EEG Classification of Covert Speech Using Regularized Neural Networks
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Bibliographic record
Abstract
Communication using brain-computer interfaces (BCIs) can be non-intuitive, often requiring the performance of a conversation-irrelevant task such as hand motor imagery. In this paper, the reliability of electroencephalography (EEG) signals in discriminating between different covert speech tasks is investigated. Twelve participants, across two sessions each, were asked to perform multiple iterations of three differing mental tasks for 10 s each: unconstrained rest or the mental repetition of the words “yes” or “no.” A multilayer perceptron (MLP) artificial neural network (ANN) was used to classify all three pairwise combinations of “yes,” “no,” and rest trials and also for ternary classification. An average accuracy of 75.7% ± 9.6 was reached in the classification of covert speech trials versus rest, with all participants exceeding chance level (57.8%). The classification of “yes” versus “no” yielded an average accuracy of 63.2 ± 6.4 with ten participants surpassing chance level (57.8%). Finally, the ternary classification yielded an average accuracy of 54.1% ± 9.7 with all participants exceeding chance level (39.1%). The proposed MLP network provided significantly higher accuracies compared to some of the most common classification techniques in BCI. To our knowledge, this is the first report of using ANN for the classification of EEG covert speech across multiple sessions. Our findings support further study of covert speech as a BCI activation task, potentially leading to the development of more intuitive BCIs for communication.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it