Using electroencephalogram for emotion recognition based on filter-bank long short-term memory networks.
10.7507/1001-5515.202012054
- Author:
Jiaheng WANG
1
;
Yueming WANG
1
;
Lin YAO
1
Author Information
1. School of Computer Science, Zhejiang Universty, Hangzhou 310000, P.R.China.
- Publication Type:Journal Article
- Keywords:
electroencephalogram;
emotion recognition;
feature extraction;
long short-term memory
- MeSH:
Arousal;
Electroencephalography;
Emotions;
Humans;
Memory, Short-Term;
Neural Networks, Computer
- From:
Journal of Biomedical Engineering
2021;38(3):447-454
- CountryChina
- Language:Chinese
-
Abstract:
Emotion plays an important role in people's cognition and communication. By analyzing electroencephalogram (EEG) signals to identify internal emotions and feedback emotional information in an active or passive way, affective brain-computer interactions can effectively promote human-computer interaction. This paper focuses on emotion recognition using EEG. We systematically evaluate the performance of state-of-the-art feature extraction and classification methods with a public-available dataset for emotion analysis using physiological signals (DEAP). The common random split method will lead to high correlation between training and testing samples. Thus, we use block-wise
