Improving EEG Signal Prediction via SSA and Channel Selection
سال انتشار: 1388
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 2,038
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شناسه ملی سند علمی:
CSICC14_060
تاریخ نمایه سازی: 24 خرداد 1388
چکیده مقاله:
Being able to predict the coming seizure can impressively improve the quality of the patients' lives since they can be warned to avoid doing risky activities via a prediction system. Here, a locally linear neuro fuzzy model is used to predict the EEG time series. Subsequently, this model is utilized in accompany with Singular Spectrum Analysis for prediction. Afterward, an information theoretic criterion is used to select a reliable subset of input variables which contain more information about the target signal. Comparison of three mentioned methods on one hand shows that SSA enables our prediction model to extract the main patterns of the EEG signal and highly improves the prediction accuracy. On the other hand, applying the method of channel selection to the model yields more accurate prediction. It is shown that fusion of some certain signals provides more information about the target and considerably improves the prediction ability.
نویسندگان
Bahareh Atoufi
Department of Electrical and Computer Engineering, University of Shahid Beheshti
Ali Zakerolhosseini
Department of Electrical and Computer Engineering, University of Shahid Beheshti
Caro Lucas
Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran