EEG-based brain-computer interface using wavelet coherence and graph-theoretic methods

Emotion and cognition, two fundamental aspects of human mental processes, have traditionally been investigated independently. A comprehensive understanding of human mental functioning requires exploration of emotion and cognition. Among various physiological methods, electroencephalography (EEG), stands out as a reliable, non-invasive tool to evaluate emotional and cognitive processes. Studying neural oscillations across various EEG frequency bands and evaluating event-related potentials (ERPs) helps identifying distinct patterns linked to various emotional and cognitive states. In this thesis, pleasant/unpleasant emotional moods as well as low/high cognitive states were discriminated using graph-theoretic features extracted from spatio-temporal components. Wavelet coherence values corresponding to the N100, N200, and P300 single-trial ERP components were examined across the delta, theta, alpha, and beta frequency bands. Subsequently, graph-theoretic analyses were performed on connectivity matrices constructed from wavelet coherence-based functional networks to obtain several global and local graph metrics. These metrics were employed as features in classification using support vector machines (SVM), k-nearest neighbors (k-NN), and linear discriminant analysis (LDA) to enhance the classification accuracy by leveraging graph-theoretic features. Delta band wavelet coherence based graph features enabled accurate classification using SVM. Pleasant/unpleasant emotional moods and low/high cognitive states were classified with average accuracies of up to 92% and 89%, respectively. These findings emphasize the robustness of the proposed approach, highlighting its potential to improve EEG-based assessment of emotional and cognitive states, while paving the way for future brain–computer interface (BCI) applications.