Recognition and Analysis of Motor Imagery EEG Signal Based on Improved BP Neural Network
With the rapid development of neuroinformatics and related intelligent algorithms, the research of recognition and classification based on EEG signals is becoming more and more important and valuable. With the progress of science and technology, the related research of EEG signal recognition and pro...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8685102/ |
| _version_ | 1828885092595924992 |
|---|---|
| author | Long Liu |
| author_facet | Long Liu |
| author_sort | Long Liu |
| collection | DOAJ |
| description | With the rapid development of neuroinformatics and related intelligent algorithms, the research of recognition and classification based on EEG signals is becoming more and more important and valuable. With the progress of science and technology, the related research of EEG signal recognition and processing has been gradually applied to rehabilitation medicine, intelligent information processing, and other cross-cutting fields. As one of the most important research directions in the field of the brain-computer interface, motor imagery EEG has a wide range of applications. At the same time, it shows a good application effect in the process of application practice. At present, the main EEG recognition and analysis algorithms always have some problems and defects in data processing, such as low signal-to-noise ratio, unclean noise filtering, and high data dimension. In this paper, based on the improved BP neural network algorithm, weight splitting technology is added to the traditional BP neural network algorithm. In order to solve the filtering problem, this paper uses the non-linear mapping function of the traditional BP neural network, and intelligently trains the small weight particles by combining the particle swarm filter algorithm, so as to improve the filtering performance of the whole BP algorithm. Based on the above two algorithms, the problem of low signal-to-noise ratio (SNR) and unclean filtering in EEG data processing caused by fast weight degradation in traditional BP algorithm can be solved. Finally, according to the actual data of brain-computer interface, this paper compares the improved BP neural network algorithm with the traditional BP neural network algorithm in recognition and analysis of motor imagery EEG signals. The experiment shows that the proposed algorithm has obvious advantages in recognition accuracy and analysis effect. |
| first_indexed | 2024-12-13T11:20:08Z |
| format | Article |
| id | doaj.art-cef839c916f344059e00fd7dc894466b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T11:20:08Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-cef839c916f344059e00fd7dc894466b2022-12-21T23:48:31ZengIEEEIEEE Access2169-35362019-01-017477944780310.1109/ACCESS.2019.29101918685102Recognition and Analysis of Motor Imagery EEG Signal Based on Improved BP Neural NetworkLong Liu0https://orcid.org/0000-0003-1762-3434College of Physical Education, Anyang Normal University, Anyang, ChinaWith the rapid development of neuroinformatics and related intelligent algorithms, the research of recognition and classification based on EEG signals is becoming more and more important and valuable. With the progress of science and technology, the related research of EEG signal recognition and processing has been gradually applied to rehabilitation medicine, intelligent information processing, and other cross-cutting fields. As one of the most important research directions in the field of the brain-computer interface, motor imagery EEG has a wide range of applications. At the same time, it shows a good application effect in the process of application practice. At present, the main EEG recognition and analysis algorithms always have some problems and defects in data processing, such as low signal-to-noise ratio, unclean noise filtering, and high data dimension. In this paper, based on the improved BP neural network algorithm, weight splitting technology is added to the traditional BP neural network algorithm. In order to solve the filtering problem, this paper uses the non-linear mapping function of the traditional BP neural network, and intelligently trains the small weight particles by combining the particle swarm filter algorithm, so as to improve the filtering performance of the whole BP algorithm. Based on the above two algorithms, the problem of low signal-to-noise ratio (SNR) and unclean filtering in EEG data processing caused by fast weight degradation in traditional BP algorithm can be solved. Finally, according to the actual data of brain-computer interface, this paper compares the improved BP neural network algorithm with the traditional BP neural network algorithm in recognition and analysis of motor imagery EEG signals. The experiment shows that the proposed algorithm has obvious advantages in recognition accuracy and analysis effect.https://ieeexplore.ieee.org/document/8685102/Keywords improved BP neural algorithmsmotor imagery EEG signalsweight splitting technologyrecognition accuracyparticle cluster filtering technology |
| spellingShingle | Long Liu Recognition and Analysis of Motor Imagery EEG Signal Based on Improved BP Neural Network IEEE Access Keywords improved BP neural algorithms motor imagery EEG signals weight splitting technology recognition accuracy particle cluster filtering technology |
| title | Recognition and Analysis of Motor Imagery EEG Signal Based on Improved BP Neural Network |
| title_full | Recognition and Analysis of Motor Imagery EEG Signal Based on Improved BP Neural Network |
| title_fullStr | Recognition and Analysis of Motor Imagery EEG Signal Based on Improved BP Neural Network |
| title_full_unstemmed | Recognition and Analysis of Motor Imagery EEG Signal Based on Improved BP Neural Network |
| title_short | Recognition and Analysis of Motor Imagery EEG Signal Based on Improved BP Neural Network |
| title_sort | recognition and analysis of motor imagery eeg signal based on improved bp neural network |
| topic | Keywords improved BP neural algorithms motor imagery EEG signals weight splitting technology recognition accuracy particle cluster filtering technology |
| url | https://ieeexplore.ieee.org/document/8685102/ |
| work_keys_str_mv | AT longliu recognitionandanalysisofmotorimageryeegsignalbasedonimprovedbpneuralnetwork |