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. 2018 Dec 13:2018:9750904.
doi: 10.1155/2018/9750904. eCollection 2018.

Recognition of Emotions Using Multichannel EEG Data and DBN-GC-Based Ensemble Deep Learning Framework

Hao Chao  1 , Huilai Zhi  1 , Liang Dong  1 , Yongli Liu  1
Affiliations

Recognition of Emotions Using Multichannel EEG Data and DBN-GC-Based Ensemble Deep Learning Framework

Hao Chao et al. Comput Intell Neurosci. .

Abstract

Fusing multichannel neurophysiological signals to recognize human emotion states becomes increasingly attractive. The conventional methods ignore the complementarity between time domain characteristics, frequency domain characteristics, and time-frequency characteristics of electroencephalogram (EEG) signals and cannot fully capture the correlation information between different channels. In this paper, an integrated deep learning framework based on improved deep belief networks with glia chains (DBN-GCs) is proposed. In the framework, the member DBN-GCs are employed for extracting intermediate representations of EEG raw features from multiple domains separately, as well as mining interchannel correlation information by glia chains. Then, the higher level features describing time domain characteristics, frequency domain characteristics, and time-frequency characteristics are fused by a discriminative restricted Boltzmann machine (RBM) to implement emotion recognition task. Experiments conducted on the DEAP benchmarking dataset achieve averaged accuracy of 75.92% and 76.83% for arousal and valence states classification, respectively. The results show that the proposed framework outperforms most of the above deep classifiers. Thus, potential of the proposed framework is demonstrated.

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Figures

Figure 1
Figure 1
Arousal-valence plane.
Figure 2
Figure 2
Structure of deep belief network.
Figure 3
Figure 3
Structure of deep belief network with glia chains.
Figure 4
Figure 4
Structure of RBM with glia chain.
Figure 5
Figure 5
Architecture of DBN-GC-based ensemble deep learning model.
Figure 6
Figure 6
Comparison of emotion recognition results between DBN and DBN-GC.
Figure 7
Figure 7
MRA of the proposed ensemble deep learning model with different values of glia effect weight.
Figure 8
Figure 8
MRA of the proposed ensemble deep learning model with different values of attenuation factor.
Figure 9
Figure 9
MRA of the proposed ensemble deep learning model with different values of glia threshold.
Algorithm 1
Algorithm 1
Pseudocodes for training the RBM with glia chain.
See this image and copyright information in PMC

References

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