Combining multi-scale convolutional neural network and Transformers for EEG-Based RSVP detection
Rapid serial visual presentation (RSVP) is an effective brain-computer interface (BCI) technique for recognizing target objects. Decoding the subject's intention from the single-trial electroencephalogram (EEG) signal through a decoding algorithm is the key to RSVP-based BCI. The unavoidable no...
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| Vydáno v: | 2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC) s. 426 - 431 |
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IEEE
19.11.2022
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| Abstract | Rapid serial visual presentation (RSVP) is an effective brain-computer interface (BCI) technique for recognizing target objects. Decoding the subject's intention from the single-trial electroencephalogram (EEG) signal through a decoding algorithm is the key to RSVP-based BCI. The unavoidable noise and variability between trials in EEG signals lead to low accuracy of EEG-based RSVP detection and low universality of the model. It is necessary to develop an EEG decoding algorithm with robust generalization ability and high recognition accuracy. In this study, we proposed a novel end-to-end model architecture that combines multi-scale spatiotemporal convolutional neural network (CNN) and Transformers. Specifically, the multi-scale CNN is used to capture spatiotemporal features at different scales, while the Transformers are used to extract the most discriminative global information. Experimental results on the RSVP-based benchmark datasets show that the proposed method in this study can achieve higher recognition accuracy compared to the other three advanced methods in both cross-subject and within-subject experiments. The results of fine-tuning experiments using pre-trained models on a new subject show that better results can be obtained in single-subject experiments using only a small amount of data. The experimental results validate the effectiveness of our method and provide a new idea for constructing a feature extraction method with better generalization capability for RSVP-based BCI. |
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| AbstractList | Rapid serial visual presentation (RSVP) is an effective brain-computer interface (BCI) technique for recognizing target objects. Decoding the subject's intention from the single-trial electroencephalogram (EEG) signal through a decoding algorithm is the key to RSVP-based BCI. The unavoidable noise and variability between trials in EEG signals lead to low accuracy of EEG-based RSVP detection and low universality of the model. It is necessary to develop an EEG decoding algorithm with robust generalization ability and high recognition accuracy. In this study, we proposed a novel end-to-end model architecture that combines multi-scale spatiotemporal convolutional neural network (CNN) and Transformers. Specifically, the multi-scale CNN is used to capture spatiotemporal features at different scales, while the Transformers are used to extract the most discriminative global information. Experimental results on the RSVP-based benchmark datasets show that the proposed method in this study can achieve higher recognition accuracy compared to the other three advanced methods in both cross-subject and within-subject experiments. The results of fine-tuning experiments using pre-trained models on a new subject show that better results can be obtained in single-subject experiments using only a small amount of data. The experimental results validate the effectiveness of our method and provide a new idea for constructing a feature extraction method with better generalization capability for RSVP-based BCI. |
| Author | Liu, Yingxin Yu, Yang Zhang, Yifan Lu, Gai Chu, Xingxing |
| Author_xml | – sequence: 1 givenname: Gai surname: Lu fullname: Lu, Gai organization: National University of Defense Technology,College of Intelligence Science and Technology,ChangSha,China – sequence: 2 givenname: Yifan surname: Zhang fullname: Zhang, Yifan organization: National University of Defense Technology,College of Intelligence Science and Technology,ChangSha,China – sequence: 3 givenname: Xingxing surname: Chu fullname: Chu, Xingxing organization: National University of Defense Technology,College of Intelligence Science and Technology,ChangSha,China – sequence: 4 givenname: Yingxin surname: Liu fullname: Liu, Yingxin organization: National University of Defense Technology,College of Intelligence Science and Technology,ChangSha,China – sequence: 5 givenname: Yang surname: Yu fullname: Yu, Yang email: yuyangnudt@hotmail.com organization: National University of Defense Technology,College of Intelligence Science and Technology,ChangSha,China |
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| Snippet | Rapid serial visual presentation (RSVP) is an effective brain-computer interface (BCI) technique for recognizing target objects. Decoding the subject's... |
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| SubjectTerms | Benchmark testing Brain modeling EEG decoding algorithm electroencephalogram(EEG) Electroencephalography Feature extraction multi-scale Target recognition Transformers Visualization |
| Title | Combining multi-scale convolutional neural network and Transformers for EEG-Based RSVP detection |
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