Eigenface analysis for brain signal classification: a novel algorithm
| Autor: | Yeon Mo Yang, Wansu Lim, Byeong Man Kim |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Scheme (programming language)
business.industry Computer science Speech recognition Feature extraction Pattern recognition Signal classification Eigenface Artificial intelligence business Projection (set theory) Algorithm computer Statistical hypothesis testing computer.programming_language Brain–computer interface |
| Zdroj: | International Journal of Telemedicine and Clinical Practices. 2:148 |
| ISSN: | 2052-8442 2052-8434 |
| DOI: | 10.1504/ijtmcp.2017.083887 |
| Popis: | This paper proposes a novel feature extraction scheme utilising an Eigenface analysis (EFA) algorithm for a brain computer interface (BCI). In EFA, the obtained BCI data is systematically rearranged into time, channels, and trials to develop neuro-images. Based on these images, the scheme extracts Eigenfaces with a training dataset and utilises the cross-correlation to find the coefficients of projection. Compared to the existing scheme, EFA outperforms in accuracy with BCI competition III, dataset IIIa. Specifically, the accuracy improves by 27.21% for the second subject. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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