Open AccessThe Research of BDPCA Identifying Emotion by EEG
Author(s) -
Chao Yan,
Jintao Nian,
LV Chao
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2003/1/012002
Subject(s) - electroencephalography , cluster analysis , computer science , artificial intelligence , pattern recognition (psychology) , set (abstract data type) , feeling , data set , bayesian information criterion , machine learning , psychology , social psychology , neuroscience , programming language
Electroencephalogram (EEG) data contain wealthy information about the brain’s and body’s pathology and physiological state. It’s not easily to identify the truth that EEG contained. The unsupervised learning method don’t need to take label by human. Without subjective feeling, it greatly improve training accuracy. In this current paper, adopted improved Density Peak Clustering Algorithm (DPCA) to train EEG data. To solved the problem that difficult to determined cluster center number, Bayesian Information Criterion(BIC) was introduced. The algorithm was verified feasibility that in EEG processing by experiment which divided fatigue state level in lab. And used SJTU Emotion EEG Data set (SEED) identifying different emotions. Compared with other cluster algorithms, BDPCA accuracy totally raised about 5%. And BDPCA behavior was steadier in different emotion types.