Which Physiological Components are More Suitable for Visual ERP Based Brain–Computer Interface? A Preliminary MEG/EEG Study

Abstract

We investigated which evoked response component occurring in the first 800 ms after stimulus presentation was most suitable to be used in a classical P300-based brain-computer interface speller protocol. Data was acquired from 275 Magnetoencephalographic sensors in two subjects and from 61 Electroencephalographic sensors in four. To better characterize the evoked physiological responses and minimize the effect of response overlap, a 1000 ms Inter Stimulus Interval was preferred to the short (\400 ms) trial length traditionally used in this class of BCIs. To investigate which scalp regions conveyed information suitable for BCI, a stepwise linear discriminant analysis classifier was used. The method iteratively analyzed each individual sensor and determined its performance indicators. These were then plotted on a 2-D topographic head map. Preliminary results for both EEG and MEG data suggest that components other than the P300 maximally represented in the occipital region, could be successfully used to improve classification accuracy and finally drive this class of BCIs.

A Comparison of a Brain-Computer Interface and an Eye Tracker: Is There a More Appropriate Technology for Controlling a Virtual Keyboard in an ALS Patient?

“…They are based on the P300 speller first developed by Farwell and Donchin [5], which is still referenced and intensely studied [3,6,7,8]. In this BCI, a 6 x 6 matrix of letters, arranged in rows and columns, is shown to the subject.…”

An Efficient Decoder for the Recognition of Event-Related Potentials in High-Density MEG Recordings

“…In previous work [10], we demonstrated greater discrimination of adjacent motor potentials in MEG data compared with simultaneously recorded EEG data. Furthermore, a study investigating the classification of visual ERPs [11] has provided evidence that discriminable data are more focused in the MEG than in the EEG. The potential advantages of MEG could open a promising communication channel for severely impaired patients, even though only applicable in a laboratory environment.…”

Proposal of a P300-based BCI Speller using a Predictive Text System

“…They are based on the P300 speller first developed by Donchin et al (2000), which is still referenced and intensely studied (Bianchi et al, 2010;Kleih et al, 2010;Krusienski et al, 2008;. In this BCI, a 6 x 6 matrix of letters, arranged in rows and columns, is shown to the subject.…”