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[Research on performance of motor-imagery-based brain-computer interface in different complexity of Chinese character
Cili Zuo1, Ying Mao1, Qianqian Liu1
1Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, P.R. China.
Summary
Increasing the complexity of Chinese character writing tasks in brain-computer interfaces (BCIs) enhances sensorimotor rhythm and recognition accuracy. This finding improves motor-imagery-based BCIs by optimizing task design.
Area of Science:
- Neuroscience
- Human-Computer Interaction
- Biomedical Engineering
Background:
- Traditional motor-imagery-based brain-computer interfaces (BCIs) lack effective user guidance for brain activity modulation.
- Motor imagery tasks involving Chinese character writing show user acceptance and aid sensorimotor rhythm modulation.
- The impact of Chinese character writing complexity on BCI performance remains underexplored.
Purpose of the Study:
- To investigate the effect of Chinese character writing complexity on motor-imagery-based BCI performance.
- To compare the sensorimotor rhythm modulation and recognition accuracy between tasks of varying writing complexity.
Main Methods:
- Recruited 12 healthy subjects for the study.
- Assessed the performance of motor-imagery-based BCI using Chinese characters with 5 and 10 strokes.
- Analyzed sensorimotor rhythm strength and recognition accuracy.
Main Results:
- Motor imagery tasks using 10-stroke Chinese characters elicited stronger sensorimotor rhythms compared to 5-stroke characters.
- Recognition performance was significantly better (P < 0.05) for motor imagery tasks with 10-stroke Chinese characters.
- Increased task complexity enhanced motor imagery potential.
Conclusions:
- Appropriately increasing the complexity of Chinese character writing tasks can strengthen motor imagery potential.
- This approach improves the recognition accuracy of motor-imagery-based BCIs.
- Findings offer valuable insights for designing future BCI paradigms.
