genetic basis of ADHD, there is paucity of literature investigating the alteration of DMN in children with ADHD and their unaffected siblings. AimTo study the cortical sources of DMN in children with ADHD compared to their siblings and neurotypical controls. MethodsEighty-six participants (35 ADHD (12.4(±2.7) years), 16 unaffected siblings (11.8(±4.3) years) and 35 matched neurotypical controls (12.6 (±3.6) years) participated in the study. 128 channel EEG data was acquired during rest and Stroop cognitive task and analyzed for cortical source estimation using LORETA software. ResultsHigher activation of DMN and DMN associated areas were observed during encoding of the color-word stimuli in children with ADHD. Sustained activation of core DMN areas namely medial frontal gyrus, posterior cingulate gyrus, parahippocampal gyrus and inferior parietal lobule was observed across all groups. Among the three groups, distinct cortical source activation differences were identified solely in the DMN and its associated areas among children with ADHD during the task encoding phase compared to baseline. In contrast, both siblings and neurotypical controls displayed activation in fronto-parieto-temporal areas subserving executive function were also observed. ConclusionSustained activity of DMN areas with minimal activity in executive network in ADHD children and unaffected siblings during encoding of stimulus implies potential endophenotypic marker in children with ADHD compared to neurotypical controls.

Development of Machine Learning Algorithms Using EEG Data to Detect the Presence of Chronic Pain.

Chronic pain impacts more than one in five adults in the United States (US) and the costs associated with the condition amount to hundreds of billions of dollars annually. Despite the tremendous impact of chronic pain in the US and worldwide, the standard of care for diagnosis depends on subjective self-reporting of pain state, with no effective objective assessment procedure available. This study investigated the application of signal processing and machine learning to electroencephalography (EEG) data for the development of classification algorithms capable of differentiating subjects in pain from pain free subjects. In this study, nineteen (19) channels of EEG data were obtained from subjects in an eyes closed resting state, and ultimately data from 186 participants were used for algorithm development, including 35 healthy controls and 151 chronic pain patients. Signal processing was applied to identify noise free segments of EEG data and 6375 quantitative EEG (qEEG) measures were calculated for each subject. Various machine learning methodologies were applied to the data, with Elastic Net chosen as the optimal methodology. The final classifier developed using Elastic Net contained 34 qEEG features with non-zero weights. The classifier was able to differentiate pain versus no pain subjects with an accuracy of 79.6%, sensitivity of 82.2%, and specificity of 66.7%. The features used in the classifier were evaluated and found to align well with contemporary literature regarding changes in neurological characteristics associated with chronic pain.

Achieving EEG-based depression recognition using Decentralized-Centralized structure

Currently, electroencephalography (EEG)-based depression recognition using deep learning methods has become an important approach for early diagnosis and treatment. Due to the functional differences and activation differences among various brain regions, the channels in EEG data may potentially exhibit a certain degree of local correlation. Furthermore, several studies have indicated that there are significant differences in the synchronization for multiple brain regions between patients with depression and healthy controls. However, few researchers have designed depression recognition model architectures according to the spatial distribution characteristics of EEG data. In multiple existing EEG-based classification models, the channels of EEG data are directly merged. This approach hinders the model from effectively capturing the spatial information of EEG data and leads to a partial loss of synchronized information of several brain regions. In light of this, we propose a novel attention network named Decentralized-Centralized Structure Transformer (DCST), which can achieve effective depression recognition utilizing the spatial distribution characteristics of EEG data. The leave-one-subject-out cross-validation is employed to evaluate the classification performance for all models. The experimental results show that our proposed model achieves the highest classification accuracy, 89.8%.

