Cross Task Neural Architecture Search for EEG Signal Classifications
This work addresses the inefficiency of task-specific manual network design in EEG-based brain-computer interfaces, offering an automated solution that improves accuracy, though it is incremental as it applies existing NAS concepts to EEG data.
The paper tackles the problem of manually designing neural network structures for different EEG signal classification tasks by proposing a cross-task neural architecture search (CTNAS-EEG) framework, which automatically designs network structures across tasks and achieves state-of-the-art performance on tasks like Motor Imagery and Emotion recognition.
Electroencephalograms (EEGs) are brain dynamics measured outside the brain, which have been widely utilized in non-invasive brain-computer interface applications. Recently, various neural network approaches have been proposed to improve the accuracy of EEG signal recognition. However, these approaches severely rely on manually designed network structures for different tasks which generally are not sharing the same empirical design cross-task-wise. In this paper, we propose a cross-task neural architecture search (CTNAS-EEG) framework for EEG signal recognition, which can automatically design the network structure across tasks and improve the recognition accuracy of EEG signals. Specifically, a compatible search space for cross-task searching and an efficient constrained searching method is proposed to overcome challenges brought by EEG signals. By unifying structure search on different EEG tasks, this work is the first to explore and analyze the searched structure difference cross-task-wise. Moreover, by introducing architecture search, this work is the first to analyze model performance by customizing model structure for each human subject. Detailed experimental results suggest that the proposed CTNAS-EEG could reach state-of-the-art performance on different EEG tasks, such as Motor Imagery (MI) and Emotion recognition. Extensive experiments and detailed analysis are provided as a good reference for follow-up researchers.