Frequency filter networks for EEG-based recognition

In some of the EEG-based recognition tasks, for example, EEG-based emotion recognition (EEG-ER), enhancing feature extractors is difficult. In such cases, the use of deep neural networks which are capable of classification and recognition by the input of raw data is desirable. Therefore, effective components and models of neural networks for EEG-based recognition must be proposed. In addition, the capability of easily interpreting the feature networks learned is also needed not only from the viewpoint of enhancing features but also from the neuroscientific viewpoint. This paper proposes a discrete Fourier transform (DFT) layer, inverse DFT layer, and other components to compose a frequency filter module. This module was proposed for embedding a bandpass filter suitable for EEG data and the targets, and interpreting features in frequency domain. The proposed models were compared with their counterparts and state-of-the-art models, and evaluated according to their accuracies and visualizing features for person recognition and emotion recognition. The results showed that the frequency filter module is effective in preprocessing or interpreting some features in EEG with a characteristic in frequency domain.