Classification of EEG using PCA, ICA and Neural Network

clinicians and researchers alike buried in a sea of EEG paper records. The advent of computers and the technologies associated with them has made it possible to effectively apply a host of methods to quantify EEG changes [4]. The EEG spectrum contains some characteristic waveforms that fall primarily within four frequency bands: delta (<4 Hz), theta (4-8 Hz), alpha (8-13 Hz) and beta (13-30 Hz). Since the EEG signals are non-stationary, the parametric methods are not suitable for frequency decomposition of these signals. A powerful method was proposed in the late 1980s to perform time-scale analysis of signals: the wavelet transforms (WT). This method provides a unified framework for different techniques that have been developed for various applications. Since the WT is appropriate for analysis of non-stationary signals and this represents a major advantage over spectral analysis, it is well suited to locating transient events, which may occur during epileptic seizures. Wavelet’s feature extraction and representation properties can be used to analyze various transient events in biological signals. Adeli et al. [2] gave an overview of the discrete wavelet transform (DWT) developed for recognizing and quantifying spikes, sharp waves and spike-waves. They used wavelet transform to analyze and characterize epileptiform discharges in the form of 3-Hz spike and wave complex in patients with absence seizure. The techniques have been used to address this problem such as the analysis of EEG signals for epileptic seizure detection using the autocorrelation function; frequency domain features, time–frequency analysis, and wavelet transform (WT). The results of the studies in the literature have demonstrated that the WT is the most promising method to extract features from the EEG signals. In this respect, in the present study for epileptic seizure detection in patients with absence seizures (petit mal), the WT was used for feature extraction from the EEG signals belonging to the normal and the patient with absence seizure [11].