Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2-D cursor movements for BCI using SVM and ANN

Abstract

Brain computer interface (BCI) is a new communication way between man and machine. It identifies mental task patterns stored in electroencephalogram (EEG). So, it extracts brain electrical activities recorded by EEG and transforms them machine control commands. The main goal of BCI is to make available assistive environmental devices for paralyzed people such as computers and makes their life easier. This study deals with feature extraction and mental task pattern recognition on 2-D cursor control from EEG as offline analysis approach. The hemispherical power density changes are computed and compared on alpha-beta frequency bands with only mental imagination of cursor movements. First of all, power spectral density (PSD) features of EEG signals are extracted and high dimensional data reduced by principle component analysis (PCA) and independent component analysis (ICA) which are statistical algorithms. In the last stage, all features are classified with two types of support vector machine (SVM) which are linear and least squares (LS-SVM) and three different artificial neural network (ANN) structures which are learning vector quantization (LVQ), multilayer neural network (MLNN) and probabilistic neural network (PNN) and mental task patterns are successfully identified via k-fold cross validation technique.

GKP Signal Processing Using Deep CNN and SVM for Tongue-Machine Interface

“…Moreover, a 50-Hz notch filter was carried out to eliminate the power line noise. After that, for the part of the SVM algorithm process, 10th order infinite impulse response (IIR) Butterworth low-pass filter was applied with a 40 Hz cut-off frequency [21]. After filtering operation and discrete wavelet transform technique, the raw data set for SVM is normalized according to Eq.…”

Identifying motor imagery activities in brain computer interfaces based on the intelligent selection of most informative timeframe

“…Spectral features are one of the most commonly used feature extraction techniques in sensorimotor based BCI systems [27,50]. We calculated spectral features from the power spectral density (PSD) of the timeframe (via Welch period gram) that provide signal power in our interested frequency bands (alpha and beta) in which ERD/ERS occurred.…”

Introducing chaos behavior to kernel relevance vector machine (RVM) for four-class EEG classification

“…It was proposed that appropriate regularization of LDA by shrinkage improves the LDA performance in single-trial ERP classification [ 10 ]. The ANN [ 12 – 15 ] is an artificial multi-layer “neuron” inspired by the biological neuronal structure in the human brain. In ANN, a hyperplane used for classification is obtained by computing the weighted sum between neurons.…”

“…In ANN, a hyperplane used for classification is obtained by computing the weighted sum between neurons. Three types of ANN structures for two-class 2-D cursor movement classification were developed in [ 12 ]. A filter based on ANN [ 13 ] was proposed to reduce EEG interference signals.…”

“…In these cases, kernel functions can be designed to map nonlinear separable samples into a high-dimensional space first, so that the samples in the high- dimensional space become linearly separable. In recent years, successful BCI experiments were reported based on SVM or its variations [ 12 , 16 – 23 ]. For instance, a SVM method was developed to distinguish seizure EEG epochs from normal ones [ 21 ].…”