Performance Analysis of Hybrid – BCI Signals Using CNN for Motor Movement Classification
Bibliographic record
Abstract
The design of a hybrid brain-computer interface (BCI) system is an upgradation of the existing BCI systems.Contemporary studies that combine two modalities for a good BCI show that electroencephalogram (EEG) and functional near infra-red spectroscopy (fNIRS) were more convenient.Using this hybrid system various multi-class classification problems have been solved with better ease.The motor imagery and motor execution tasks performed for the Right/Left Arm and Hand were taken from CORE dataset which consists of 15 male subjects.In most cases, feature extraction was done after good pre-processing and channel selections to obtain good results.Deep learning methods like Convolutional Neural Networks and Thin ICA were used for feature extraction and classification of EEG signals.CNN was used for feature extraction and classification in fNIRS with minimal preprocessing and data augmentation.Comparison of performance was done with CNN and a combination of LSTM-CNN classifiers.The proposed CNN model showed 98.3% accuracy with minimal pre-processing and with no channel selection algorithms.Evaluation metrics like Accuracy, Precision, Recall, F1 score and confusion matrix are used to evaluate the classification accuracy.This concludes that the proposed CNN model can classify contralateral and ipsilateral data with lower computational load and with good accuracy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".