Steady-State Visually Evoked Magnetic Signal Classification and BCI Evaluation Based on a Convolutional Neural Network
The steady-state visually evoked magnetic field (SSVEF) is a promising modality in brain-computer interference (BCI), which has the advantages of being non-invasive and non-contact. The combination of optically pumped magnetometers (OPM) and artificial intelligence technology makes SSVEF measurement...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10818692/ |
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| author | Yutong Wei Fudan Zhao Fengwen Zhao Shiqiang Zheng Chaofeng Ye Liangyu Liu |
| author_facet | Yutong Wei Fudan Zhao Fengwen Zhao Shiqiang Zheng Chaofeng Ye Liangyu Liu |
| author_sort | Yutong Wei |
| collection | DOAJ |
| description | The steady-state visually evoked magnetic field (SSVEF) is a promising modality in brain-computer interference (BCI), which has the advantages of being non-invasive and non-contact. The combination of optically pumped magnetometers (OPM) and artificial intelligence technology makes SSVEF measurements more portable, accurate, and cost-effective. This paper examines the distribution of the human brain visually evoked magnetic field experimentally and then presents an SSVEF measurement system based on an OPM. A three-block temporal convolutional neural network (3B-TCN) is developed to classify brain magnetic signals. A data augmentation method based on statistical analysis of SSVEF signals is proposed, which generates 30,000 sets of data to train the 3B-TCN. The SSVEF signal classification accuracies of the 3B-TCN network are 96.61%, 92.36%, and 86.75% for 10 s, 5 s, and 2 s time length data, respectively. The impact of visually fatigued states on BCI is studied. The accuracy of controlling the character in the game is above 90% when the subjects are in a normal state, but it decreases considerably when the subjects are visually fatigued. The experimental results demonstrate the feasibility of realizing BCI using an OPM sensor and a convolutional neural network. |
| format | Article |
| id | doaj-art-6bb428a93b3f4920a0c63d0b1b4cd0c9 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-6bb428a93b3f4920a0c63d0b1b4cd0c92025-08-20T02:20:23ZengIEEEIEEE Access2169-35362025-01-0113686226863110.1109/ACCESS.2024.352439710818692Steady-State Visually Evoked Magnetic Signal Classification and BCI Evaluation Based on a Convolutional Neural NetworkYutong Wei0https://orcid.org/0000-0003-0431-3621Fudan Zhao1Fengwen Zhao2Shiqiang Zheng3Chaofeng Ye4Liangyu Liu5National Institute of Extremely-Weak Magnetic Field Infrastructure, Hangzhou, ChinaInstitute of Large-Scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Beijing, ChinaNational Institute of Extremely-Weak Magnetic Field Infrastructure, Hangzhou, ChinaInstitute of Large-Scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Beijing, ChinaSchool of Information Science and Technology, ShanghaiTech University, Shanghai, ChinaSchool of Pharmaceutical Sciences, Peking University, Beijing, ChinaThe steady-state visually evoked magnetic field (SSVEF) is a promising modality in brain-computer interference (BCI), which has the advantages of being non-invasive and non-contact. The combination of optically pumped magnetometers (OPM) and artificial intelligence technology makes SSVEF measurements more portable, accurate, and cost-effective. This paper examines the distribution of the human brain visually evoked magnetic field experimentally and then presents an SSVEF measurement system based on an OPM. A three-block temporal convolutional neural network (3B-TCN) is developed to classify brain magnetic signals. A data augmentation method based on statistical analysis of SSVEF signals is proposed, which generates 30,000 sets of data to train the 3B-TCN. The SSVEF signal classification accuracies of the 3B-TCN network are 96.61%, 92.36%, and 86.75% for 10 s, 5 s, and 2 s time length data, respectively. The impact of visually fatigued states on BCI is studied. The accuracy of controlling the character in the game is above 90% when the subjects are in a normal state, but it decreases considerably when the subjects are visually fatigued. The experimental results demonstrate the feasibility of realizing BCI using an OPM sensor and a convolutional neural network.https://ieeexplore.ieee.org/document/10818692/Bio-magnetic field measurementoptically pumped magnetometerconvolutional neural networkbrain-computer interference |
| spellingShingle | Yutong Wei Fudan Zhao Fengwen Zhao Shiqiang Zheng Chaofeng Ye Liangyu Liu Steady-State Visually Evoked Magnetic Signal Classification and BCI Evaluation Based on a Convolutional Neural Network IEEE Access Bio-magnetic field measurement optically pumped magnetometer convolutional neural network brain-computer interference |
| title | Steady-State Visually Evoked Magnetic Signal Classification and BCI Evaluation Based on a Convolutional Neural Network |
| title_full | Steady-State Visually Evoked Magnetic Signal Classification and BCI Evaluation Based on a Convolutional Neural Network |
| title_fullStr | Steady-State Visually Evoked Magnetic Signal Classification and BCI Evaluation Based on a Convolutional Neural Network |
| title_full_unstemmed | Steady-State Visually Evoked Magnetic Signal Classification and BCI Evaluation Based on a Convolutional Neural Network |
| title_short | Steady-State Visually Evoked Magnetic Signal Classification and BCI Evaluation Based on a Convolutional Neural Network |
| title_sort | steady state visually evoked magnetic signal classification and bci evaluation based on a convolutional neural network |
| topic | Bio-magnetic field measurement optically pumped magnetometer convolutional neural network brain-computer interference |
| url | https://ieeexplore.ieee.org/document/10818692/ |
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