Periodicity detection for BCI based on periodic code modulation visual evoked potentials

In this paper, we studied the brain computer interface (BCI) based on periodic code modulation visual evoked potential (VEP). The code modulation VEP (c-VEP) is one of electroencephalogram (EEG)-based BCI methods, and can achieve high speed communication. In this method, by identifying a pseudorandom binary code (PRBC) that modulates visual stimulus from measured EEG, we can transfer the command related with the PRBC into external devices. However, the communication speed becomes slow inversely with increased number of commands. In order to solve this problem, we proposed extended c-VEP method using periodic pseudorandom binary codes. In this method, we identify the periodicity from the EEG by using autocorrelation, and the command related with periodicity of the EEG is transferred. As a result of computer simulation, we were able to detect the periodicity of the EEG. Therefore, we verified the feasibility of the periodic pseudorandom binary codes for VEP-based BCI.

1. INTRODUCTION

Brain-computer interface (BCI) realizes a direct communication between the human brain and the external environment by translating human intentions into control signals [1]. A BCI allows an individual with severe motor disabilities or aphasia to have effective control over devices such as computers, wheelchairs and music instruments. A BCI system detects the presence of specific patterns in a brain activity and translates these patterns into meaningful control commands. Recently, electroencephalogram (EEG)-based BCI is attracting much attention due to their noninvasiveness and high communication speed. The information transfer rate (ITR) is commonly used to evaluate the communication speed of a BCI. Current EEG-based BCIs fall into four main categories such as sensorimotor activities, P300, visual evoked potentials (VEP), and common spatial pattern (CSP) [1]. In particular, a VEP-based BCI has received increasing attention due to their advantages of little user training, ease of use, and a high ITR [1]–[3].