Artificial Intelligence Model‐based Alzheimer’s Disease Dementia Classification Using Electroencephalogram‐acquired Brain Functional Network

AbstractBackgroundAlzheimer’s disease dementia (ADD) is a predominant neurodegenerative disorder that results severe abnormalities in cognitive functions. However, most diagnoses rely on assessments made by individual clinical experts.Hence, we aim to establish a novel biomarker which can provide both accuracy and objectivity. Electroencephalogram (EEG) data were utilized for the training of artificial intelligence (AI) models, making use of its cost‐advantage and simple utility.Method19‐channel EEG data were recorded at Chung‐Ang University hospital in accordance with international 10‐20 system at resting state with eyes being closed. We first converted 19 channels into 68 regions of interest (ROIs) using standardized low resolution brain electromagnetic tomography (sLORETA). Henceforth, imaginary part of coherence (iCoh) between each ROI were calculated, reflecting functional connectivity between the ROIs. The iCoh values were then utilized in the calculation of the brain’s functional brain network.The subjects were divided into non‐ADD and ADD group. The resulting training dataset consisted of 118 non‐ADD and 110 ADD subjects, where the number of non‐ADD training data were adjusted in order to account for data imbalance. The test dataset included 476 non‐ADD and 27 ADD subjects.The classification model was trained using 68 ROI source powers and functional brain network features. Only statistically significant features were selected for feature reduction purposes. Through primary classification via light gradient boosting method (LGBM), top 30 features with high importance values were selected and were employed in the training of our final LGBM classification model.ResultThrough appropriate hyperparameter tuning, the LGBM classification model resulted in 87.67% accuracy, 88.89% sensitivity and 87.61% specificity. From the receiver operating characteristic (ROC) curve, 0.9506 area under ROC curve (AUC) was achieved.ConclusionThe LGBM model resulted in promising classification performance, solely through EEG features. In addition, functional brain network features had higher importance in comparison with source power features, indicating high potential of EEG brain network features in distinguishment of neurodegenerative diseases.

QEEG‐based TabNet classifier for dementia pathologies: Alzheiemer’s disease and Lewy body disease

AbstractBackgroundEarly screening and prevention of dementia are widely discussed and studied in the current healthcare paradigm. However, about 70 percent of dementia diagnoses are categorized into Alzheimer’s disease (AD), neglecting the presence and influence of Lewy body. The present study aims to distinguish Alzheimer’s disease dementia (pure AD) and Lewy body dementia (pure LBD) through quantitative electroencephalogram (QEEG)‐based classification algorithm. The mixed dementia that exhibits both AD and LBD related propensities were further tested for the verification of the established classification model.Method19 channel EEG data were recorded in accordance with the 10‐20 system in eyes‐closed resting state. The acquired data were bandpass filtered into the range of interest (1‐45Hz) and re‐referenced into common average reference. Artifacts were removed by bad epoch rejection and independent component analysis (ICA) through an automated, cloud‐based QEEG analysis platform iSyncBrain®. Henceforth, sensor‐level features were extracted and computed for the training of TabNet structure. Due to the data imbalance (N = 146, Pure AD = 48, Pure LBD = 102), time window‐based augmentation has been applied to establish the final dataset (N = 7292, Pure AD = 3376, Pure LBD = 3916), which were then split into 8 to 2 ratio for the training and testing of the classification model. In addition, mixed dementia (N = 136) data were used to further validate the model’s performance.ResultThe best classification model yielded test accuracy of 80% with pure AD sensitivity at 80% and pure LBD sensitivity 80%. Moreover, the predicted probability distribution for the mixed dementia data were prominently concentrated around 0.5, which reflect that both pure AD and LBD properties exist.ConclusionAlong with the promising classification performance of the established classification model, the centered probability distribution of the mixed dementia data reflect that the model can well distinguish the characteristics that are specific to pure AD and LBD.

Anesthetic Drugs as a Pharmacologic Stress Test for the Aging Brain: Towards a Real Time EEG Read‐Out

