Enhancing Auditory BCI Performance: Incorporation of Connectivity Analysis

Brain connectivity analysis to classify auditory stimuli applicable to invasive auditory BCI technology, particularly intracranial electroencephalography (iEEG) remains an exciting frontier. This study revealed insights into brain network dynamics, improving analysis precision to distinguish related auditory stimuli such as speech and music. We thereby contribute to advancing auditory BCI systems to bridge the gap between noninvasive and invasive BCI by utilizing noninvasive BCI methodological frameworks to invasive BCI (iEEG) data. We focused on the viability of using connectivity matrices in BCI calculated across brain waves such as alpha, beta, theta, and gamma. The research highlights that the traditional machine learning classifier, Support Vector Machine (SVM), demonstrates exceptional capabilities in handling brain connectivity data, exhibiting an outstanding 97% accuracy in classifying brain states, surpassing previous relevant studies with an improvement of 9.64% The results are significant as we show that neural activity in the gamma band provides the best classification performance using connectivity matrices calculated with Phase Locking Values and Coherence methods.