Paper Info
Title
Ready for Use: Subject-Independent Movement Intention Recognition via a Convolutional Attention Model.
Abstract
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.
Year
DOI
Venue
2018
10.1145/3269206.3269259
CIKM
Keywords
Field
DocType
Electroencephalogram (EEG), Subject Independent, Movement Intention Recognition, Convolutional Attention Model
Data mining,Computer science,Generalization,Brain–computer interface,Attention model,Speech recognition,Invariant (mathematics),Discriminative model,Electroencephalography
Conference
ISBN
Citations 
PageRank 
978-1-4503-6014-2
2
0.36
References 
Authors
5
4
Name
Order
Citations
PageRank
Dalin Zhang1446.16
Lina Yao298193.63
Kaixuan Chen3474.80
Sen Wang447737.24