Motor Imagery Brain Activity Recognition through Data Augmentation using DC-GANs and Mu-Sigma
Why this work is in the frame
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Bibliographic record
Abstract
The brain-computer interface is a technology that allows a machine to connect with the human brain and work based on the commands released by thoughts and activities of the brain. Electrodes are placed on the scalp and the changes in electric waves released by the brain are recorded as Electroencephalography (EEG) signals. In this work, we propose the use of generative adversarial networks and musigma methods to augment the EEG signals. Some of the existing deep learning methods such as convolutional neural network and recurrent neural network for classification of the EEG signals are implemented and their classification performance is examined with and without data augmentation. It is shown that the use of data augmentation can improve the performance of the EEG signal classification with deep learning models to a considerable extend.
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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 | 0.001 | 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