Dynamic stopping using eSVM scores analysis for event-related potential brain-computer interfaces
- Publication Type:
- Conference Proceeding
- Citation:
- International Symposium on Medical Information and Communication Technology, ISMICT, 2017, 0 pp. 82 - 85
- Issue Date:
- 2017-04-03
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 07891773.pdf | Published version | 291.08 kB | |||
| OCC-107147_AM.pdf | Accepted Manuscript version | 292.75 kB |
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© 2017 IEEE. In brain-computer interface (BCI) research, there must be a trade-off between accuracy and speed of the BCI system, especially those based on event-related potentials (ERPs). This paper proposes a novel method which can significantly increase the spelling bit rate while also maintaining the desired accuracy. We provide an adaptive real-time stopping method based on the scores of ensemble support vector machine classifiers. We apply a criteria assessment process on the classifiers' scores to dynamically stop the ERP-evoked paradigms at any flashing sequence. Our experiments were conducted on three different P300-Speller data sets (BCI Competition II, BCI Competition III and Akimpech). Our proposed framework significantly outperformed the related state-of-the-art studies in terms of character output accuracy and elicitation bit rate rise between static and dynamic stopping schemes. We improve the average bit rate by over 80% while perfectly maintaining the best original static accuracy of over 96%.
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