Yan Bian1, Hongzhi Qi2, Li Zhao3, Dong Ming4, Tong Guo5, Xing Fu5. 1. School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Weijin Road No.92, Nankai District, Tianjin 300072, China; Tianjin Information Sensing & Intelligent Control Key Lab, Tianjin University of Technology and Education, Dagu South Road, No.1310, Hexi District, Tianjin 300222, China. 2. School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Weijin Road No.92, Nankai District, Tianjin 300072, China. Electronic address: qhz@tju.edu.cn. 3. Tianjin Information Sensing & Intelligent Control Key Lab, Tianjin University of Technology and Education, Dagu South Road, No.1310, Hexi District, Tianjin 300222, China. 4. School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Weijin Road No.92, Nankai District, Tianjin 300072, China. Electronic address: richardming@tju.edu.cn. 5. School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Weijin Road No.92, Nankai District, Tianjin 300072, China.
Abstract
BACKGROUND AND OBJECTIVE: The motor-imagery based brain-computer interface supplies a potential approach for motor-impaired patients, not only to control rehabilitation facilities but also to promote recovery from motor dysfunctions. To improve event-related desynchronization during motor imagery and obtain improved brain-computer interface classification accuracy, we introduce dynamic video guidance and complex motor tasks to the motor imagery paradigm. METHODS: Eleven participants were included in the experiment; 64-channel electroencephalographic data were collected and analyzed during four motor imagery tasks with different guidance. Time-frequency analysis, spectral-time variation analysis, topographical distribution maps, and statistical analysis were utilized to analyze the event-related desynchronization patterns. Common spatial patterns were used to extract spatial pattern features and support vector machines were used to discriminate the offline classification accuracies in three bands (the alpha band, beta band, alpha and beta band) for comparison. RESULTS: The experimental outcomes showed that complex motor imagery tasks coupled with dynamic video guidance induced significantly stronger event-related desynchronization than other paradigms, which use simple motor imagery tasks or static guidance. Similar results were obtained during analysis of the motor imagery brain-computer interface classification performance; namely, the highest average classification accuracy in complex and dynamic guidance was improved by approximately 14%, compared with static guidance. For individually specified paradigms, all participants obtained a classification accuracy that exceeded or was equal to 87.5%. CONCLUSIONS: This study provides an optional route to enhance the event-related desynchronization activities and classification accuracy of a motor imagery brain-computer interface through optimization of motor imagery tasks and instructive guidance.
BACKGROUND AND OBJECTIVE: The motor-imagery based brain-computer interface supplies a potential approach for motor-impairedpatients, not only to control rehabilitation facilities but also to promote recovery from motor dysfunctions. To improve event-related desynchronization during motor imagery and obtain improved brain-computer interface classification accuracy, we introduce dynamic video guidance and complex motor tasks to the motor imagery paradigm. METHODS: Eleven participants were included in the experiment; 64-channel electroencephalographic data were collected and analyzed during four motor imagery tasks with different guidance. Time-frequency analysis, spectral-time variation analysis, topographical distribution maps, and statistical analysis were utilized to analyze the event-related desynchronization patterns. Common spatial patterns were used to extract spatial pattern features and support vector machines were used to discriminate the offline classification accuracies in three bands (the alpha band, beta band, alpha and beta band) for comparison. RESULTS: The experimental outcomes showed that complex motor imagery tasks coupled with dynamic video guidance induced significantly stronger event-related desynchronization than other paradigms, which use simple motor imagery tasks or static guidance. Similar results were obtained during analysis of the motor imagery brain-computer interface classification performance; namely, the highest average classification accuracy in complex and dynamic guidance was improved by approximately 14%, compared with static guidance. For individually specified paradigms, all participants obtained a classification accuracy that exceeded or was equal to 87.5%. CONCLUSIONS: This study provides an optional route to enhance the event-related desynchronization activities and classification accuracy of a motor imagery brain-computer interface through optimization of motor imagery tasks and instructive guidance.
Authors: Jonathan A Martinez; Matthew W Wittstein; Stephen F Folger; Stephen P Bailey Journal: Front Hum Neurosci Date: 2019-11-26 Impact factor: 3.169