A Grouped Dynamic EEG Channel Selection Method for Emotion Recognition | IEEE Conference Publication | IEEE Xplore

A Grouped Dynamic EEG Channel Selection Method for Emotion Recognition


Abstract:

EEG signals directly reflect the active state of the brain, so they are widely used for emotion recognition. At present, many researchers have achieved noteworthy results...Show More

Abstract:

EEG signals directly reflect the active state of the brain, so they are widely used for emotion recognition. At present, many researchers have achieved noteworthy results by using multi-channel EEG signals. However, too many EEG channels will cause slow transmission, high experimental costs, and low efficiency. This paper proposed a grouped dynamic EEG channel selection method based on ReliefF and random forest (RF), termed GDCSBR. We divided all channels into four groups, which provided more choices and flexibility to subsequent dynamic channel selection. GDCSBR selected channels iteratively. With the iteration increased, the number of alternative channels decreased. At each iteration, we adopted the strategy with a minor loss of recognition accuracy. Finally, the results on subject-independent data were taken as the final choice, since there were relatively large differences between subjects. Experiments were carried out on the DEAP dataset. The recognition accuracy for valence reaches 81.27% while 10 channels are selected. As for arousal scale, 11 channels can obtain 82.36% of classification accuracy. In addition, we found that high-frequency bands play a crucial part in emotion recognition, and the selected channels were mostly located in the frontal and parietal lobes. These findings are coincident with previous work. Experimental results demonstrate the effectiveness of the proposed method.
Date of Conference: 09-12 December 2021
Date Added to IEEE Xplore: 14 January 2022
ISBN Information:
Conference Location: Houston, TX, USA

References

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