Classification of Imagery Hand Movement Based on Electroencephalogram Signal Using Long-Short Term Memory Network Method
Bibliographic record
Abstract
Amputation is sometimes utilized to overcome tissue death in human limbs.Prostheses offer individuals an effective solution for restoring their quality of life.The development of prosthetic control systems using EEG-acquired movement imagery signals is ongoing.This technology has proven a viable option due to its easy controllability by an individual's thought patterns.This study aimed to discover distinguishing features between imagery movement and grasping and opening hand movements.To this end, the proposed method is a classification using Long-Short Term Memory Network (LSTM) with various feature combinations of mean, standard deviation, variance, RMS, skewness, kurtosis, and PSD at alpha rhythm.Data were acquired from three healthy subjects using the Emotiv Epoc+Headset.The classification results showed that applying skewness and kurtosis features yielded an accuracy range of 73.52% to 100% for each subject's data.On the other hand, combining kurtosis and Power Spectrum Density (PSD) features resulted in 84.9% accuracy for the subjects' combined data.This result shows great potential in supporting the development of prosthetic control to improve the quality of life of an amputee.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.001 | 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".