Analyzing neural activity under prolonged mask usage through EEG.

In recent COVID times, mask has been a compulsion at workplaces and institutes as a preventive measure against multiple viral diseases including coronavirus (COVID-19) disease. However, the effects of prolonged mask-wearing on humans' neural activity are not well known. This paper is to investigate the effect of prolonged mask usage on the human brain through electroencephalogram (EEG), which acquires neural activity and translates it into comprehensible electrical signals. The performances of 10 human subjects with and without mask were assessed on a random patterned alphabet game. Besides EEG, physiological parameters of oxygen saturation, heart rate, blood pressure, and body temperature were recorded. Spectral and statistical analysis were performed on the recorded entities along with linear discriminant analysis (LDA) on extracted spectral features. The mean EEG spectral power in alpha, beta, and gamma sub-bands of the subjects with mask was smaller than the subjects without mask. The performances on the task and the oxygen saturation level between the two groups differed significantly (p < 0.05). Whereas, the blood pressure, body temperature, and heart rate of both groups were similar. Based on the LDA analysis, the occipital and frontal lobes exhibited the greatest variability in channel measurements, with O1 and O2 channels in the occipital lobe demonstrating significant variations within the alpha band due to visual focus, while the F3, AF3, and F7 channels were found to be differentiating within the beta and gamma frequency bands due to the cognitive stimulating tasks. All other channels were observed to be non-discriminatory.

Classification of alcoholic EEG signals using wavelet scattering transform-based features.

Following the research question and the relevant dataset, feature extraction is the most important component of machine learning and data science pipelines. The wavelet scattering transform (WST) is a recently developed knowledge-based feature extraction technique and is structurally like a convolutional neural network (CNN). It preserves information in high-frequency, is insensitive to signal deformations, and generates low variance features of real-valued signals generally required in classification tasks. With data from a publicly-available UCI database, we investigated the ability of WST-based features extracted from multichannel electroencephalogram (EEG) signals to discriminate 1.0-s EEG records of 20 male subjects with alcoholism and 20 male healthy subjects. Using record-wise 10-fold cross-validation, we found that WST-based features, inputted to a support vector machine (SVM) classifier, were able to correctly classify all alcoholic and normal EEG records. Similar performances were achieved with 1D CNN. In contrast, the highest independent-subject-wise mean 10-fold cross-validation performance was achieved with WST-based features fed to a linear discriminant (LDA) classifier. The results achieved with two 10-fold cross-validation approaches suggest that the WST together with a conventional classifier is an alternative to CNN for classification of alcoholic and normal EEGs. WST-based features from occipital and parietal regions were the most informative at discriminating between alcoholic and normal EEG records.

Detection and Prediction of Microsleeps from EEG using Spatio-Temporal Patterns.

A microsleep is a brief lapse in performance due to an involuntary sleep-related loss of consciousness. These episodes are of particular importance in occupations requiring extended unimpaired visuomotor performance, such as driving. Detection and even prediction of microsleeps has the potential to prevent catastrophic events and fatal accidents. In this study, we examined detection and prediction of microsleeps using EEG data of 8 subjects who performed two 1-h sessions of continuous 1-D tracking. A regularized spatio-temporal filtering and classification (RSTFC) method was used to extract features from 5-s EEG segments. These features were then used to train three different linear classifiers: linear discriminant analysis (LDA), sparse Bayesian learning (SBL), and variational Bayesian logistic regression (VBLR). The performance of microsleep state detection and prediction was evaluated using leave-one-subject-out cross-validation. The detection performance measures were AUCROC 0.96, AUCPR 0.52, and phi 0.47. As expected, prediction of microsleep states with a 0.25-s ahead prediction time resulted in slightly lower performances compared to the detection. Prediction performance measures were substantially higher than those achieved with log-power spectral features, i.e., AUCROC 0.95 (cf. 0.90), AUCPR 0.50 (cf. 0.36), and phi 0.46 (cf. 0.34).

Ensemble learning based on overlapping clusters of subjects to predict microsleep states from EEG.

Microsleeps are brief and involuntary instances of complete loss of sleep-related consciousness. We present a novel approach of creating overlapping clusters of subjects and training of an ensemble classifier to enhance the prediction of microsleep states from EEG. Overlapping clusters are created using Kullback-Leibler divergence between responsive state features of each pair of training subjects. Highly correlated features within each overlapping cluster are discarded. The remaining features are selected via Fisher score based ranking followed by an average of 5-fold cross-validation areas under the curves of receiver operating characteristics (AUCRoc) of a linear discriminant analysis (LDA) classifier. The decisions of LDA classifiers on overlapping clusters are fused using weighted average. We evaluated this new approach on 16-channel EEG data from 8 subjects who had performed a 1-D visuomotor task for two l-h sessions. Joint entropy features were extracted from a 5-s window of EEG with steps of 0.25 s Test performances were evaluated using leave-one-subject-out cross-validation. Our ensemble of overlapping clusters of subjects achieved a mean prediction performance, phi, of 0.42 compared with 0.39 for a single LDA classifier and 0.37 for generalized stacking.

Predicting Microsleep States Using EEG Inter-Channel Relationships.

A microsleep is a brief and an involuntary sleep-related loss of consciousness of up to 15 s. We investigated the performances of seven pairwise inter-channel relationships-covariance, Pearson's correlation coefficient, wavelet cross-spectral power, wavelet coherence, joint entropy, mutual information, and phase synchronization index-in continuous prediction of microsleep states from EEG. These relationships were used as the feature sets of a linear discriminant analysis (LDA) and a linear support vector machine classifiers. Priors for both classifiers were incorporated to address the class imbalance in the training data sets. Each feature set was extracted from a 5-s window of EEG with the step of 0.25 s and was demeaned with respect to the mean of first 2 min. The sequential forward selection (SFS) method, based on a serial combination of the correlation coefficient, Fisher score-based filter, and an LDA-based wrapper, was used to select features from each training set. The comparison was based on 16-channel EEG data from eight subjects who had performed a 1-D visuomotor task for two 1-h sessions. The prediction performances were evaluated using leave-one-subject-out cross-validation. For both classifiers, non-normalized feature sets were found to perform better than normalized feature sets. Furthermore, demeaning the non-normalized features considerably improved the prediction performance. Overall, the LDA classifier with joint entropy features resulted in the best average prediction performances (phi, AUCPR, and AUCROC) of (0.47, 0.50, and 0.95). Joint entropy between O1 and O2 from theta frequency band was the most informative feature.