Improving the Performance of Individually Calibrated SSVEP Classification by Rhythmic Entrainment Source Separation
| Autor: | Wei Xu, Yufeng Ke, Dong Ming |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 32, Pp 4284-4293 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1534-4320 1558-0210 |
| DOI: | 10.1109/TNSRE.2024.3503772 |
| Popis: | The supervised decoding algorithms of Steady-State Visual Evoked Potentials (SSVEP) have achieved remarkable performance with sufficient training data. However, these methods have typically failed to achieve acceptable performance in single-trial training scenarios. To address this challenge, we propose a method to enhance SSVEP classification performance using less training data by employing Rhythmic Entrainment Source Separation (RESS) to construct spatial filters. We evaluate RESS alongside other state-of-the-art methods using two distinct datasets to assess their effectiveness. Our results indicate that RESS significantly outperforms other advanced algorithms when trained with a single block of calibration data. Specifically, compared to task-related component analysis, the RESS-based method improves average classification accuracy by 49.81% and 59.06% on the two datasets using 1-second EEG segments. The RESS-based method can significantly improve SSVEP classification performance with limited training data. RESS holds promise for practical applications in SSVEP-based BCIs, offering a novel solution to reduce the calibration data requirements for individually calibrated systems. |
| Databáze: | Directory of Open Access Journals |
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