Abstract
The brain-computer interface (BCI) links a human brain and a computer directly. Using the BCI, people with motor disorder, such as amyotrophic lateral sclerosis (ALS), or spinal injury can control their environment or communicate with people around them. A single-trial BCI is needed to realize fast communication via a BCI-based speller, which types words into the computer, driven by brain signals. A single-trial BCI based on independent component analysis (ICA) is proposed. In the conventional method, the accuracy of 76.7% is achieved by using correlation analysis to obtain the existence of P300, which is an evoked potential in electroencephalogram (EEG). However, there is still room for improvement of the accuracy by selecting optimal sensors. By considering this possibility, better results can be realized. Therefore, in this study, we use the backward elimination method for sensor selection (to reduce the number of electrodes) and 81.0% accuracy is achieved. The aim of this study is the identification of the important electrode points for the achievement of the single-trial BCI. The proposed method exhibits a high recognition accuracy.
