Journal of neuroscience methods · 2021
Enhancing P300 based character recognition performance using a combination of ensemble classifiers and a fuzzy fusion method.
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Li Shurui, Jin Jing, Daly Ian, Wang Xingyu, Lam Hak-Keung, Cichocki Andrzej
Аннотация
BACKGROUND: P300-based brain-computer interfaces provide communication pathways without the need for muscle activity by recognizing electrical signals from the brain. The P300 speller is one of the most commonly used BCI applications, as it is very simple and reliable, and it is capable of reaching satisfactory communication performance. However, as with other BCIs, it remains a challenge to improve the P300 speller's performance to increase its practical usability.
NEW METHODS: In this study, we propose a novel multi-feature subset fuzzy fusion (MSFF) framework for the P300 speller to recognize the users' spelling intention. This method includes two parts: 1) feature selection by the Lasso algorithm and feature division; 2) the construction of ensemble LDA classifiers and the fuzzy fusion of those classifiers to recognize user intention.
RESULTS: The proposed framework is evaluated in three public datasets and achieves an average accuracy of 100% after 4 epochs for BCI Competition II Dataset IIb, 96% for BCI Competition III dataset II and 98.3% for the BNCI Horizon Dataset. It indicates that the proposed MSFF method can make use of temporal information of signals and helps to enhance classification performance.
COMPARISON WITH EXISTING METHODS: The proposed MSFF method yields better or comparable performance than previously reported machine learning algorithms.
CONCLUSIONS: The proposed MSFF method is able to improve the performance of P300-based BCIs.
NEW METHODS: In this study, we propose a novel multi-feature subset fuzzy fusion (MSFF) framework for the P300 speller to recognize the users' spelling intention. This method includes two parts: 1) feature selection by the Lasso algorithm and feature division; 2) the construction of ensemble LDA classifiers and the fuzzy fusion of those classifiers to recognize user intention.
RESULTS: The proposed framework is evaluated in three public datasets and achieves an average accuracy of 100% after 4 epochs for BCI Competition II Dataset IIb, 96% for BCI Competition III dataset II and 98.3% for the BNCI Horizon Dataset. It indicates that the proposed MSFF method can make use of temporal information of signals and helps to enhance classification performance.
COMPARISON WITH EXISTING METHODS: The proposed MSFF method yields better or comparable performance than previously reported machine learning algorithms.
CONCLUSIONS: The proposed MSFF method is able to improve the performance of P300-based BCIs.