EEG-based video identification using graph signal modeling and graph convolutional neural network
This work addresses video identification for applications like brain-computer interfaces, but it appears incremental as it builds on existing graph-based methods for EEG data.
The paper tackled EEG-based video identification by representing EEG data as graph signals and using graph convolutional neural networks, achieving effectiveness in experimental results compared to existing methods.
This paper proposes a novel graph signal-based deep learning method for electroencephalography (EEG) and its application to EEG-based video identification. We present new methods to effectively represent EEG data as signals on graphs, and learn them using graph convolutional neural networks. Experimental results for video identification using EEG responses obtained while watching videos show the effectiveness of the proposed approach in comparison to existing methods. Effective schemes for graph signal representation of EEG are also discussed.