A passive BCI for monitoring the intentionality of the gaze-based moving object selection

Improving the performance of a gaze independent P300-BCI by using the expectancy wave

OBJECTIVE

A P300-BCI conveys a subject's intention through recognition of their ERPs. However, in the case of visual stimuli, its performance depends strongly on eye gaze. When eye movement is impaired, it becomes difficult to focus attention on a target stimulus, and the quality of the ERP declines greatly, thereby affecting recognition efficiency.

APPROACH

In this paper, the expectancy wave (E-wave) is proposed to improve signal quality and thereby improve identification of visual targets under the covert attention. The stimuli of the P300-BCI described here are presented in a fixed sequence, so the subjects can predict the next target stimulus and establish a stable expectancy effect of the target stimulus through training. Features from the E-wave that occurred 0~300ms before a stimulus were added to the post-stimulus ERP components for intention recognition.

MAIN RESULTS

Comparisons of 10 healthy subjects before and after training demonstrated that the expectancy wave generated before target stimulus could be used with the P300 component to improve character recognition accuracy (CRA) from 85% to 92.4%. In addition, CRA using only the expectancy component can reach 68.2%, which is significantly greater than random probability (16.7%). The results of this study indicate that the expectancy wave can be used to improve recognition efficiency for a gaze-independent P300-BCI, and that training contributes to induction and recognition of the potential.

SIGNIFICANCE

This study proposes an effective approach to an efficient gaze-independent P300-BCI system.

Voice, rhythm, and beep stimuli differently affect the right hemisphere preponderance and components of stimulus-preceding negativity

The present study investigated whether auditory stimuli with different contents affect right laterality and the components of stimulus-preceding negativity (SPN). A time-estimation task was performed under voice, rhythm, beep, and control conditions. The SPN interval during which participants anticipated the stimulus was divided into quarters to define early and late SPNs. Early and late components of SPN were also extracted using a principal component analysis. The anticipation of voice sounds enhanced the early SPN and the early component, which reflected the anticipation of language processing. Beep sounds elicited the right hemisphere preponderance of the early component, the early SPN, and the late SPN. The rhythmic sound tended to attenuate the amplitude compared with the two other stimuli. These findings further substantiate the existence of separate early and late components of the SPN. In addition, they suggest that the early component reflects selective anticipatory attention toward differing types of auditory feedback.