Enhanced Performance by Time-Frequency-Phase Feature for EEG-Based BCI Systems
We introduce a new motor parameter imagery paradigm using clench speed and clench force motor imagery. The time-frequency-phase features are extracted from mu rhythm and beta rhythms, and the features are optimized using three process methods: no-scaled feature using “MIFS” feature selection criteri...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/420561 |
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| _version_ | 1850236244641972224 |
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| author | Baolei Xu Yunfa Fu Gang Shi Xuxian Yin Zhidong Wang Hongyi Li Changhao Jiang |
| author_facet | Baolei Xu Yunfa Fu Gang Shi Xuxian Yin Zhidong Wang Hongyi Li Changhao Jiang |
| author_sort | Baolei Xu |
| collection | DOAJ |
| description | We introduce a new motor parameter imagery paradigm using clench speed and clench force motor imagery. The time-frequency-phase features are extracted from mu rhythm and beta rhythms, and the features are optimized using three process methods: no-scaled feature using “MIFS” feature selection criterion, scaled feature using “MIFS” feature selection criterion, and scaled feature using “mRMR” feature selection criterion. Support vector machines (SVMs) and extreme learning machines (ELMs) are compared for classification between clench speed and clench force motor imagery using the optimized feature. Our results show that no significant difference in the classification rate between SVMs and ELMs is found. The scaled feature combinations can get higher classification accuracy than the no-scaled feature combinations at significant level of 0.01, and the “mRMR” feature selection criterion can get higher classification rate than the “MIFS” feature selection criterion at significant level of 0.01. The time-frequency-phase feature can improve the classification rate by about 20% more than the time-frequency feature, and the best classification rate between clench speed motor imagery and clench force motor imagery is 92%. In conclusion, the motor parameter imagery paradigm has the potential to increase the direct control commands for BCI control and the time-frequency-phase feature has the ability to improve BCI classification accuracy. |
| format | Article |
| id | doaj-art-154cf3d258b041169b8a887d3bc8c4e0 |
| institution | OA Journals |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-154cf3d258b041169b8a887d3bc8c4e02025-08-20T02:02:01ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/420561420561Enhanced Performance by Time-Frequency-Phase Feature for EEG-Based BCI SystemsBaolei Xu0Yunfa Fu1Gang Shi2Xuxian Yin3Zhidong Wang4Hongyi Li5Changhao Jiang6State Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang 110016, ChinaSchool of Automation and Information Engineering, Kunming University of Science and Technology, Kunming 650500, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang 110016, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang 110016, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang 110016, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang 110016, ChinaKey Laboratory of Motor and Brain Imaging, Capital Institute of Physical Education, Beijing 100088, ChinaWe introduce a new motor parameter imagery paradigm using clench speed and clench force motor imagery. The time-frequency-phase features are extracted from mu rhythm and beta rhythms, and the features are optimized using three process methods: no-scaled feature using “MIFS” feature selection criterion, scaled feature using “MIFS” feature selection criterion, and scaled feature using “mRMR” feature selection criterion. Support vector machines (SVMs) and extreme learning machines (ELMs) are compared for classification between clench speed and clench force motor imagery using the optimized feature. Our results show that no significant difference in the classification rate between SVMs and ELMs is found. The scaled feature combinations can get higher classification accuracy than the no-scaled feature combinations at significant level of 0.01, and the “mRMR” feature selection criterion can get higher classification rate than the “MIFS” feature selection criterion at significant level of 0.01. The time-frequency-phase feature can improve the classification rate by about 20% more than the time-frequency feature, and the best classification rate between clench speed motor imagery and clench force motor imagery is 92%. In conclusion, the motor parameter imagery paradigm has the potential to increase the direct control commands for BCI control and the time-frequency-phase feature has the ability to improve BCI classification accuracy.http://dx.doi.org/10.1155/2014/420561 |
| spellingShingle | Baolei Xu Yunfa Fu Gang Shi Xuxian Yin Zhidong Wang Hongyi Li Changhao Jiang Enhanced Performance by Time-Frequency-Phase Feature for EEG-Based BCI Systems The Scientific World Journal |
| title | Enhanced Performance by Time-Frequency-Phase Feature for EEG-Based BCI Systems |
| title_full | Enhanced Performance by Time-Frequency-Phase Feature for EEG-Based BCI Systems |
| title_fullStr | Enhanced Performance by Time-Frequency-Phase Feature for EEG-Based BCI Systems |
| title_full_unstemmed | Enhanced Performance by Time-Frequency-Phase Feature for EEG-Based BCI Systems |
| title_short | Enhanced Performance by Time-Frequency-Phase Feature for EEG-Based BCI Systems |
| title_sort | enhanced performance by time frequency phase feature for eeg based bci systems |
| url | http://dx.doi.org/10.1155/2014/420561 |
| work_keys_str_mv | AT baoleixu enhancedperformancebytimefrequencyphasefeatureforeegbasedbcisystems AT yunfafu enhancedperformancebytimefrequencyphasefeatureforeegbasedbcisystems AT gangshi enhancedperformancebytimefrequencyphasefeatureforeegbasedbcisystems AT xuxianyin enhancedperformancebytimefrequencyphasefeatureforeegbasedbcisystems AT zhidongwang enhancedperformancebytimefrequencyphasefeatureforeegbasedbcisystems AT hongyili enhancedperformancebytimefrequencyphasefeatureforeegbasedbcisystems AT changhaojiang enhancedperformancebytimefrequencyphasefeatureforeegbasedbcisystems |