AbstractBackgroundRoughly 65 million older adults (ie age >65 years) undergo anesthesia/surgery each year and are at risk for postoperative delirium. Further, postoperative delirium is itself associated with increased long term risk for Alzheimer’s Disease and Related Dementias (ADRD). Recent work has also shown that some patients have deeply suppressed neurophysiologic (ie EEG) patterns in response to relatively low anesthetic drug doses, and that these patients are more likely to have preoperative cognitive impairment and to develop postoperative delirium. However, to date few if any studies have examined the role of specific ADRD pathologic processes and neuroimaging abnormalities in these altered anesthetic dose‐dependent EEG response patterns in older adults.MethodWe examined relationships between intraoperative 32 channel EEG data (in cognitively normal older non‐cardiac surgery patients who received inhaled anesthetics such as isoflurane) and both 1) preoperative CSF ADRD biomarker levels and 2) preoperative structural and functional MRI imaging characteristics. CSF abeta, tau and p‐tau‐181p were measured using the Roche elecsys platform. Frontal‐parietal alpha band phase lag index and alpha power were calculated from frontal and parietal electrode regions of interest (ROIs), and normalized to inhaled anesthetic dosage.ResultLower anesthetic dose‐adjusted frontal‐parietal alpha band phase lag index values were associated with higher preoperative CSF p‐tau/Ab ratios (p = 0.013, N = 9‐18 per group, Fig 1A), lower cortical thickness in the left and right parahippocampal regions (Fig 1B, N = 16, p<0.001), and altered resting state fMRI connectivity between the left anterior entorhinal cortex and both the left medial prefrontal cortex and the left angular gyrus (two key default mode network regions; Fig 1C, N = 16, peak voxel p<0.01).ConclusionThese data suggest that both molecular markers and neuroimaging features of preclinical AD neuropathology in cognitively normal older adults are associated with altered anesthetic dose‐dependent changes in neurophysiologic/EEG functional connectivity. More generally, these data demonstrate the need for additional studies to understand the extent to which intraoperative EEG data can be used to make inferences about ADRD‐related neuropathologic processes in older adults, which could provide a real‐time EEG “readout” for the stress test of anesthesia and surgery on the aging brain

Alzheimer’s Disease Dementia Classification Through Residual Network and QEEG Image representations

AbstractBackgroundOur everyday actions and decisions involve a complex coordination of various cognitive functions. Severe deterioration of such functions, commonly referred to as dementia, adversely affects quality of our lives. However, through early screening of the disease at its pre‐clinical stage and appropriate treatment, they can be reverted. Current diagnostic criteria for dementia utilize questionnaire‐based cognitive function assessments, such as the mini‐mental state examination. However, it is statistically less robust, where further tests such as positron emission tomography are required for accurate diagnosis. This is laborious, expensive and expose patients to harmful radiation. Therefore, we propose a quantitative electroencephalography (QEEG) based approach that distinguishes Alzheimer’s disease dementia (ADD) from a non‐ADD group that include mild cognitive impairment and subjective cognitive decline data.Method19 channel EEG data employed in the present study were clinically acquired from Chung‐Ang university hospital, in eyes‐closed resting state. Artefacts were eliminated through independent component analysis and bad epoch rejection. Through spectral analysis, the powers of a frequency spectrum (1‐45Hz) were computed with 0.25Hz resolution. Henceforth, the Z‐score of each value were calculated through iMediSync’s age‐ and sex‐differentiated normative database and were mapped into a rectangular arrangement in accordance with channel locations on the scalp. The rearranged Z‐score matrices were visualized, clearly exhibiting both spectral and spatial information. Gamma spectrum (30‐45Hz) was excluded due to its vulnerability to contaminations induced by external noises and muscle movements. The final dataset (N = 732; 137 ADD, 595 non‐ADD) was established, where the 75% of non‐ADD were first excluded as test data due to data imbalance. The remaining data were split into 2 to 8 ratio for testing (N = 503; 27 ADD; 476 non‐ADD) and training (N = 229; 110 ADD; 119 non‐ADD).ResultThe final 18‐layer Residual network model showed test accuracy at 88.5% with ADD sensitivity at 88.9% and specificity at 88.4%. Explainable artificial intelligence algorithm was utilized for the inference and verification of classification criteria.ConclusionThe outstanding classification results of the established model upholds QEEG utility in the distinguishment of dementia from its pre‐clinical stages. Continuous refinement will bolster its potential in the diagnosis several other neurological diseases.

An EEG Channel Selection Framework for Driver Drowsiness Detection via Interpretability Guidance.

