Comparative Analysis of EEG Signals in Bimanual Coordination: Real vs. Virtual Environments for Rehabilitation
Bibliographic record
Abstract
To develop robots to assist patients in the rehabilitation of upper limbs, research into bimanual coordination is imperative.The aim of our project is to obtain, decode, and compare electroencephalogram (EEG) signals of brain activities in bimanual movement coordination in real and virtual environments.The work included the development of a virtual gaming environment for users to perform three bimanual coordination tasks.EEG signals were collected, preprocessed, and analyzed from three subjects performing these activities in the virtual environment.Time-Frequency Analysis (TFA) was used to extract features in five channels (C1, C2, C3, C4, and Cz).EEG signals were also collected from the same users performing similar activities in the real environment.Comparing the TFA results between the virtual and real environments, significant differences were found in the two subjects.Machine learning techniques were also applied to classify the three motions in the virtual and real environments based on the EEG signals collected from 64 channels.Results show that the highest average classification accuracies of 72.9 ± 9.37% and 70.5 ± 6.11% in real and virtual environments were obtained in three bimanual coordination movements using the EEGNet model.The results indicate the feasibility of decoding the bimanual coordination movements on EEG, and the impact of the virtual environment on EEG signals in time and frequency domains.In the future, we would increase the number of subjects and improve the immersion quality of our virtual environment.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".