An efficient ICA approach based on neural network framework for biomedical applications

The paper developed a block-wise approach for ICA algorithms which can improve the computational efficiency of ICA without the degradation of performance for the separation of biomedical signals. Source signals including electrocardiogram (ECG), electromyogram (EMG) and 60-Hz sinusoid are linearly mixed for experimental tests. The mean-square errors (MSE) between the original sources and the separated signals are calculated for the evaluation of separation performance. These results demonstrated that the proposed block-wise approach can achieve the desired separation performance of signals in a more efficient way.