Abstract
The paper aimed to present a method for the identification of alcoholic brain state using optimum allocation (OA)-based support vector machine (SVM). The OA scheme determines the representative data from a single time window of electroencephalogram (EEG) signals (called brain signal). Several statistical features have been extracted from each time window of EEG signals, and then these features are used to SVM classifier to identify the alcoholic brain state. The experimental results achieved by using benchmark database bespeak that the proposed method with SVM classifier (polynomial kernel) accomplishes higher classification accuracy and low false alarm rate than the other kernel functions. Thus, the proposed OA scheme can be used to classify the alcoholic state from EEG signals.
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Siuly, S., Kabir, E., Wang, H., Whittaker, F., Kuang, H. (2020). Alcoholic Brain State Identification from Brain Signals Using Support Vector Machine-Based Algorithm. In: Uddin, M., Bansal, J. (eds) Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-13-7564-4_22
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DOI: https://doi.org/10.1007/978-981-13-7564-4_22
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