Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding
  Time-resolved EEG Motor Imagery Signals   Towards developing effective and efficient brain-computer interface (BCI)
systems, precise decoding of brain activity measured by electroencephalogram
(EEG), is highly demanded. Traditional works classify EEG signals without
considering the topological relationship among electrodes. However,
neuroscience research has increasingly emphasized network patterns of brain
dynamics. Thus, the Euclidean structure of electrodes might not adequately
reflect the interaction between signals. To fill the gap, a novel deep learning
framework based on the graph convolutional neural networks (GCNs) was presented
to enhance the decoding performance of raw EEG signals during different types
of motor imagery (MI) tasks while cooperating with the functional topological
relationship of electrodes. Based on the absolute Pearson's matrix of overall
signals, the graph Laplacian of EEG electrodes was built up. The GCNs-Net
constructed by graph convolutional layers learns the generalized features. The
followed pooling layers reduce dimensionality, and the fully-connected softmax
layer derives the final prediction. The introduced approach has been shown to
converge for both personalized and group-wise predictions. It has achieved the
highest averaged accuracy, 93.056% and 88.57% (PhysioNet Dataset), 96.24% and
80.89% (High Gamma Dataset), at the subject and group level, respectively,
compared with existing studies, which suggests adaptability and robustness to
individual variability. Moreover, the performance was stably reproducible among
repetitive experiments for cross-validation. To conclude, the GCNs-Net filters
EEG signals based on the functional topological relationship, which manages to
decode relevant features for brain motor imagery.

17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci