Depression diagnosis and management using EEG-based affective brain mapping in real time

Development of affective Brain-Computer Interfaces (BCIs) via Electroencephalogram (EEG) has emerged as a cynosure of research in early diagnosis and effective management of depression. However, conventional BCIs are still lacking in terms of high computational complexity, less accuracy due to Fourier phase suppression and lack of substantial conclusion for depression diagnosis. An automated, EEG-based depression diagnostic and management tool is proposed to overcome these limitations. Channel eventrelated potentials, cross-coherence and power spectra plots in MATLAB are quantified and studied as an outcome to map real-time, emotion-specific multichannel EEG data set into distinct emotional states. A fast and stable fourth-order statistics-based independent component analysis is incorporated to reject temporal/spatial artefacts. Increases in frontal alpha (8-13 Hz) and delta (0.5-4 Hz) power/coherence are during depressed and normal/relaxed states, respectively. Devotional music (relaxed state) is found to facilitate depression elimination. Results are found to be statistically significant across all subjects with minimal p-values. Hence, it has been inferred that the proposed model has the potential to aid early and accurate depression diagnostic and management process.