In addition, the proposed approach was also applied on public dataset for classification of two cognitive tasks and achieved comparable classification results, i.e., 93.33% accuracy with KNN. Accuracy rates for MLP and NB classifiers were comparable at 97.11вЂ“89.63% and 91.60вЂ“81.07% for A5 and D5 coefficients, respectively. Accuracy rates for detailed coefficients were 98.57 and 98.39% for SVM and KNN, respectively and for detailed coefficients (D5) deriving from the sub-band range (3.90вЂ“7.81 Hz). Outcomes yielded 99.11% accuracy via SVM classifier for coefficient approximations (A5) of low frequencies ranging from 0 to 3.90 Hz. Classifiers such as, K-nearest neighbors (KNN), Support Vector Machine (SVM), Multi-layer Perceptron (MLP), and NaГЇve Bayes (NB) were then employed. A high density EEG dataset validated the proposed method (128-channels) by identifying two classifications: (1) EEG signals recorded during complex cognitive tasks using Raven's Advance Progressive Metric (RAPM) test (2) EEG signals recorded during a baseline task (eyes open). Extracted relative wavelet energy features were normalized to zero mean and unit variance and then optimized using Fisher's discriminant ratio (FDR) and principal component analysis (PCA). Wavelet based feature extraction such as, multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were computed. The authors propose a вЂњpattern recognitionвЂќ approach that discriminates EEG signals recorded during different cognitive conditions. Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi Petronas, Seri Iskandar, MalaysiaДЇeature extraction is an important step in the process of electroencephalogram (EEG) signal classification.Hafeez Ullah Amin *, Wajid Mumtaz, Ahmad Rauf Subhani, Mohamad Naufal Mohamad Saad and Aamir Saeed Malik *

Sample eeg data