Drowsy driving has a crucial influence on driving safety, creating an urgent demand for driver drowsiness detection. Electroencephalogram (EEG) signal can accurately reflect the mental fatigue state and thus has been widely studied in drowsiness monitoring. However, the raw EEG data is inherently noisy and redundant, which is neglected by existing works that just use single-channel EEG data or full-head channel EEG data for model training, resulting in limited performance of driver drowsiness detection. In this paper, we are the first to propose an Interpretability-guided Channel Selection (ICS) framework for the driver drowsiness detection task. Specifically, we design a two-stage training strategy to progressively select the key contributing channels with the guidance of interpretability. We first train a teacher network in the first stage using full-head channel EEG data. Then we apply the class activation mapping (CAM) to the trained teacher model to highlight the high-contributing EEG channels and further propose a channel voting scheme to select the top N contributing EEG channels. Finally, we train a student network with the selected channels of EEG data in the second stage for driver drowsiness detection. Experiments are designed on a public dataset, and the results demonstrate that our method is highly applicable and can significantly improve the performance of cross-subject driver drowsiness detection.

Spatiotemporal phase slip patterns for visual evoked potentials, covert object naming tasks, and insight moments extracted from 256 channel EEG recordings.

Phase slips arise from state transitions of the coordinated activity of cortical neurons which can be extracted from the EEG data. The phase slip rates (PSRs) were studied from the high-density (256 channel) EEG data, sampled at 16.384 kHz, of five adult subjects during covert visual object naming tasks. Artifact-free data from 29 trials were averaged for each subject. The analysis was performed to look for phase slips in the theta (4-7 Hz), alpha (7-12 Hz), beta (12-30 Hz), and low gamma (30-49 Hz) bands. The phase was calculated with the Hilbert transform, then unwrapped and detrended to look for phase slip rates in a 1.0 ms wide stepping window with a step size of 0.06 ms. The spatiotemporal plots of the PSRs were made by using a montage layout of 256 equidistant electrode positions. The spatiotemporal profiles of EEG and PSRs during the stimulus and the first second of the post-stimulus period were examined in detail to study the visual evoked potentials and different stages of visual object recognition in the visual, language, and memory areas. It was found that the activity areas of PSRs were different as compared with EEG activity areas during the stimulus and post-stimulus periods. Different stages of the insight moments during the covert object naming tasks were examined from PSRs and it was found to be about 512 ± 21 ms for the 'Eureka' moment. Overall, these results indicate that information about the cortical phase transitions can be derived from the measured EEG data and can be used in a complementary fashion to study the cognitive behavior of the brain.

Cortical tracking of speech in quiet and noise in infants and children

Past research has shown that in multi-talker listening situations, cortical tracking of the speech envelope is observed for the target speech stream in adults. While children are known to perform worse than adults under noisy conditions, many aspects of how the cortical processing of speech develops is unknown. Here, we recorded EEG responses to continuous speech in infants and 7-to-18-year-olds in Quiet, Co-located Noise, and Segregated Noise. The target speech stream consisted of infant directed speech or an audiobook for children. The noise consisted of four-talker babble and was constructed from four audiobooks read by 2 males and 2 females. Thirty-two channels of EEG data were recorded with the stimuli presented at an overall level of 65 dB SPL for children and 70 dB SPL for infants with a +5 dB target-to-noise SNR via speakers at 0°, +90°, and −90° azimuth. EEG signals were analyzed using the decoding model of the Multivariate Temporal Response Function toolbox to assess how well the stimulus envelope could be reconstructed from the recorded neural responses. Preliminary results show evidence of cortical tracking of the speech envelope for both infants and children in quiet and that cortical tracking degrades in both Co-located and Segregated Noise.

Computer Aided Detection of Major Depressive Disorder (MDD) Using Electroencephalogram Signals

MDD or depression is psychiatric disorder which affects many of people globally. Due to the nonexistence of any available laboratory tests, poor identification of depression is a key contributing factor. In this study, a framework is developed in order detect depression with minimal number of channels to increase the portability as well as with high accuracy. In this study, two dataset has been considered i.e. Public and Private dataset. Different regions of brain as well as feature has been selected to find out the minimal number of channels and features so that higher accuracy is also achieved. According to the study, depression has different impacts on each hemisphere of the temporal and parietal regions of the brain. Depression also has an impact on Detrended Fluctuation Analysis. The study results in a model made up of 4 channels with high accuracy of 91.74% which is portable, faster and cheaper. Thus, model can act as an assistive tool for diagnosis of depression. The study confirms that depression affects the temporal area of the brain since using the same classifiers in both the public and private datasets and utilizing only temporal channel EEG data provided quite high accuracy in detection of MDD. The fact that accuracy of 89.24%(Public Dataset using only 2 channels i.e T7 and T8) and 90.26%(Private Dataset using only 6 channels FT7, FT8, T7, T8, TP7, TP8 ) supports the claim that the temporal lobe of the brain is impacted by depression.

