Efficient feature selection and linear discrimination of EEG signals
نویسندگان
چکیده
Brain Computer Interface systems (BCIs) based on Electroencephalogram (EEG) signal processing allow to translate the subject’s brain activities into control commands for computer devices. This paper presents an efficient embedded approach for feature selection and linear discrimination of EEG signals. In the first stage, four well-known feature extraction methods are used: Power spectral features, Hjorth parameters, Autoregressive modelling and Wavelet transform. From all obtained features, the proposed method efficiently selects and combines the most useful features for classification with less computational requirements. Least Angle Regression (LARS) is used for properly ranking each feature and, then, an efficient Leave-One-Out (LOO) estimation based on the PRESS statistic is used to choose the most relevant features. Experimental results on motor-imagery BCIs problems are provided to illustrate the competitive performance of the proposed approach against other conventional methods.
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ورودعنوان ژورنال:
- Neurocomputing
دوره 115 شماره
صفحات -
تاریخ انتشار 2013