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Feature extraction is an important step in the process of electroencephalogram (EEG) signal classification. The authors propose a “pattern recognition” approach that discriminates EEG signals recorded during different cognitive conditions. Wavelet based feature extraction such as, multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were computed. Extracted relative wavelet energy features were normalized to zero mean and unit variance and then optimized using Fisher's discriminant ratio (FDR) and principal component analysis (PCA). A high density EEG dataset validated the proposed method (128-channels) by identifying two classifications: (1) EEG signals recorded during complex cognitive tasks using Raven's Advance Progressive Metric (RAPM) test; (2) EEG signals recorded during a baseline task (eyes open). Classifiers such as, K-nearest neighbors (KNN), Support Vector Machine (SVM), Multi-layer Perceptron (MLP), and Naïve Bayes (NB) were then employed. Outcomes yielded 99.11% accuracy via SVM classifier for coefficient approximations (A5) of low frequencies ranging from 0 to 3.90 Hz. Accuracy rates for detailed coefficients were 98.57 and 98.39% for SVM and KNN, respectively; and for detailed coefficients (D5) deriving from the sub-band range (3.90–7.81 Hz). Accuracy rates for MLP and NB classifiers were comparable at 97.11–89.63% and 91.60–81.07% for A5 and D5 coefficients, respectively. In addition, the proposed approach was also applied on public dataset for classification of two cognitive tasks and achieved comparable classification results, i.e., 93.33% accuracy with KNN. The proposed scheme yielded significantly higher classification performances using machine learning classifiers compared to extant quantitative feature extraction. These results suggest the proposed feature extraction method reliably classifies EEG signals recorded during cognitive tasks with a higher degree of accuracy.

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Complaints of stress are common in modern life. Psychological stress is a major cause of lifestyle-related issues, contributing to poor quality of life. Chronic stress impedes brain function, causing impairment of many executive functions, including working memory, decision making and attentional control. The current study sought to describe newly developed stress mitigation techniques, and their influence on autonomic and endocrine functions. The literature search revealed that the most frequently studied technique for stress mitigation was biofeedback (BFB). However, evidence suggests that neurofeedback (NFB) and noninvasive brain stimulation (NIBS) could potentially provide appropriate approaches. We found that recent studies of BFB methods have typically used measures of heart rate variability, respiration and skin conductance. In contrast, studies of NFB methods have typically utilized neurocomputation techniques employing electroencephalography, functional magnetic resonance imaging and near infrared spectroscopy. NIBS studies have typically utilized transcranial direct current stimulation methods. Mitigation of stress is a challenging but important research target for improving quality of life.

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