Deep Convolutional Neural Networks for mental load classification based on EEG data

Electroencephalograph (EEG), the representation of the brain's electrical activity, is a widely used measure of brain activities such as working memory during cognitive tasks. Varying in complexity of cognitive tasks, mental load results in different EEG recordings. Classification of mental load is one of core issues in studies on working memory. Various machine learning methods have been introduced into this area, achieving competitive performance. Inspired by the recent breakthrough via deep recurrent convolutional neural networks (CNNs) on classifying mental load, we propose improved CNNs methods for this task. Specifically, our frameworks contain both single-model and double-model methods. With the help of our models, spatial, spectral, and temporal information of EEG data is taken into consideration. Meanwhile, a novel fusion strategy for utilizing different networks is introduced in this work. The proposed methods have been compared with state-of-the-art ones on the same EEG database. The comparison results show that both our single-model method and double-model method can achieve comparable or even better performance than the well-performed deep recurrent CNNs. Furthermore, our proposed CNNs models contain less parameters than state-of-the-art ones, making it be more competitive in further practical application.