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Adaptation of motor imagery EEG classification model based on tensor decomposition.
Xinyang Li1, Cuntai Guan, Haihong Zhang
1NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore 119613, Singapore. Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore 138632, Singapore.
Journal of Neural Engineering
|September 23, 2014
Summary
This study quantifies model-data mismatch in electroencephalography brain-computer interfaces due to nonstationarity. A novel tensor model adaptation method effectively minimizes this mismatch, improving classification accuracy.
Area of Science:
- Neuroscience
- Machine Learning
- Biomedical Engineering
Background:
- Electroencephalography (EEG)-based brain-computer interfaces (BCIs) are susceptible to session-to-session nonstationarity.
- This variability poses a challenge for maintaining consistent BCI performance over time.
Purpose of the Study:
- To quantify the data-model mismatch caused by nonstationarity in EEG-BCIs.
- To develop and validate a model adaptation technique to minimize this mismatch and enhance BCI performance.
Main Methods:
- A semi-supervised tensor model was utilized to estimate the data-model mismatch.
- The mismatch estimate was regularized within a discriminative objective function for model adaptation.
Main Results:
- The proposed adaptation method demonstrated superior performance compared to existing regularization-based and spatial filter adaptation techniques.
- A significant correlation was observed between the quantified data-model mismatch and classification accuracy.
- Evaluation on data from 16 subjects performing motor imagery tasks confirmed the method's effectiveness.
Conclusions:
- The proposed approach directly addresses nonstationarity by focusing on data-model mismatch, offering a more direct solution than traditional data variation measurements.
- The method effectively enhances the performance of feature extraction models in EEG-BCIs.
- This work contributes to more robust and reliable BCI systems.
