ARFN: An Attention-Based Recurrent Fuzzy Network for EEG Mental Workload Assessment

Assessing mental workload using electroencephalogram (EEG) signals is a significant research avenue within the brain-computer interface domain. However, due to the low signal-to-noise ratio in EEG signals and the inter-individual variability in EEG data acquisition, achieving high accuracy and generalization in feature extraction and classification for mental workload assessment is still challenging. We propose a novel deep learning framework named attention-based recurrent fuzzy network (ARFN) for EEG mental workload assessment. In ARFN, we adopt a fuzzy recursive module which employs a feature attention mechanism and a fuzzy rule attention mechanism, respectively, to flexibly extract EEG features related to mental workload. The former can extract the frequency domain features of EEG signals, while the latter is used to represent the membership degrees within the distribution of frequency features, so as to find effective fuzzy rules for classification. Subsequently, the output of the fuzzy recursive module is directed into the long short-term memory (LSTM) to further extract temporal features of EEG, followed by a fully connected layer and the Softmax function for classification. The experimental results on three public datasets show that ARFN outperforms other state-of-the-art models in EEG mental workload assessment.