PMID- 35588679
OWN - NLM
STAT- MEDLINE
DCOM- 20220624
LR  - 20220729
IS  - 1879-0534 (Electronic)
IS  - 0010-4825 (Linking)
VI  - 146
DP  - 2022 Jul
TI  - EEG emotion recognition based on enhanced SPD matrix and manifold dimensionality 
      reduction.
PG  - 105606
LID - S0010-4825(22)00398-5 [pii]
LID - 10.1016/j.compbiomed.2022.105606 [doi]
AB  - Recently, Riemannian geometry-based pattern recognition has been widely employed 
      to brain computer interface (BCI) researches, providing new idea for emotion 
      recognition based on electroencephalogram (EEG) signals. Although the symmetric 
      positive definite (SPD) matrix manifold constructed from the traditional 
      covariance matrix contains large amount of spatial information, these methods do 
      not perform well to classify and recognize emotions, and the high dimensionality 
      problem still unsolved. Therefore, this paper proposes a new strategy for EEG 
      emotion recognition utilizing Riemannian geometry with the aim of achieving 
      better classification performance. The emotional EEG signals of 32 healthy 
      subjects were from an open-source dataset (DEAP). The wavelet packets were first 
      applied to extract the time-frequency features of the EEG signals, and then the 
      features were used to construct the enhanced SPD matrix. A supervised 
      dimensionality reduction algorithm was then designed on the Riemannian manifold 
      to reduce the high dimensionality of the SPD matrices, gather samples of the same 
      labels together, and separate samples of different labels as much as possible. 
      Finally, the samples were mapped to the tangent space, and the K-nearest 
      neighbors (KNN), Random Forest (RF) and Support Vector Machine (SVM) method were 
      employed for classification. The proposed method achieved an average accuracy of 
      91.86%, 91.84% on the valence and arousal recognition tasks. Furthermore, we also 
      obtained the superior accuracy of 86.71% on the four-class recognition task, 
      demonstrated the superiority over state-of-the-art emotion recognition methods.
CI  - Copyright (c) 2022 Elsevier Ltd. All rights reserved.
FAU - Gao, Yunyuan
AU  - Gao Y
AD  - College of Automation, Hangzhou Dianzi University, Hangzhou, China. Electronic 
      address: gyy@hdu.edu.cn.
FAU - Sun, Xinyu
AU  - Sun X
AD  - College of Automation, Hangzhou Dianzi University, Hangzhou, China.
FAU - Meng, Ming
AU  - Meng M
AD  - College of Automation, Hangzhou Dianzi University, Hangzhou, China.
FAU - Zhang, Yingchun
AU  - Zhang Y
AD  - Department of Biomedical Engineering, University of Houston, Houston, USA.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20220513
PL  - United States
TA  - Comput Biol Med
JT  - Computers in biology and medicine
JID - 1250250
SB  - IM
MH  - Algorithms
MH  - *Brain-Computer Interfaces
MH  - *Electroencephalography/methods
MH  - Emotions
MH  - Humans
MH  - Support Vector Machine
OTO - NOTNLM
OT  - Dimensionality reduction
OT  - EEG
OT  - Emotion recognition
OT  - Riemannian manifold
OT  - SPD matrix
EDAT- 2022/05/20 06:00
MHDA- 2022/06/25 06:00
CRDT- 2022/05/19 18:24
PHST- 2022/03/26 00:00 [received]
PHST- 2022/05/08 00:00 [revised]
PHST- 2022/05/09 00:00 [accepted]
PHST- 2022/05/20 06:00 [pubmed]
PHST- 2022/06/25 06:00 [medline]
PHST- 2022/05/19 18:24 [entrez]
AID - S0010-4825(22)00398-5 [pii]
AID - 10.1016/j.compbiomed.2022.105606 [doi]
PST - ppublish
SO  - Comput Biol Med. 2022 Jul;146:105606. doi: 10.1016/j.compbiomed.2022.105606. Epub 
      2022 May 13.