DenseNet Based Speech Imagery EEG Signal Classification using Gramian Angular Field
Bibliographic record
Abstract
One of the most challenging tasks in the Brain-Computer Interface (BCI) system is to classify the speech imagery electroencephalography (EEG) signals. In this work, we addressed the existing low classification accuracy problem with deep learning and improved beta band selection method. When the subject imagines, uttering a word rather saying it directly, there are changes in electrical stimulation in the brain. These electrical stimulations of the brain are recorded using EEG signal recording device. The recorded EEG data is then processed using the Dual-Tree Complex Wavelet Transform (DTCWT) for beta band selection which is responsible for activity related to imagery. To take advantage of Deep Convolutional Neural Networks (DCNN), we converted the time series EEG data into images. We generated images using two versions of Gramian Angular Field (GAF): Gramian Summation Angular Filed (GASF) and Gramian Difference Angular Field (GADF). Then these images were fed to DenseNet for image classification. DenseNet is an improved version of DCNN that minimizes the vanishing gradient problem. Between two different image generation techniques, GADF has the best average classification accuracy rate of 90.68 %. The dataset used in this study named `The KARA ONE Database' is collected from Computational Linguistics Lab, University of Toronto, Canada.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".