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Classification of electroencephalogram signals using wavelet-CSP and projection extreme learning machine
Yixuan Dai1, Xinman Zhang1, Zhiqi Chen1
1MOE Key Lab for Intelligent Networks and Network Security, School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.
This study presents an efficient brain-computer interface (BCI) method using wavelet-CSP and PELM for motor imagery recognition. The proposed system achieves high accuracy with significantly reduced processing times compared to other methods.
Area of Science:
- Neuroscience
- Signal Processing
- Machine Learning
Background:
- Brain-computer interfaces (BCIs) enable direct communication from the brain to devices.
- Recognizing motor imagery signals is challenging due to signal complexity and non-stationarity.
Purpose of the Study:
- To introduce an optimal and intelligent method for motor imagery BCIs.
- To demonstrate the superiority of wavelet-CSP and PELM for BCI signal recognition.
Main Methods:
- Wavelet packet decomposition and Common Spatial Pattern (CSP) were used for signal preprocessing and dimensionality reduction.
- A Projection Extreme Learning Machine (PELM) classifier was employed for electroencephalogram (EEG) signal recognition.
Main Results:
- The proposed wavelet-CSP and PELM method achieved an average recognition rate of approximately 70%.
- Compared to other methods with optimal rates around 72%, the PELM-based system exhibited significantly faster training (4.75 ms) and classification (4.87 ms) times.
Conclusions:
- The developed BCI system demonstrates superior performance in terms of both accuracy and efficiency.
- The combination of wavelet-CSP and PELM offers a robust and effective approach for motor imagery-based BCIs.
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