Using Hybrid Feature and Classifier Fusion for an Asynchronous Brain–Computer Interface Framework Based on Steady-State Motion Visual Evoked Potentials
This study proposes an asynchronous brain–computer interface (BCI) framework based on steady-state motion visual evoked potentials (SSMVEPs), designed to enhance the accuracy and robustness of control state recognition. The method integrates filter bank common spatial patterns (FBCSPs) and filter ba...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/11/6010 |
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| author | Bo Hu Jun Xie Huanqing Zhang Junjie Liu Hu Wang |
| author_facet | Bo Hu Jun Xie Huanqing Zhang Junjie Liu Hu Wang |
| author_sort | Bo Hu |
| collection | DOAJ |
| description | This study proposes an asynchronous brain–computer interface (BCI) framework based on steady-state motion visual evoked potentials (SSMVEPs), designed to enhance the accuracy and robustness of control state recognition. The method integrates filter bank common spatial patterns (FBCSPs) and filter bank canonical correlation analysis (FBCCA) to extract complementary spatial and frequency domain features from EEG signals. These multimodal features are then fused and input into a dual-classifier structure consisting of a support vector machine (SVM) and extreme gradient boosting (XGBoost). A weighted fusion strategy is applied to combine the probabilistic outputs of both classifiers, allowing the system to leverage their respective strengths. Experimental results demonstrate that the fused FB(CSP + CCA)-(SVM + XGBoost) model achieves superior performance in distinguishing intentional control (IC) and non-control (NC) states compared to models using a single feature type or classifier. Furthermore, the visualization of feature distributions using UMAP shows improved inter-class separability when combining FBCSP and FBCCA features. These findings confirm the effectiveness of both feature-level and classifier-level fusion in asynchronous BCI systems. The proposed approach offers a promising and practical solution for developing more reliable and user-adaptive BCI applications, particularly in real-world environments requiring flexible control without external cues. |
| format | Article |
| id | doaj-art-9bf2ba494ce149cc841bdd48ed1fb488 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-9bf2ba494ce149cc841bdd48ed1fb4882025-08-20T02:33:06ZengMDPI AGApplied Sciences2076-34172025-05-011511601010.3390/app15116010Using Hybrid Feature and Classifier Fusion for an Asynchronous Brain–Computer Interface Framework Based on Steady-State Motion Visual Evoked PotentialsBo Hu0Jun Xie1Huanqing Zhang2Junjie Liu3Hu Wang4School of Mechanical Engineering, Xinjiang University, Urumqi 830017, ChinaSchool of Mechanical Engineering, Xinjiang University, Urumqi 830017, ChinaSchool of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Mechanical Engineering, Xinjiang University, Urumqi 830017, ChinaSchool of Mechanical Engineering, Xinjiang University, Urumqi 830017, ChinaThis study proposes an asynchronous brain–computer interface (BCI) framework based on steady-state motion visual evoked potentials (SSMVEPs), designed to enhance the accuracy and robustness of control state recognition. The method integrates filter bank common spatial patterns (FBCSPs) and filter bank canonical correlation analysis (FBCCA) to extract complementary spatial and frequency domain features from EEG signals. These multimodal features are then fused and input into a dual-classifier structure consisting of a support vector machine (SVM) and extreme gradient boosting (XGBoost). A weighted fusion strategy is applied to combine the probabilistic outputs of both classifiers, allowing the system to leverage their respective strengths. Experimental results demonstrate that the fused FB(CSP + CCA)-(SVM + XGBoost) model achieves superior performance in distinguishing intentional control (IC) and non-control (NC) states compared to models using a single feature type or classifier. Furthermore, the visualization of feature distributions using UMAP shows improved inter-class separability when combining FBCSP and FBCCA features. These findings confirm the effectiveness of both feature-level and classifier-level fusion in asynchronous BCI systems. The proposed approach offers a promising and practical solution for developing more reliable and user-adaptive BCI applications, particularly in real-world environments requiring flexible control without external cues.https://www.mdpi.com/2076-3417/15/11/6010asynchronous BCISSMVEPfeature fusionensemble classification |
| spellingShingle | Bo Hu Jun Xie Huanqing Zhang Junjie Liu Hu Wang Using Hybrid Feature and Classifier Fusion for an Asynchronous Brain–Computer Interface Framework Based on Steady-State Motion Visual Evoked Potentials Applied Sciences asynchronous BCI SSMVEP feature fusion ensemble classification |
| title | Using Hybrid Feature and Classifier Fusion for an Asynchronous Brain–Computer Interface Framework Based on Steady-State Motion Visual Evoked Potentials |
| title_full | Using Hybrid Feature and Classifier Fusion for an Asynchronous Brain–Computer Interface Framework Based on Steady-State Motion Visual Evoked Potentials |
| title_fullStr | Using Hybrid Feature and Classifier Fusion for an Asynchronous Brain–Computer Interface Framework Based on Steady-State Motion Visual Evoked Potentials |
| title_full_unstemmed | Using Hybrid Feature and Classifier Fusion for an Asynchronous Brain–Computer Interface Framework Based on Steady-State Motion Visual Evoked Potentials |
| title_short | Using Hybrid Feature and Classifier Fusion for an Asynchronous Brain–Computer Interface Framework Based on Steady-State Motion Visual Evoked Potentials |
| title_sort | using hybrid feature and classifier fusion for an asynchronous brain computer interface framework based on steady state motion visual evoked potentials |
| topic | asynchronous BCI SSMVEP feature fusion ensemble classification |
| url | https://www.mdpi.com/2076-3417/15/11/6010 |
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