Channel Reduction for an EEG-Based Authentication System While Performing Motor Movements

Commercial use of biometric authentication is becoming increasingly popular, which has sparked the development of EEG-based authentication. To stimulate the brain and capture characteristic brain signals, these systems generally require the user to perform specific activities such as deeply concentrating on an image, mental activity, visual counting, etc. This study investigates whether effective authentication would be feasible for users tasked with a minimal daily activity such as lifting a tiny object. With this novel protocol, the minimum number of EEG electrodes (channels) with the highest performance (ranked) was identified to improve user comfort and acceptance over traditional 32-64 electrode-based EEG systems while also reducing the load of real-time data processing. For this proof of concept, a public dataset was employed, which contains 32 channels of EEG data from 12 participants performing a motor task without intent for authentication. The data was filtered into five frequency bands, and 12 different features were extracted to train a random forest-based machine learning model. All channels were ranked according to Gini Impurity. It was found that only 14 channels are required to perform authentication when EEG data is filtered into the Gamma sub-band within a 1% accuracy of using 32-channels. This analysis will allow (a) the design of a custom headset with 14 electrodes clustered over the frontal and occipital lobe of the brain, (b) a reduction in data collection difficulty while performing authentication, (c) minimizing dataset size to allow real-time authentication while maintaining reasonable performance, and (d) an API for use in ranking authentication performance in different headsets and tasks.

Multi-Channel EEG Emotion Recognition Based on Parallel Transformer and 3D-Convolutional Neural Network

Due to its covert and real-time properties, electroencephalography (EEG) has long been the medium of choice for emotion identification research. Currently, EEG-based emotion recognition focuses on exploiting temporal, spatial, and spatiotemporal EEG data for emotion recognition. Due to the lack of consideration of both spatial and temporal aspects of EEG data, the accuracy of EEG emotion detection algorithms employing solely spatial or temporal variables is low. In addition, approaches that use spatiotemporal properties of EEG for emotion recognition take temporal and spatial characteristics of EEG into account; however, these methods extract temporal and spatial information directly from EEG data. Since there is no reconstruction of the EEG data format, the temporal and spatial properties of the EEG data cannot be extracted efficiently. To address the aforementioned issues, this research proposes a multi-channel EEG emotion identification model based on the parallel transformer and three-dimensional convolutional neural networks (3D-CNN). First, parallel channel EEG data and position reconstruction EEG sequence data are created separately. The temporal and spatial characteristics of EEG are then retrieved using transformer and 3D-CNN models. Finally, the features of the two parallel modules are combined to form the final features for emotion recognition. On the DEAP, Dreamer, and SEED databases, the technique achieved greater accuracy in emotion recognition than other methods. It demonstrates the efficiency of the strategy described in this paper.

Automated CCA-MWF Algorithm for Unsupervised Identification and Removal of EOG Artifacts From EEG.

Affective brain computer interface (ABCI) enables machines to perceive, understand, express and respond to people's emotions. Therefore, it is expected to play an important role in emotional care and mental disorder detection. EEG signals are most frequently adopted as the physiology measurement in ABCI applications. Eye blinking and movements introduce lots of artifacts into raw EEG data, which seriously affect the quality of EEG signal and the subsequent emotional EEG feature engineering and recognition. In this paper, we propose a fully automatic and unsupervised ocular artifact identification and removal algorithm named automated canonical correlation analysis (CCA)-multi-channel wiener filter (MWF) (ACCAMWF). Firstly, spatial distribution entropy (SDE) and spectral entropy (SE) are computed to automatically annotate artifact segments. Then, CCA algorithm is used to extract neural signal from artifact contaminated data to further supplement the clean EEG data. Finally, MWF is trained to remove ocular artifacts from multiple channel EEG data adaptively. Extensive experiments have been carried out on semi-simulated EEG/EOG dataset and real eye blinking-contaminated EEG dataset to verify the effectiveness of our method when compared to two state-of-the-art algorithms. The results clearly demonstrate that ACCAMWF is a promising solution for removing EOG artifacts from emotional EEG data.

