Abstract
A brain-computer interface (BCI) is a tool to communicate with a computer via brain signals without the user making any physical movements, thus enabling disabled people to communicate with their environment and with others. P300-based ERP spellers are a widely used spelling visual BCI using the P300 component of event-related potential (ERP). However, they have a technical problem in that at least 2 N flashes are required to present N characters. This prevents the improvement of accuracy and restricts the typing speed. To address this issue, we propose a method that uses N100 in addition to P300. We utilize novel stimulus images to detect the user’s gazing position by using N100. By using both P300 and N100, the proposed visual BCI reduces the number of flashes and improves the accuracy of the P300 speller. We also propose using N100 to classify non-control (NC) and intentional control (IC) states. In our experiments, the detection accuracy of N100 was significantly higher than that of P300 and the proposed method exhibited a higher information transfer rate (ITR) than the P300 speller.
Highlights
The brain-computer interface (BCI) is an alternative communication pathway to communicate with and control devices by discriminating brain signals without the user making any physical movements
Since N100 is evoked without any counting task, we propose an efficient stimulus presentation based on rapid visual presentation (RVP) in order to utilize N100 for BCI
The significance of the difference over the P1 peak of FC2 was confirmed by t-testing (p < 0.01)
Summary
The brain-computer interface (BCI) is an alternative communication pathway to communicate with and control devices by discriminating brain signals without the user making any physical movements. The major goal of BCI research is to develop applications that enable disabled or elderly users to communicate with others and control their limbs and/or the environment [1]. We focus on an EEG-based BCI system. EEG-based ERP spellers have been extensively used because of their simplicity and high accuracy. Most of ERP-spellers use P300 evoked by counting the number of times the target is intensified to detect the desired target command [3,4]. Each row and column of the matrix is flashed in random order and the user silently counts the number of times the desired character is presented. The desired character is determined by detecting P300 evoked by the mental task. In [4], early ERP components such as
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