LDA-based classifiers for a mental tasks-based Brain-Computer Interface

Abstract

This paper describes a Brain-Computer Interface (BCI) based on electroencephalography (EEG). This BCI registers the brain rhythmic activity through 16 electrodes situated on the scalp and differentiates three cognitive processes. The Wavelet Transform (WT) has been used to extract the features. In order to differentiate three mental tasks, two Linear Discriminant Analysis (LDA) based classifiers have been developed. Both classifiers integrate four simultaneous LDA model solutions. One classifier uses a score system to classify the EEG features vector, while the other classifier applies a logical criterion. In the paper, both classifiers have been evaluated. The experimental results with different volunteers have been reported in the paper. This BCI will be incorporated into a shared control architecture that we are developing to control a robot arm. This shared control architecture is based on Radio Frequency Identification (RFID) technology.