Paper Abstract and Keywords |
Presentation |
2021-12-15 11:40
Emotion discrimination model for learning spatio-temporal frequency information of EEG using SNN Rio Kanda, Chika Sugimoto (Yokohama National Univ.) |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In order to achieve high accuracy in emotion recognition based on EEG, it is considered effective to simultaneously learn the interrelationships among temporal, spatial, and frequency information contained in the EEG related to emotion. Therefore, we propose a new emotion identification model that incorporates frequency information into a spiking neural network (SNN) that can learn spatio-temporal information. The input data is encoded by decomposing the EEG into the frequency bands of theta, alpha, beta, and gamma waves, and then taking the product of the correlation coefficient between each band and emotion. As a result, we confirmed that the use of HSIC as a correlation coefficient improved the recognition accuracy for both positive and negative emotions. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Emotion recognition / Spiking Neural Network / EEG / Affective Computing / / / / |
Reference Info. |
IEICE Tech. Rep. |
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Conference Information |
Committee |
HCGSYMPO |
Conference Date |
2021-12-15 - 2021-12-17 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
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Paper Information |
Registration To |
HCGSYMPO |
Conference Code |
2021-12-HCGSYMPO |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Emotion discrimination model for learning spatio-temporal frequency information of EEG using SNN |
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Emotion recognition |
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Spiking Neural Network |
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EEG |
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Affective Computing |
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1st Author's Name |
Rio Kanda |
1st Author's Affiliation |
Yokohama National University (Yokohama National Univ.) |
2nd Author's Name |
Chika Sugimoto |
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Yokohama National University (Yokohama National Univ.) |
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Author-1 |
Date Time |
2021-12-15 11:40:00 |
Presentation Time |
15 minutes |
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HCGSYMPO |
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