An Intelligent EEG Classification Methodology Based on Sparse Representation Enhanced Deep Learning Networks

The classification of electroencephalogram (EEG) signals is of significant importance in brain computer interface (BCI) systems. Aiming to achieve intelligent classification of EEG types with high accuracy, a classification methodology using sparse representation (SR) and fast compression residual convolutional neural networks (FCRes-CNN) is proposed. In the proposed methodology, EEG waveforms of class-1 and class-2 are segmented into sub-signals and 140 experimental samples was achieved for each type of EEG signal. The common spatial patterns (CSP) algorithm is used to obtain the features of the EEG signal. Subsequently, the redundant dictionary with sparse representation is constructed based on these features. Finally, the samples of the EEG types were imported into the FCRes-CNN model having fast down-sampling module and residual block structural units to be identified and classified. The datasets from BCI Competition 2005 (dataset IVa) and BCI Competition 2003 (dataset III) were used to test the performance of the proposed deep learning classifier. The classification experiments show that the recognition averaged accuracy of the proposed method is 98.82%. The experimental results show that the classification method provides better classification performance compared with sparse representation classification (SRC) method. The method can be applied successfully to BCI systems where the amount of data is large due to daily recording.