Neonatal EEG seizure detection using a new signal structural complexity measure based on matching pursuit decomposition with nonstationary dictionary

Background and objectiveIn newborns, it is often difficult to accurately differentiate between seizure and non-seizure based solely on clinical manifestations. This highlights the importance of electroencephalogram (EEG) in the recognition and management of neonatal seizures. This paper proposes an effective algorithm for the detection of neonatal seizure using multichannel EEG. MethodsNeonatal EEG changes morphology as it alternates between seizure and non-seizure states. A new signal complexity measure based on matching pursuit (MP) decomposition is proposed and used to detect transitions between these two states. The new measure, referred to as weighted structural complexity (WSC), was used for the detection of seizures in 30 newborn EEG records. Multiple IIR filters and an MP-based filter were designed and used to remove artifacts from the EEG data. Geometrical correlation between the EEG data channels was applied to reduce the number of false detections caused by remnant artifacts. The seizure detector's performance was assessed using several epoch-based (e.g., accuracy) and event-based (GDR = good detection rate and FD/h = false detections per hour) metrics. ResultsCompared to the neurologist marking, the proposed detector was able to detect EEG seizures with 94% accuracy, 90.9% GDR, and 0.14 FD/h (95% CI: [0.06, 0.34]). ConclusionsThe high performance of the MP-based detector may have significant implications for the accurate diagnosis of neonatal seizures and the appropriate use of anticonvulsants and ongoing clinical assessment and care of the newborn.

Minimal EEG channel selection for depression detection with connectivity features during sleep

Background and objectivesSleeping cortical electroencephalogram (EEG) has the potential for depression detection, for different sleep structure and cortical connection have been proved in depressed patients. However, the operation of multi-channel sleep EEG recording is cumbersome and requires laboratory equipment and professional sleep technician. Here, we focus on the depression detection using minimal sleep EEG channels. MethodsSixteen channels of EEG data of 30 patients with depression and 30 age-matched normal controls were recorded during sleep. Power spectral density of each single EEG channel was calculated, followed by measuring the symbolic transfer entropy (STE) and weighed phase lag index (WPLI) between EEG channel pairs in various frequency bands. Thereafter, these features were evaluated by F-score in the two-way classification (depression vs. control) of 30-s sleep EEG segments. Based on the F-score, entropy method was introduced to calculate the weight which could further assess the classification ability of various EEG channels or channel pairs. Finally, machine learning was implemented to verify the important EEG channels or channel pairs in depression diagnosis. ResultsThe features characterizing the inter-hemispheric connectivity in the posterior lobe, especially in the temporal lobe, showed high classification capacity. The classification accuracy of using two and four EEG channels in the temporal lobe were 97.96% and 99.61%, respectively. ConclusionsThis study showed the possibility of using only a few sleep EEG channels for depression screening, which may greatly facilitate the diagnosis of depression outside the hospital.

A Study of Response Inhibition in Overweight/Obesity People Based on Event-Related Potential.

