Brain-Computer Interface (BCI) enables human to communicate with and intuitively control an external device through brain signals. Movement intention recognition paves the path for developing BCI applications. The current state-of-the-art in EEG based BCI usually involves subject-specific adaptation before ready to use. However, the subject-independent scenario, in which a well-trained model is directly applied to new subjects without any pre-calibration, is particularly desired yet rarely explored. In order to fill the gap, we present a Convolutional Attention Model (CAM) for EEG-based human movement intention recognition in the subject-independent scenario. The convolutional network is designed to capture the spatio-temporal features of EEG signals, while the integrated attention mechanism is utilized to focus on the most discriminative information of EEG signals during the period of movement imagination while omitting other less relative parts. Experiments conducted on a real-world EEG dataset containing 55 subjects show that our model is capable of mining the underlying invariant EEG patterns across different subjects and generalizing to unseen subjects. Our model achieves better performance than a series of state-of-the-art and baseline approaches.
|Titel||CIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management|
|Redaktører||Norman Paton, Selcuk Candan, Haixun Wang, James Allan, Rakesh Agrawal, Alexandros Labrinidis, Alfredo Cuzzocrea, Mohammed Zaki, Divesh Srivastava, Andrei Broder, Assaf Schuster|
|Forlag||Association for Computing Machinery|
|Publikationsdato||17 okt. 2018|
|Status||Udgivet - 17 okt. 2018|
|Begivenhed||27th ACM International Conference on Information and Knowledge Management, CIKM 2018 - Torino, Italien|
Varighed: 22 okt. 2018 → 26 okt. 2018
|Konference||27th ACM International Conference on Information and Knowledge Management, CIKM 2018|
|Periode||22/10/2018 → 26/10/2018|
|Sponsor||ACM SIGIR, ACM SIGWEB|
|Navn||International Conference on Information and Knowledge Management, Proceedings|
Bibliografisk notePublisher Copyright:
© 2018 Association for Computing Machinery.