Toward Wearable EEG-based Alertness Detection System Using SVM with Optimal Minimum Channels
| Autor: | Hailong Duan, Huiyan Li, Xiaozhou Sun, Yanqiu Che, Li Yang, Chunxiao Han, Mihong Yang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
medicine.diagnostic_test business.industry Wearable computer Pattern recognition 02 engineering and technology Electroencephalography Task (project management) Support vector machine Alertness 020901 industrial engineering & automation lcsh:TA1-2040 0202 electrical engineering electronic engineering information engineering medicine Optimal combination 020201 artificial intelligence & image processing Artificial intelligence business lcsh:Engineering (General). Civil engineering (General) Wearable eeg Communication channel |
| Zdroj: | MATEC Web of Conferences, Vol 214, p 03009 (2018) |
| Popis: | Alertness is the state of attention by high sensory awareness. A lack of alertness is one of the main reasons of serious accidents. Traffic accidents caused by driver’s drowsy driving have a high fatality rate. This paper presents an EEG-based alertness detection system. In order to ensure the convenience and long-term wearing comfort of EEG recordings, the wearable electrode cap will be the principal choice in the future, and the selection of channels will be limited. We first built a 3-D simulated driving platform using Unity3D. Then, we perform an experiment with driving drift task. EEG signals are recorded form frontal and occipital regions. We select data segments using the driving reaction time, classify the state of alertness with a support vector machine (SVM), and select the optimal combination of channels with minimum number of channels. Our results demonstrate that alertness can be classified efficiently with one channel (PO6) at accuracy of 93.52%, with two channels (FP1+PO6) at 95.85% and with three channels (FP1+PO6+PO5 and FP1+PO6+POZ) at 96.11%. |
| Databáze: | OpenAIRE |
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