PMID- 33052888
OWN - NLM
STAT- MEDLINE
DCOM- 20210625
LR  - 20220121
IS  - 1741-2552 (Electronic)
IS  - 1741-2552 (Linking)
VI  - 17
IP  - 5
DP  - 2020 Oct 14
TI  - Data augmentation for enhancing EEG-based emotion recognition with deep 
      generative models.
PG  - 056021
LID - 10.1088/1741-2552/abb580 [doi]
AB  - OBJECTIVE: The data scarcity problem in emotion recognition from 
      electroencephalography (EEG) leads to difficulty in building an affective model 
      with high accuracy using machine learning algorithms or deep neural networks. 
      Inspired by emerging deep generative models, we propose three methods for 
      augmenting EEG training data to enhance the performance of emotion recognition 
      models. APPROACH: Our proposed methods are based on two deep generative models, 
      variational autoencoder (VAE) and generative adversarial network (GAN), and two 
      data augmentation ways, full and partial usage strategies. For the full usage 
      strategy, all of the generated data are augmented to the training dataset without 
      judging the quality of the generated data, while for the partial usage, only 
      high-quality data are selected and appended to the training dataset. These three 
      methods are called conditional Wasserstein GAN (cWGAN), selective VAE (sVAE), and 
      selective WGAN (sWGAN). MAIN RESULTS: To evaluate the effectiveness of these 
      proposed methods, we perform a systematic experimental study on two public EEG 
      datasets for emotion recognition, namely, SEED and DEAP. We first generate 
      realistic-like EEG training data in two forms: power spectral density and 
      differential entropy. Then, we augment the original training datasets with a 
      different number of generated realistic-like EEG data. Finally, we train support 
      vector machines and deep neural networks with shortcut layers to build affective 
      models using the original and augmented training datasets. The experimental 
      results demonstrate that our proposed data augmentation methods based on 
      generative models outperform the existing data augmentation approaches such as 
      conditional VAE, Gaussian noise, and rotational data augmentation. We also 
      observe that the number of generated data should be less than 10 times of the 
      original training dataset to achieve the best performance. SIGNIFICANCE: The 
      augmented training datasets produced by our proposed sWGAN method significantly 
      enhance the performance of EEG-based emotion recognition models.
FAU - Luo, Yun
AU  - Luo Y
AUID- ORCID: 0000-0001-7780-4929
AD  - Center for Brain-like Computing and Machine Intelligence, Department of Computer 
      Science and Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Road, 
      Shanghai 200240, People's Republic of China.
FAU - Zhu, Li-Zhen
AU  - Zhu LZ
AD  - Center for Brain-like Computing and Machine Intelligence, Department of Computer 
      Science and Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Road, 
      Shanghai 200240, People's Republic of China.
FAU - Wan, Zi-Yu
AU  - Wan ZY
AD  - Center for Brain-like Computing and Machine Intelligence, Department of Computer 
      Science and Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Road, 
      Shanghai 200240, People's Republic of China.
FAU - Lu, Bao-Liang
AU  - Lu BL
AUID- ORCID: 0000-0001-8359-0058
AD  - Center for Brain-like Computing and Machine Intelligence, Department of Computer 
      Science and Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Road, 
      Shanghai 200240, People's Republic of China.
AD  - Key Laboratory of Shanghai Education Commission for Intelligent Interaction and 
      Cognitive Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Road, 
      Shanghai 200240, People's Republic of China.
AD  - Brain Science and Technology Research Center, Shanghai Jiao Tong University, 800 
      Dong Chuan Road, Shanghai 200240, People's Republic of China.
AD  - Qing Yuan Research Institute, Shanghai Jiao Tong University, 800 Dong Chuan Road, 
      Shanghai 200240, People's Republic of China.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20201014
PL  - England
TA  - J Neural Eng
JT  - Journal of neural engineering
JID - 101217933
SB  - IM
MH  - Algorithms
MH  - *Electroencephalography
MH  - Emotions
MH  - Machine Learning
MH  - *Neural Networks, Computer
EDAT- 2020/10/15 06:00
MHDA- 2021/06/29 06:00
CRDT- 2020/10/14 17:59
PHST- 2020/10/14 17:59 [entrez]
PHST- 2020/10/15 06:00 [pubmed]
PHST- 2021/06/29 06:00 [medline]
AID - 10.1088/1741-2552/abb580 [doi]
PST - epublish
SO  - J Neural Eng. 2020 Oct 14;17(5):056021. doi: 10.1088/1741-2552/abb580.