Optimizing the P300-based brain–computer interface: current status, limitations and future directions

Abstract

This paper summarizes the presentations and discussions at a workshop held during the Fourth International BCI Meeting charged with reviewing and evaluating the current state, limitations and future development of P300-based brain-computer interface (P300-BCI) systems. We reviewed such issues as potential users, recording methods, stimulus presentation paradigms, feature extraction and classification algorithms, and applications. A summary of the discussions and the panel's recommendations for each of these aspects are presented.

A High Performance MEG Based BCI Using Single Trial Detection of Human Movement Intention

“…The hand area also was activated during tongue movement. This may occur because the tongue is more difficult to move as compared to hand or foot, leading to a broader region of activation overlap as detailed in [62,63] All subjects showed dynamic activity mostly in the beta band (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). This is consistent with previous studies demonstrating the important role of beta band activity in motor control [3].…”

“…Temporal filters that are used include finite impulse response (FIR) filtering, power spectral density (PSD) estimation and discrete wavelet transformation (DWT). Signal averaging is also a commonly used method in the P300 and visual evoked potential (VEP) based BCI systems in [24,25] to enhance signal quality although this may slow down the response time.…”

“…For SAM-filtered virtual channel based classification of movement intensions from MEG data, channel reduction was achieved through the selection of virtual channels. Also, the selection of beta band (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) to study ERD served as an important parameter for feature reduction. In the MEG-Sensor domain, the power samples were calculated in the beta band (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) for the active state period when subjects were intending or urging to move (1.5 s -2.5 s), the segment length was 0.25 s with no overlapping between consecutive segments.…”

An introduction to the measurement of auditory event-related potentials (ERPs)

“…Over the past decade, a number of alternative EEG analyses have been proposed, such as time-frequency analysis (for a review see [105]), classification analyses (e.g., [106]), and computational model-based analyses [107]. The latter is a hypothesis-oriented approach to the processes in the brain, which has been developed mainly in fMRI research.…”

Application of P300 Event-Related Potential in Brain-Computer Interface

“…The trained classifier discriminates the brain response best resembling to a target stimulus from nontarget stimulus. In case of a P300 Speller, the classifier detects the letter with a maximum probability [10,[28][29][30].…”