ObjectiveTo investigate the characteristics of response inhibition of overweight/obese people, using behavior experiments combine with neural electrophysiological technology and discussing the difference in impulse level between obesity/overweight and normal-weight people through EEG data, questionnaire, and behavior experiment.Method(1) All participants completed the Go/Nogo task; meanwhile, behavior data and 64 channel EEG data were recorded. (2) Participants completed the Stop-Signal task and behavior date was recorded.Results(1) During Go/Nogo task, no significant differences were found in reaction time, omission errors of the Go task between the two groups, while commission errors of the Nogo task of the control group were significantly greater than the overweight/obesity group. (2) About SSRT during the Stop-Signal Task, the interaction of stimulus type (high-calorie food picture, low-calorie food picture) and group (control group, overweight/obesity group) was significant (p = 0.008). (3) No significant differences were found between the two groups in amplitude and latency of N2. About the amplitude of P3, the interaction of task type (Go task, Nogo task), electrode point (Cz, CPz, Pz), and groups were significant (p = 0.041), the control group P3 amplitude was significantly greater than overweight/obesity group during the Nogo task. Regarding about latency of P3, the interaction of group and electrode point were not significant (p = 0.582), but the main effect of task type was significant (p = 0.002).Conclusion(1) In terms of behavioral outcomes, overweight–obese subjects had lower dominant response inhibition and response cessation compared to normal-weight subjects. (2) In terms of EEG results, overweight–obese subjects showed no difference in processing speed and level of conflict monitoring for early inhibitory processing compared to normal-weight subjects, but there was a deficit in behavioral control for late inhibitory processing.

A Convolutional Long Short-Term Memory-Based Neural Network for Epilepsy Detection From EEG

Epilepsy is a severe neurological disorder characterized by recurrent seizures, which increases the risk of death three times more than normal. Currently Electroencephalography (EEG) has emerged as a highly promising technique for the diagnosis of epilepsy. The majority of current EEG-based epilepsy detection research have employed a variety of deep learning-based models, but most of the approaches suffer from poor generalizability, optimal design and performance rates. To address these issues, this study aims to develop an efficient framework based on deep spatio-temporal neural network called convolutional long short-term memory (ConvLSTM) for epilepsy detection from EEG signals. In the proposed model, firstly standard 19 channel EEG data are selected and resampled at 256Hz, and then those signals are segmented into three-second time frames. Afterward, the segmented data are fed as input to the ConvLSTM model for identifying epileptic patients from normal subjects. To generalize the proposed model, we have tested it on two different datasets with varying population sizes. We have used the five-fold cross validation and leave-one-out cross validation schemes to eliminate the experiment’s biases. To further validate the proposed framework, we have carried out various ablation studies. The experimental results demonstrate that the proposed model outperforms the current state-of-the-art results for the studied datasets, making it suitable for use as an automated system for the diagnosis of epilepsy.

Using EEG to detect driving fatigue based on common spatial pattern and support vector machine

To investigate the correlation between electroencephalogram (EEG) and driving fatigue states, this study used machine learning algorithms to detect driving fatigue based on EEG. 14 channels of EEG data were collected from thirty-four healthy subjects in this research at Northeastern University. Each subject participated in two scenarios (baseline and fatigue scenarios). Subjective ratings of fatigue levels were also obtained from the subjects using the NASA-Task Load Index (TLX). The common spatial pattern (CSP) algorithm was used to extract features from the raw EEG data. The support vector machine (SVM) was used as the classifier in the design of the machine learning algorithm. A grid search cross validation was exploited to find optimal hyperparameter settings. The best classification result was 90%, obtained by using all 14 EEG channels and linear kernel of SVM. The experimental results proved that a machine learning algorithm was able to reliably classify driving fatigue states using EEG data. This study demonstrated that CSP and SVM were promising in detecting driving fatigue, and therefore, they could be strong foundations for future efforts to reduce traffic accidents and save thousands of human lives.

Revisiting ICEEMDAN and EEG rhythms

A new procedure is proposed to choose the optimum noise amplitude in the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) method. This new procedure is referred to as the Performance Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (PICEEMDAN). The intrinsic mode functions (IMF) obtained using ICEEMDAN depend on the chosen noise amplitude. Depending on the noise amplitude, the number of IMF’s can vary and also its properties. The paper explores the best choice of the noise amplitude that allows minimal mode mixing amongst its components. This involves the use of the marginal Hilbert spectrum, to compute the spectral energy over the mode’s bandwidth for each IMF and a parameter defined as the total index of overlap in the frequency domain (TIOF). The procedure is demonstrated initially using synthetic signals and later applied to electroencephalogram (EEG) signals. The results indicate a marked reduction in mode mixing amongst in components. The IMF’s obtained from single channel EEG data using this technique showed remarkable correspondence with the beta, alpha, theta and delta EEG rhythms. This was demonstrated using eyes open, eyes closed and seizure data and provides an alternate methodology to process EEG data.