Brain Computer Interface Based on the Flash Onset and Offset Visual Evoked Potentials

Abstract

Severe motor disabilities can limit one’s ability in communication, especially for patients suffering from amyotrophic lateral sclerosis (ALS), severe cerebral palsy, head trauma, multiple sclerosis, and muscular dystrophies who are incapable of conveying their intentions (locked-in syndrome) to the external environment. For the last several decades, a considerable amount of research effort has been devoted towards the development of novel communication techniques which are independent of peripheral nerves and muscles. One promising method is the use of neural activities, for example, electroencephalography (EEG) or intracortical neural activities, arising from the human brain, as control or communication signals. Such techniques are referred to as ‘brain computer interfaces’ (BCIs) (Wolpaw et al., 2000). Several EEG-based BCI systems have been developed with elaborately designed paradigms to induce endogenous or exogenous neuroelectric signals which were detected and translated into control signals for communication purposes. Endogenous BCI communicates with environments independent of sensatory responses or muscles which enable users to control external environments directly. For examples, Pfurtscheller et al. (2000) measured sensorimotor mu rhythms during subject’s imagery movements and achieved a high recognition rate of 90%; Blankertz et al. (2007) constructed Berlin Brain-Computer Interface (BBCI) with high ITR (>35 bits/min) based on detections of the modulations of sensorimotor rhythms due to motor imagery; Birbaumer et al. (1999) developed a Thought Translation Device (TTD) to measure slow cortical potentials (SCPs) for a binary selection task; Mason & Birch (2000) designed an asynchronous detector to control a binary switch by using the detected motor-related potentials (MRPs) filtered within 1-4 Hz. However, the ITRs of endogenous BCIs are relatively low (between 5 and 25 bits/min) because the performance of translation algorithm in extracting reliable features can be easily degraded by the undesired characteristics of neuroelectric signals, such as artifacts, task-unrelated EEG, and large variability in latencies. Besides, the subjects participated in the endogenous BCIs usually require extensive training for generating specific patterns. The exogenous BCIs, on the contrary, require parts of user’s sensation ability involved in a stimulating environment to induce sensatory neurophysiological activities, such as P300-based (Donchin et al., 2000; Meinicke et al., 2003), VEP (visual evoked potential)-based (Lee et al., 2005; Lee et al., 2006; Sutter, 1992), SSVEP (steady-state visual evoked potential)-based (Cheng et al., 2002; Cheng