Brain topographic map: A visual feature for multi-view fusion design in EEG-based biometrics

In recent years, lightweight electroencephalogram (EEG) electronic acquisition devices with less electrodes have garnered increasing attention, leading to diverse applications for consumers, such as biometrics systems. Nevertheless, conventional EEG feature extraction relies on matrix data with low spatial resolution from user-friendly acquisition devices, resulting in the lack of brain region information. To this end, we consider the brain topographic map (BTM) as an image-like feature for EEG representation, which promotes us to design a multi-view fusion learning architecture. The proposed method extracts the power spectral density (PSD) feature and interpolates the PSD feature by the Biharmonic Spline Interpolation algorithm to obtain the BTM data, which are then mapped onto the human scalp to generate the BTM image as the BTM feature, explicitly embodying the connectivity and potential information between brain regions. Finally, we establish a multi-view fusion model to fuse the image-view feature and the tensor-view feature. Extensive experimental results show that the BTM feature is competitive and the proposed multi-view fusion model outperforms other models by 4% to 20% improvement in the Correct Recognition Rate. Our research provides a novel visual feature generation aspect with a multi-view fusion design to build robust EEG-based biometrics systems.