Abstract
Abstract To establish a stable electrooculography (EOG)-based communication way for the patients with motor diseases, we proposed a saccadic signal recognition algorithm using independent component analysis (ICA) in this paper. According to the mapping pattern of independent components (ICs)-to-electrode, we designed an optimum ICA-based spatial filter. On this basis, we extracted feature parameters of four types of saccadic signals (i.e., up, down, left, and right) by linearly projecting pre-processed EOG signals to the spatial filter. In order to determine saccade related independent components (SRICs) and improve the recognition accuracy, we also developed an automatic SRICs detection algorithm and sample optimization strategy. Under lab environment, we adopted the support vector model (SVM) as the classifier. The average recognition accuracy of unit saccadic signals achieved 99.0% (before sample optimization) and 99.57% (sample optimized) over 10 participants, which reveals that the proposed algorithm presents an excellent classification performance in saccadic signals recognition.
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