Abstract:
Brain-Computer Interface (BCI) helps people who have a severe disease to interact
with external devices. Event-Related Potential (ERP) based BCI systems send
stimuli, then detect brain signals responding to stimuli. Stimuli play loud or flashes
with high intensity to distinguish the target brain signals in most researches. Thus,
typical oddball-paradigm causes psychological and psychical discomforts. This thesis
proposed non-oddball BCI paradigms which send stimuli which has almost zero
volume or intensity. Users tend to produce voluntary mental task during experiment
to cover typical stimuli’s brain signal, thus it compensates for the loss of accuracy of
a result of the reduced stimulus. As an outcome, a specific mental task was used to
investigate task-relevant endogenous components, and system performance was significantly
enhanced. The proposed Convolutional Neural Network (CNN) approach
has a decoding accuracy more than 90% for both the non-oddball visual and auditory
paradigms, respectively, outperforming the linear classifier model noticeably. These
discoveries offer new opportunities for pragmatic ERP systems, potentially improving
the usability of current brain-computer interfaces remarkably.