Name
Abstract | ||
|---|---|---|
An error-related potentials (ErrP) is generated in the brain when human's expectations are inconsistent with actual results. The decoding of ErrP can improve the performance of brain-computer systems (BCI). In this paper, we propose an effective ErrP classification method using the proposed attention-based convolutional neural network (AT-CNN). Every 1D EEG signal is transformed into a 2D grayscale image as an input data for the model. In addition, we introduced label smoothing to mitigate the impact of label mismatching data. We evaluate and compare our method using the Monitoring Error-Related Potential dataset. The accuracy of our proposed method is 83.42%, the sensitivity is 69.02%, the specificity is 88.48% and these results outperform the state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/978-3-031-13822-5_64 | INTELLIGENT ROBOTICS AND APPLICATIONS (ICIRA 2022), PT II |
Keywords | DocType | Volume |
Brain-computer interface (BCI), Error-related potential (ErrP), 2D grayscale image | Conference | 13456 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuxiang Gao | 1 | 0 | 0.34 |
| Tangfei Tao | 2 | 0 | 1.01 |
| Yaguang Jia | 3 | 0 | 0.68 |