Evolutionary Algorithm Based Feature Optimization for Multi-Channel EEG Classification - Wang, Yubo; Veluvolu, Kalyana C. (Frontiers in Neuroscience, 2017)
BCI systems that rely on EEG signals usually employ Fourier based methods for time-frequency decomposition and for subsequent feature extraction. The band-limited multiple Fourier linear combiner is well suited for such band-limited signals due to its real-time applicability. Despite the improved performance of these techniques in two channel settings, its application in multiple-channel EEG is not straightforward and challenging. As more channels are available, a spatial filter will be required to eliminate the noise and preserve the required useful information. Moreover, multiple-channel EEG also adds the high dimensionality to the frequency feature space. Feature selection will be required to stabilize the performance of the classifier. In this paper, we develop a new method based on Evolutionary Algorithm (EA) to solve these two problems simultaneously. The real-valued EA encodes both the spatial filter estimates and the feature selection into its solution and optimizes it with respect to the classification error. Three Fourier based designs are tested in this paper. Our results show that the combination of Fourier based method with co-variance matrix adaptation evolution strategy (CMA-ES) has the best overall performance.