Systems Design for EEG Signal Classification of Sensorimotor Activity Using Machine Learning
Bibliographic record
Abstract
This paper proposes a systems design for classifying EEG motor movement signals using AI that achieves a high degree of accuracy. EEG motor movement signals are generated by the brain when the subject consciously attempts to move their body. These signals are reflective of the kind of movement they are attempting to achieve, and improving the classification would allow for better assislive devices for the physically disabled. Al classification requires features to be extracted from the raw data. Features can be extracted using different algorithms. The systems design allows the selection of different features. The features used arc calculated from the dalapuints corresponding to 1 second windows and transformed into the sigma (Σ), phi (Φ), and omega (Ω) features. To our knowledge, this is the first time that these features have been used with machine learning techniques. The approach allows the use of different classification models. We test the system with a Support Vector Machine (SVM) and an Artificial Neural Network (ANN), which were both trained on these features, and each window classified independently according to the model. The SVM had an average accuracy of 88%, while the neural network had a higher accuracy of 94%. There was a relatively large amount of variance in the accuracy for different subjects, ranging from 45.9% to 99.6% for the SVM and 243% to 99.7% for the ANN. The proof of concept demonstrates that different machine learning algorithms can be used for classification if a pipeline architecture is used.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".