Parallel Implementation on GPU for EEG Artifact Rejection by Combining FastICA and TQWT

In this work, a new method for removal of ocular and muscular artifacts from Multi-channel electroencephalogram (EEG) is presented in order to obtain a 3D filtered cerebral mapping images. First, a FastICA algorithm of Independent Component Analysis (ICA) is applied in combination with the tunable Q-factor wavelet transform (TQWT), FastICA-TQWT. Then, to show the robustness of this method, a comparison was made between the proposed FastICA-TQWT method and the classical one FastICA-DWT based on three criteria: Mean Squared Error (MSE), correlation coefficient and Signal Noise Ratio (SNR). The results showed that the FastICA-TQWT method gave the highest Signal Noise Ratio and correlation coefficient and the minimum Mean Squared Error. However, the FastICA-TQWT algorithm requires an extremely high computing power. Therefore, the second contribution of this paper is to provide an EEG signal treatment by implementing the hybrid FastICA-TQWT algorithm using a new computing technology designed for a high-performance computing, called Graphical Processing Units (GPUs) using the Compute Unified Device Architecture (CUDA) technology. The performance of the parallel approach running along the GPU was compared to a CPU implementation.