No abstract

No abstract

The present work proposes two novel approaches namely One Dimensional adaptive average Local Binary Pattern (1-D AaLBP) and One-Dimensional adaptive difference Local Binary Pattern (1-D AdLBP) for feature extraction from EEG signals and Convolutional Neural Network (CNN) for classification of EEG signals. Both the proposed feature extraction methods are computationally easy to implement. In the first step the histograms are formed from the extracted patterns, after that feature vectors of the histogram are given as input to the classifier. Two benchmark EEG datasets such as Bonn and CHB-MIT are employed for conducting experiments for comparing the performances of the proposed method with other existing research works. The performance measures such as sensitivity, specificity, classification accuracy and execution time are used for evaluating the proposed methods. It is learned from the experiments conducted that among various methods the proposed method provides improved performance in terms of sensitivity, specificity, classification accuracy and execution time.

No abstract

No abstract