Topological Data Analysis and Decoding Based on Electroencephalogram Signals
Bibliographic record
Abstract
In order to assist patients in performing upper limb activities in a normal manner, research on bimanual coordination is necessary for robots to assist in the rehabilitation process.The objective of this paper is to investigate brain activities in bimanual movements coordination to obtain and decode signals of the electroencephalogram (EEG) in bimanual movements in both real and virtual environments.We designed three paradigms of bimanual coordination tasks to collect and analyse EEG signals, including (1) raise both hands vertically, (2) spread both hands horizontally, (3) left hand horizontally spread to the left, right hand vertically rise upwards.We aim to classify the three types of the motions using persistent homology-based machine learning techniques.First we extract topological features from functional connectivity network and effective connectivity network based on correlations between channels.In particular, the persistent diagrams and persistent landscapes are calculated.Then we use them as input for the classification.Our results show that the highest average binary classification accuracies which is functional connectivity network (FC)+Persistent Scale Space kernel (PSSK), has an accuracy of only 72.48%±1.07%in the real environment.The design of our experimental paradigm and the classification results demonstrated the preliminary exploration and indicated the feasibility of decoding the bimanual coordination on EEG.In the future, we would expand the size of subjects and improve the topological data analysis methods for further exploration of EEG in the field of decoding two-handed coordination.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".