EEG signals measure the neuronal activities on different brain regions via electrodes. Many existing studies on EEG-based emotion recognition do not exploit the topological structure of EEG signals . In this paper, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition , which is biologically supported and captures both local and global inter-channel relations . Specifically, we model the inter-channel relations in EEG signals via an adjacency matrix in our graph neural network where the connection and sparseness of the adjacency matrix are supported by the neurosicience theories of human URLanization. In addition, we propose two regularizers, namely node-wise domain adversarial training (NodeDAT) and emotion-aware distribution learning (EmotionDL), to improve the robustness of our model against cross-subject EEG variations and noisy labels, respectively. To thoroughly evaluate our model, we conduct extensive experiments in both subject-dependent and subject-independent classification settings on two public datasets : SEED and SEED-IV . Our model obtains better performance than competitive baselines such as SVM, DBN, DGCNN, BiDANN, and the state-of-the-art BiHDM in most experimental settings. Our model analysis demonstrates that the proposed biologically supported adjacency matrix and two regularizers contribute consistent and significant gain to the performance . Investigations on the neuronal activities reveal that pre-frontal, parietal and occipital regions may be the most informative regions for emotion recognition, which is consistent with relevant prior studies. In addition, experimental results suggest that global inter-channel relations between the left and right hemispheres are important for emotion recognition and local inter-channel relations between (FP1, AF3), (F6, F8) and (FP2, AF4) may also provide useful information .
Electroencephalography (EEG) measures the neuronal activities in different brain regions via electrodes. Many existing studies on EEG-based emotion recognition do not fully exploit the topology of EEG channels . In this paper, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition . RGNN considers the biological topology among different brain regions to capture both local and global relations among different EEG channels . Specifically, we model the inter-channel relations in EEG signals via an adjacency matrix in a graph neural network where the connection and sparseness of the adjacency matrix are inspired by neuroscience theories of human URLanization. In addition, we propose two regularizers, namely node-wise domain adversarial training (NodeDAT) and emotion-aware distribution learning (EmotionDL), to better handle cross-subject EEG variations and noisy labels, respectively. Extensive experiments on two public datasets , SEED and SEED-IV , demonstrate the superior performance of our model than state-of-the-art models in most experimental settings. Moreover, ablation studies show that the proposed adjacency matrix and two regularizers contribute consistent and significant gain to the performance of our RGNN model. Finally, investigations on the neuronal activities reveal important brain regions and inter-channel relations for EEG-based emotion recognition .
R
EEG signals measure
Electroencephalography (EEG) measures
R
on
in
R
exploit the topological structure of EEG signals
fully exploit the topology of EEG channels
R
, which is biologically supported and captures
. RGNN considers the biological topology among different brain regions to capture
R
inter-channel relations
relations among different EEG channels
R
our
a
R
supported by the neurosicience
inspired by neuroscience
R
improve the robustness of our model against
better handle
R
To thoroughly evaluate our model, we conduct extensive experiments in both subject-dependent and subject-independent classification settings
Extensive experiments
R
:
,
R
. Our model obtains better performance than competitive baselines such as SVM, DBN, DGCNN, BiDANN, and the
, demonstrate the superior performance of our model than
R
BiHDM
models
R
Our model analysis demonstrates
Moreover, ablation studies show
D
biologically supported
R
. Investigations
of our RGNN model. Finally, investigations
R
that pre-frontal, parietal and occipital regions may be the most informative regions for emotion recognition, which is consistent with relevant prior studies. In addition, experimental results suggest that global
important brain regions and
R
between the left and right hemispheres are important for emotion recognition and local inter-channel relations between (FP1, AF3), (F6, F8) and (FP2, AF4) may also provide useful information
for EEG-based emotion recognition