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Basic and Clinical Neuroscience، جلد ۱۰، شماره ۳، صفحات ۲۴۵-۲۵۶
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| عنوان فارسی |
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| چکیده فارسی مقاله |
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| کلیدواژه‌های فارسی مقاله |
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| عنوان انگلیسی |
Comparing Steady-State Visually Evoked Potentials Frequency Estimation Methods in Brain-Computer Interface With the Minimum Number of EEG Channels |
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| چکیده انگلیسی مقاله |
Introduction: Brain-Computer Interface (BCI) systems provide a communication pathway between users and systems. BCI systems based on Steady-State Visually Evoked Potentials (SSVEP) are widely used in recent decades. Different feature extraction methods have been introduced in the literature to estimate SSVEP responses to BCI applications. Methods: In this study, the new algorithms, including Canonical Correlation Analysis (CCA), Least Absolute Shrinkage and Selection Operator (LASSO), L1-regularized Multi-way CCA (L1-MCCA), Multi-set CCA (MsetCCA), Common Feature Analysis (CFA), and Multiple Logistic Regression (MLR) are compared using proper statistical methods to determine which one has better performance with the least number of EEG electrodes. Results: It was found that MLR, MsetCCA, and CFA algorithms provided the highest performances and significantly outperformed CCA, LASSO, and L1-MCCA algorithms when using 8 EEG channels. However, when using only 1 or 2 EEG channels d, CFA method provided the highest F-scores. This algorithm not only outperformed MLR and MsetCCA when applied on different electrode montages but also provided the fastest computation time on the test set. Conclusion: Although MLR method has already demonstrated to have higher performance in comparison with other frequency recognition algorithms, this study showed that in a practical SSVEP-based BCI system with 1 or 2 EEG channels and short-time windows, CFA method outperforms other algorithms. Therefore, it is proposed that CFA algorithm is a promising choice for the expansion of practical SSVEP-based BCI systems. |
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| کلیدواژه‌های انگلیسی مقاله |
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| نویسندگان مقاله |
| Mehrnoosh Neghabi
Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran.
| Hamid Reza Marateb
Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran.
| Amin Mahnam
Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran.
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| نشانی اینترنتی |
http://bcn.iums.ac.ir/browse.php?a_code=A-10-1153-1&slc_lang=en&sid=1 |
| فایل مقاله |
اشکال در دسترسی به فایل - ./files/site1/rds_journals/198/article-198-1508493.pdf |
| کد مقاله (doi) |
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| زبان مقاله منتشر شده |
en |
| موضوعات مقاله منتشر شده |
Computational Neuroscience |
| نوع مقاله منتشر شده |
Original |
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