WPD Combined with One-on-one CSP for Motor Imagery EEG Signal Classification
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Bibliographic record
Abstract
Regarding the EEG (electroencephalogram) signals of motor imagery, existing signal decomposition methods similar to EMD (Empirical Mode Decomposition) are often affected by mode aliasing and mode oscillation, and classifiers are prone to overfitting in high-dimensional data. This article combined WPD (Wavelet Packet Decomposition) and one-to-one CSP (Common Spatial Pattern) to study the classification of motor imagery EEG signals, aiming to provide better time-frequency resolution and improve classification performance. Using the publicly available dataset BCI (Brain-computer Interface) Competition IV 2a as the object: firstly, WPD was used to perform multi-level decomposition on four types of motor imagery EEG signals from nine subjects; next, the covariance matrix of each category of EEG signals in CSP was calculated to extract feature vectors, and the features that best distinguish different categories were selected to reduce dimensionality and avoid overfitting; finally, in the 10-fold cross-validation process, the number of features was optimized to improve the performance of the Random Forest (RF) classifier. The results showed that the method proposed in this article had a mean Maximum Mutual Information (MMI) of 0.67 bits and a maximum classification accuracy of 87.5% for the BCI Competition IV 2a dataset, which was approximately 2.1% higher than the Attention-based Temporal Convolutional Network (ATCNet) model.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it