ReliefF-Based EEG Sensor Selection Methods for Emotion Recognition.
Electroencephalogram (EEG) signals recorded from sensor electrodes on the scalp can directly detect the brain dynamics in response to different emotional states. Emotion recognition from EEG signals has attracted broad attention, partly due to the rapid development of wearable computing and the needs of a more immersive human-computer interface (HCI) environment. To improve the recognition performance, multi-channel EEG signals are usually used. A large set of EEG sensor channels will add to the computational complexity and cause users inconvenience. ReliefF-based channel selection methods were systematically investigated for EEG-based emotion recognition on a database for emotion analysis using physiological signals (DEAP). Three strategies were employed to select the best channels in classifying four emotional states (joy, fear, sadness and relaxation). Furthermore, support vector machine (SVM) was used as a classifier to validate the performance of the channel selection results. The experimental results showed the effectiveness of our methods and the comparison with the similar strategies, based on the F-score, was given. Strategies to evaluate a channel as a unity gave better performance in channel reduction with an acceptable loss of accuracy. In the third strategy, after adjusting channels' weights according to their contribution to the classification accuracy, the number of channels was reduced to eight with a slight loss of accuracy (58.51% ± 10.05% versus the best classification accuracy 59.13% ± 11.00% using 19 channels). In addition, the study of selecting subject-independent channels, related to emotion processing, was also implemented. The sensors, selected subject-independently from frontal, parietal lobes, have been identified to provide more discriminative information associated with emotion processing, and are distributed symmetrically over the scalp, which is consistent with the existing literature. The results will make a contribution to the realization of a practical EEG-based emotion recognition system.
College of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China. jhzhang@hdu.edu.cn.
Jianhai
J
Zhang
College of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China. cming163@163.com.
Ming
M
Chen
College of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China. lnkzsk@126.com.
Shaokai
S
Zhao
College of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China. sqhu@hdu.edu.cn.
Sanqing
S
Hu
Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310012, China. shizg@zju.edu.cn.
Zhiguo
Z
Shi
Department of Computer Science, The University of Massachusetts Lowell, Lowell, MA 01854, USA. ycao@cs.uml.edu.
Yu
Y
Cao
D000465:Algorithms; D015374:Biosensing Techniques / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D004644:Emotions / Q000502:physiology*; D006801:Humans; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine
EEG; ReliefF; emotion recognition; sensor selection
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20442037
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16468569
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21427014
Trends Cogn Sci. 2012 Sep;16(9):458-66
22890089
Brain Topogr. 2010 Jun;23(2):186-93
20162347
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:7250-3
26737965
IEEE Trans Biomed Eng. 2004 Jun;51(6):1003-10
15188871