Recognition of motor intentions from EEGs of the same upper limb by signal traceability and Riemannian geometry features
IntroductionThe electroencephalographic (EEG) based on the motor imagery task is derived from the physiological electrical signal caused by the autonomous activity of the brain. Its weak potential difference changes make it easy to be overwhelmed by noise, and the EEG acquisition method has a natura...
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| Format: | Article |
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Frontiers Media S.A.
2023-10-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2023.1270785/full |
| _version_ | 1827779146951950336 |
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| author | Meng Zhang Meng Zhang Jinfeng Huang Jinfeng Huang Shoudong Ni |
| author_facet | Meng Zhang Meng Zhang Jinfeng Huang Jinfeng Huang Shoudong Ni |
| author_sort | Meng Zhang |
| collection | DOAJ |
| description | IntroductionThe electroencephalographic (EEG) based on the motor imagery task is derived from the physiological electrical signal caused by the autonomous activity of the brain. Its weak potential difference changes make it easy to be overwhelmed by noise, and the EEG acquisition method has a natural limitation of low spatial resolution. These have brought significant obstacles to high-precision recognition, especially the recognition of the motion intention of the same upper limb.MethodsThis research proposes a method that combines signal traceability and Riemannian geometric features to identify six motor intentions of the same upper limb, including grasping/holding of the palm, flexion/extension of the elbow, and abduction/adduction of the shoulder. First, the EEG data of electrodes irrelevant to the task were screened out by low-resolution brain electromagnetic tomography. Subsequently, tangential spatial features are extracted by the Riemannian geometry framework in the covariance matrix estimated from the reconstructed EEG signals. The learned Riemannian geometric features are used for pattern recognition by a support vector machine with a linear kernel function.ResultsThe average accuracy of the six classifications on the data set of 15 participants is 22.47%, the accuracy is 19.34% without signal traceability, the accuracy is 18.07% when the features are the filter bank common spatial pattern (FBCSP), and the accuracy is 16.7% without signal traceability and characterized by FBCSP.DiscussionThe results show that the proposed method can significantly improve the accuracy of intent recognition. In addressing the issue of temporal variability in EEG data for active Brain-Machine Interfaces, our method achieved an average standard deviation of 2.98 through model transfer on different days’ data. |
| first_indexed | 2024-03-11T14:46:23Z |
| format | Article |
| id | doaj.art-8c1b91f6b330404491ad18c7f303ee43 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-03-11T14:46:23Z |
| publishDate | 2023-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-8c1b91f6b330404491ad18c7f303ee432023-10-30T11:08:25ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-10-011710.3389/fnins.2023.12707851270785Recognition of motor intentions from EEGs of the same upper limb by signal traceability and Riemannian geometry featuresMeng Zhang0Meng Zhang1Jinfeng Huang2Jinfeng Huang3Shoudong Ni4School of Mechanical and Power Engineering, Nanjing Tech University Nanjing, Nanjing, ChinaResearch Institute, NeuralEcho Technology Co., Ltd., Beijing, ChinaFaculty of Human Sciences, University of Tsukuba, Ibaraki, JapanResearch Institute, NeuralEcho Technology Co., Ltd., Beijing, ChinaSchool of Mechanical and Power Engineering, Nanjing Tech University Nanjing, Nanjing, ChinaIntroductionThe electroencephalographic (EEG) based on the motor imagery task is derived from the physiological electrical signal caused by the autonomous activity of the brain. Its weak potential difference changes make it easy to be overwhelmed by noise, and the EEG acquisition method has a natural limitation of low spatial resolution. These have brought significant obstacles to high-precision recognition, especially the recognition of the motion intention of the same upper limb.MethodsThis research proposes a method that combines signal traceability and Riemannian geometric features to identify six motor intentions of the same upper limb, including grasping/holding of the palm, flexion/extension of the elbow, and abduction/adduction of the shoulder. First, the EEG data of electrodes irrelevant to the task were screened out by low-resolution brain electromagnetic tomography. Subsequently, tangential spatial features are extracted by the Riemannian geometry framework in the covariance matrix estimated from the reconstructed EEG signals. The learned Riemannian geometric features are used for pattern recognition by a support vector machine with a linear kernel function.ResultsThe average accuracy of the six classifications on the data set of 15 participants is 22.47%, the accuracy is 19.34% without signal traceability, the accuracy is 18.07% when the features are the filter bank common spatial pattern (FBCSP), and the accuracy is 16.7% without signal traceability and characterized by FBCSP.DiscussionThe results show that the proposed method can significantly improve the accuracy of intent recognition. In addressing the issue of temporal variability in EEG data for active Brain-Machine Interfaces, our method achieved an average standard deviation of 2.98 through model transfer on different days’ data.https://www.frontiersin.org/articles/10.3389/fnins.2023.1270785/fullEEG source localizationmotor imageryRiemannian geometrysame upper limbtemporal variability |
| spellingShingle | Meng Zhang Meng Zhang Jinfeng Huang Jinfeng Huang Shoudong Ni Recognition of motor intentions from EEGs of the same upper limb by signal traceability and Riemannian geometry features Frontiers in Neuroscience EEG source localization motor imagery Riemannian geometry same upper limb temporal variability |
| title | Recognition of motor intentions from EEGs of the same upper limb by signal traceability and Riemannian geometry features |
| title_full | Recognition of motor intentions from EEGs of the same upper limb by signal traceability and Riemannian geometry features |
| title_fullStr | Recognition of motor intentions from EEGs of the same upper limb by signal traceability and Riemannian geometry features |
| title_full_unstemmed | Recognition of motor intentions from EEGs of the same upper limb by signal traceability and Riemannian geometry features |
| title_short | Recognition of motor intentions from EEGs of the same upper limb by signal traceability and Riemannian geometry features |
| title_sort | recognition of motor intentions from eegs of the same upper limb by signal traceability and riemannian geometry features |
| topic | EEG source localization motor imagery Riemannian geometry same upper limb temporal variability |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2023.1270785/full |
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