JOINT APPROXIMATE DIAGONALIZATION DIVERGENCE BASED SCHEME FOR EEG DROWSINESS DETECTION BRAIN COMPUTER INTERFACES

Neurons usually converse through electrochemical signals and pooled neuronal firings feasibly be recorded on the scalp through the medium of electroencephalogram (EEG). EEG waveforms are recorded, analysed and categorized across directives concerning a Brain-Computer Interface (BCI). Deteriorated signal to noise ratio and non-stationarities stand as a paramount obstacle in steady decoding of EEG. Appearance of non-stationarities across EEG patterns notably upset the feature waveforms thus worsening the functioning of detection block and as a whole the Brain Computer Interface. Stationary Subspace schemes bring to light subspaces within which data distribution persists stably over time. Current work focuses on the development of a novel spatial transform based feature extraction scheme to address nonstationarity in EEG signals recorded against a drowsiness detection problem (a machine learning regression scenario). The presented approach: F-DIV-IT-JAD-WS derived features distinctly surpassed DivOVR-FuzzyCSP-WS based standard features across RMSE and CC performance criteria pair. We construe that the propounded feature derivation approach based on F-DIV-IT-JAD-WS will usher a significant attention in researchers who are developing algorithms for signal processing, specifically, for BCI regression scenarios.