Abstract:
This study presents a hybrid brain computer interface (BCI) system that achieves better accuracy
based on event related potential signals. Following system based on the P300-SSVEP hybrid
sequential BCI system to decode six reactive brain commands using ensemble classifier. The
device which we are using for the record of EEG data only displays the signal on the computer
screen and does not decode the signal into some readable file. So in order to get EEG readable
signal, we convert signal images into digital form using image processing techniques. Based on
the proposed algorithm of signal conversion, we have evaluated on previous EEG dataset and
results are encouraging. P300 signal is evoked by oddball paradigm using stimuli of images flicker
in random order. For P300 we have used already recorded six images stimulus dataset. The feature
vector is extracted from the denoised waves after filtered through least mean square (LMS) filters.
Extracted feature samples are fed into ensemble classifier model for classification. To achieve high
accuracy, output from ensemble classifier trigger respective SSVEP frequency stimulus. On
computer screen, triggered SSVEP stimulus begin to flash. A person is asked to focus on the
stimulus for several seconds. EEG signal on occipital region is recorded. After classification of
SSVEP signal command is sent to drive quadcopter. For BCI application, a virtual quadcopter
environment is created and controlled by proposed hybrid BCI system.
