Name
Abstract | ||
|---|---|---|
With the development of wearable technology, portable wireless systems have been used gradually for collecting electroencephalogram (EEG) signals. However, the introduction of portable collection devices always means a descent in signal-to-noise ratio (SNR) of EEG. Subject-independent brain-computer interface (BCI) avoids conventional calibration procedure for new users. However, whether subject-independent model can be used in cross-platform BCI has not been discussed so far. This paper transplanted the subject-independent model from a high-SNR platform to a lower one for recognition in P300-Speller. After comparing their EEG features elicited from diverse collection platforms, a model adjustment strategy was proposed to increase recognition accuracy. By model adjustment, the average accuracy was 85.00% in online spell experiments. The results indicate it is feasible for subject-independent model transplantation, especially after model adjustment strategy. It provides technology supported for further development of cross-platform BCI. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s13042-016-0620-1 | Int. J. Machine Learning & Cybernetics |
Keywords | Field | DocType |
Subject-independent BCI, ERP, P300-Speller, Cross-platform, LDA | Wireless systems,Computer science,Brain–computer interface,Speech recognition,Cross-platform,Wearable technology,Transplantation,Electroencephalography | Journal |
Volume | Issue | ISSN |
9 | 6 | 1868-808X |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yawei Zhao | 1 | 14 | 6.74 |
| Zhongpeng Wang | 2 | 0 | 4.06 |
| Zhen Zhang | 3 | 394 | 62.54 |
| Jing Liu | 4 | 135 | 45.52 |
| Long Chen | 5 | 0 | 3.04 |
| Hongzhi Qi | 6 | 49 | 20.61 |
| Xuejun Jiao | 7 | 0 | 2.03 |
| Feng He | 8 | 16 | 9.45 |
| Peng Zhou | 9 | 13 | 6.25 |
| Dong Ming | 10 | 105 | 51.47 |