Comparison of Spectral and Template Matching Features for SSVEP BCI Target Frequency Classification

Abstract

Brain-computer interfaces (BCI) provide new communication and control channels to restore and support these functions of the restricted users. Among these Visual Evoked Potential (VEP) based BCIs are the most promising in terms of ease of use and performance. The frequency following phenomenon of VEPs produce Steady State Visual Evoked Potentials (SSVEP) at the frequency of stimulation of the human visual system. In such interface systems, each target is encoded with a particular stimulation frequency and phase. In communication purpose speller interfaces each target flickers a letter or character with the stimulation frequency and phase. The detection of the focused target correctly by the computer is required. In this process classification methods and feature extraction method play critical roles. This study used a publicly available benchmark dataset of a 40 target SSVEP BCI. In the analysis, two feature vectors are obtained from power spectrum parameters and one from stimulus template matching correlation coefficients. The performance of the three classification methods, namely Fine Tree, Linear Discriminant Analysis (LDA) and K-Nearest Neighbors (KNN), are compared using these feature vectors. Spectral features performed better than the template matching features. Especially target frequency signal ratio (TFSR) to the total stimulation band energy features provided better accuracy values. LDA and KNN performed better than decision tree in classification.