A Hypernetwork-Based Feature Selection Method for EEG Classification

Electroencephalography (EEG) is an important method of detecting brain activity. Various techniques are used to classify EEG for tasks such as motor imagery, emotion recognition, and medical diagnosis. However, many of these methods solely focus on the temporal information in EEG and ignore the spatial information provided by the positions of the electrodes. To address this gap, a combine technique of hypernetwork and recurrent neural network (RNN), so-called HyperRNN, is designed to utilize both spatial and temporal information in EEG and select better features subsets for EEG classification. In more details, hypernetwork is introduced to incorporates spatial information in EEG and search valid feature subsets. The RNN is employed to evaluate feature subsets, which consider temporal information with its memory architecture. Experiments on public EEG datasets demonstrate the proposed algorithm outperforms classical feature selection methods and improve EEG classification accuracy.