TY - JOUR
T1 - Toward Wearable EEG-based Alertness Detection System Using SVM with Optimal Minimum Channels
AU - Yang, Mihong
AU - Li, Huiyan
AU - Sun, Xiaozhou
AU - Yang, Li
AU - Duan, Hailong
AU - Che, Yanqiu
AU - Han, Chunxiao
N1 - Publisher Copyright:
© The Authors, published by EDP Sciences, 2018.
PY - 2018/10/15
Y1 - 2018/10/15
N2 - 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%.
AB - 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%.
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U2 - 10.1051/matecconf/201821403009
DO - 10.1051/matecconf/201821403009
M3 - Conference article
AN - SCOPUS:85059046442
SN - 2261-236X
VL - 214
JO - MATEC Web of Conferences
JF - MATEC Web of Conferences
M1 - 03009
T2 - 2nd International Conference on Information Processing and Control Engineering, ICIPCE 2018
Y2 - 27 July 2018 through 29 July 2018
ER -
