A robust sensor-selection method for P300 brain–computer interfaces

Abstract

A brain-computer interface (BCI) is a specific type of human-computer interface that enables direct communication between human and computer through decoding of brain activity. As such, event-related potentials like the P300 can be obtained with an oddball paradigm whose targets are selected by the user. This paper deals with methods to reduce the needed set of EEG sensors in the P300 speller application. A reduced number of sensors yields more comfort for the user, decreases installation time duration, may substantially reduce the financial cost of the BCI setup and may reduce the power consumption for wireless EEG caps. Our new approach to select relevant sensors is based on backward elimination using a cost function based on the signal to signal-plus-noise ratio, after some spatial filtering. We show that this cost function selects sensors' subsets that provide a better accuracy in the speller recognition rate during the test sessions than selected subsets based on classification accuracy. We validate our selection strategy on data from 20 healthy subjects.

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“…Only nine sensors were used for each participant. The particular (centroparietal and occipital) electrode locations were chosen in order to optimize the signal to signal-plus-noise ratio (SSNR), according to one of our previous P300 Speller experiment [28]. We used the following sites from the extended 10-10 system: P7, P3, Pz, P4, P8, P09, O1, O2, and PO10.…”

Objective and Subjective Evaluation of Online Error Correction during P300-Based Spelling

“…100 Hz here corresponds to a good compromise between the need to sample above 60 Hz (the so-called engineer Nyquist frequency to avoid aliasing) and the advantage of reducing the dimension of the data for online processing. Feature extraction then consisted in linear spatial filtering, whose effect is to reduce the dimension of the data as well as to maximize the discriminability between the two classes (i.e., between targets and nontargets, during spelling, and between error and correct feedbacks, during error detection) [25].…”

“…The xDAWN algorithm provides orthogonal linear spatial filters that can be learned from training samples [25]. Based on our previous studies [16,22], we used a five-dimensional filter for both spelling and error detection.…”

Robust Virtual Keyboard for Brain-Computer Interface (ROBIK): An Halfway Update on the Project

“…Identification of optimal subset of sensors was carried out by comparing P300 identification performance from three different group of sensors: the complete EEG headset (32 electrodes), 8 electrodes individually selected to provide best performance, and common subgroup of 8 electrodes providing the best average performance in the population of 20 healthy volunteers [19,20,21]. This study, illustrated in Figure 2, shows that an individually customized subsets of 8 leads provides similar accuracy (Acc) than the complete headset (32 electrodes) in 85% of subjects.…”