Detection of control or idle state with a likelihood ratio test in asynchronous SSVEP-based brain-computer interface systems
| Autor: | Yufei Huang, Lenis Mauricio Merino, Garrett Hall, Daniel Pack, Tapsya Nayak |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Computer science
Speech recognition 0206 medical engineering Word error rate 02 engineering and technology Electroencephalography 03 medical and health sciences Idle 0302 clinical medicine Robustness (computer science) Histogram medicine Humans Evoked potential Evoked Potentials Brain–computer interface medicine.diagnostic_test 020601 biomedical engineering Support vector machine ComputingMethodologies_PATTERNRECOGNITION Asynchronous communication Sample size determination Likelihood-ratio test Brain-Computer Interfaces Evoked Potentials Visual 030217 neurology & neurosurgery Algorithms |
| Zdroj: | EMBC |
| ISSN: | 2694-0604 |
| Popis: | We consider the detection of the control or idle state in an asynchronous Steady-state visually evoked potential (SSVEP)-based brain computer interface system. We propose a likelihood ratio test using Canonical Correlation Analysis (CCA) scores calculated from the EEG measurements. The test exploits the state-specific distributions of CCA scores. The algorithm was tested on offline measurements from 42 participants and the results should a significant improvement in detection error rate over the support vector machine classifier. The proposed test is also shown to be robust against training sample size. |
| Databáze: | OpenAIRE |
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