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PeriodicalsBiomedical Engineering

EEG signal classification based on simple random sampling technique with least square support vector machine

Siuly Siuly·Yan Li·Peng Paul Wen
International Journal of Biomedical Engineering and Technology2011

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Abstract & Overview

This paper proposes a new approach based on Simple Random Sampling (SRS) technique with Least Square Support Vector Machine (LS-SVM) to classify two-class of electroencephalogram (EEG) signals. The experiments are carried out on two EEG databases and a synthetic Ripley dataset. All two-class pairs are tested and our proposed approach obtains a 95.58% average classification accuracy for the EEG epileptic database, 98.73% for the mental imagery tasks EEG database and 100% for Ripley data. We compare our method with two most recent methods for the epileptic database. Experimental results demonstrate that the proposed method is more promising than previously reported classification techniques.

Cite this record

Siuly Siuly, Yan Li, Peng Paul Wen (2011). EEG signal classification based on simple random sampling technique with least square support vector machine. International Journal of Biomedical Engineering and Technology, 7(4), 390–390. https://doi.org/10.1504/ijbet.2011.044417

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Siuly, S., Li, Y., & Wen, P. P. (2011). EEG signal classification based on simple random sampling technique with least square support vector machine. International Journal of Biomedical Engineering and Technology, 7(4), 390–390. https://doi.org/10.1504/ijbet.2011.044417
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