摘要：Classifying brain activities based on electroencephalogram (EEG) signals is one of the important applications of time series discriminant analysis for diagnosing brain disorders. In this paper, we introduce a new method based on the Singular Spectrum Analysis (SSA) technique for classifying brain activity based on EEG signals via an application into a benchmark dataset for epileptic study with five categories, consisting of 100 EEG recordings per category. The results from the SSA based approach are compared with those from discrete wavelet transform before proposing a hybrid SSA and principal component analysis based approach for improving accuracy levels further.
关键词：EEG; singular spectrum analysis; time series discriminant analysis; kernel; principal component analysis EEG ; singular spectrum analysis ; time series discriminant analysis ; kernel ; principal component analysis
