Vibrotactile Brain-Computer Interface with Error-Detecting Codes

Abstract

Nowadays, Brain–Computer Interfaces (BCIs) have been used widely especially for disabled people. Vibrotactile stimuli are alternatively used in BCI especially for patients whose vision or eye movements are impaired. Moreover, users’ training in tactile BCI is also easy. In information theory and coding theory, error-detecting codes are able to detect errors from the received data which is transmitted over unreliable transmission channels. BCIs could use the advantage of error-detecting codes because the classification process could be considered as a noisy transmission channel from translating brain waves to the user’s intent. In this paper, we present a P300 vibrotactile BCI based on the electroencephalogram (EEG) with error-detecting codes. A parity check code which is a simple method of error detection is used as an error-detecting code. The aim of this study is to compare the efficiency between a vibrotactile BCI with and without error-detecting codes. The classification accuracy and the information transfer rate (ITR) of applying error detection are improved 12.04 % and 0.53 bit/min, respectively, compared to the conventional method which does not apply error-correcting codes.