Name
Abstract | ||
|---|---|---|
Functional near-infrared spectroscopy (fNIRS) is a promising neuroimaging technology. The fNIRS classification problem has always been the focus of the brain-computer interface (BCI). Inspired by the success of Transformer based on self-attention mechanism in the fields of natural language processing and computer vision, we propose an fNIRS classification network based on Transformer, named fNIRS-T. We explore the spatial-level and channel-level representation of fNIRS signals to improve data utilization and network representation capacity. Besides, a preprocessing module, which consists of one-dimensional average pooling and layer normalization, is designed to replace filtering and baseline correction of data preprocessing. It makes fNIRS-T an end-to-end network, called fNIRS-PreT. Compared with traditional machine learning classifiers, convolutional neural network (CNN), and long short-term memory (LSTM), the proposed models obtain the best accuracy on three open-access datasets. Specifically, in the most extensive ternary classification task (30 subjects) that includes three types of overt movements, fNIRS-T, CNN, and LSTM obtain 75.49%, 72.89%, and 61.94% on test sets, respectively. Compared to traditional classifiers, fNIRS-T is at least 27.41% higher than statistical features and 6.79% higher than well-designed features. In the individual subject experiment of the ternary classification task, fNIRS-T achieves an average subject accuracy of 78.22% and surpasses CNN and LSTM by a large margin of +4.75% and +11.33%. fNIRS-PreT using raw data also achieves competitive performance to fNIRS-T. Therefore, the proposed models improve the performance of fNIRS-based BCI significantly. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/JBHI.2022.3140531 | IEEE Journal of Biomedical and Health Informatics |
Keywords | DocType | Volume |
Brain-Computer Interfaces,Humans,Movement,Neural Networks, Computer,Spectroscopy, Near-Infrared | Journal | 26 |
Issue | ISSN | Citations |
6 | 2168-2194 | 0 |
PageRank | References | Authors |
0.34 | 9 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zenghui Wang | 1 | 0 | 0.34 |
| Jun Zhang | 2 | 115 | 18.02 |
| Xiaochu Zhang | 3 | 17 | 5.79 |
| Peng Chen | 4 | 0 | 0.34 |
| Bing Wang | 5 | 0 | 0.34 |