A comparison between ANN and SVM classifier for drowsiness detection based on single EEG channel

In recent years, the detection of drowsiness based on Electroencephalogram (EEG) signal has been paid great attentions. Most of the popular algorithms used for Brain Computer Interface (BCI) applications are, the Support Vector Machine (SVM) and the Artificial Neuronal Network (ANN)). The challenge is to developed a drowsiness detection system that is at once adapt to an embedded implementation and easy to use by the driver. In this respect, we propose to evaluate the performance of thise two classifiers used for EEG classification in order to select the most appropriate one which can provide higher classification accuracy. The validation process is conducted on EEG signals of the polysomnography database where EEG signals of 10 persons have been recorded from C3-O1 region. The signal read from the dataset mentioned above is segmented into 30 second windows then features are extracted from these segments using Fast Fourier Transform (FFT). These features are fed to ANN and SVM to select the most appropriate one. To evaluate the performance of the classifier we have used two metrics: the accuracy of classifier and the Receiver Operating Characteristic (ROC) curve. Based on this study, we conclude that the ANN classifier is better than SVM for the EEG drowsiness signals when using one EEG channel.