Online decoding of covert speech based on the passive perception of speech
Bibliographic record
Abstract
Abstract Background Brain-computer interfaces (BCIs) can offer solutions to communicative impairments induced by conditions such as locked-in syndrome. While covert speech-based BCIs have garnered interest, a major issue facing their clinical translation is the collection of sufficient volumes of high signal-to-noise ratio (SNR) examples of covert speech signals which can typically induce fatigue in users. Fortuitously, investigations into the linkage between covert speech and speech perception have revealed spatiotemporal similarities suggestive of shared encoding mechanisms. Here, we sought to demonstrate that an electroencephalographic cross-condition machine learning model of speech perception and covert speech can successfully decode neural speech patterns during online BCI scenarios. Methods In the current study, ten participants underwent a dyadic protocol whereby participants perceived the audio of a randomly chosen word and then subsequently mentally rehearsed it. Eight words were used during the offline sessions and subsequently narrowed down to three classes for the online session (two words, rest). The modelling was achieved by estimating a functional mapping derived from speech perception and covert speech signals of the same speech token (features were extracted via a Riemannian approach). Results While most covert speech BCIs deal with binary and offline classifications, we report an average ternary and online BCI accuracy of 75.3% (60% chance-level), reaching up to 93% in select participants. Moreover, we found that perception-covert modelling effectively enhanced the SNR of covert speech signals correlatively to their high-frequency correspondences. Conclusions These findings may pave the way to efficient and more user-friendly data collection for passively training such BCIs. Future iterations of this BCI can lead to a combination of audiobooks and unsupervised learning to train a non-trivial vocabulary that can support proto-naturalistic communication. Significance Statement Covert speech brain-computer interfaces (BCIs) provide new communication channels. However, these BCIs face practical challenges in collecting large volumes of high-quality covert speech data which can both induce fatigue and degrade BCI performance. This study leverages the reported spatiotemporal correspondences between covert speech and speech perception by deriving a functional mapping between them. While multiclass and online covert speech classification has previously been challenging, this study reports an average ternary and online classification accuracy of 75.3%, reaching up to 93% for select participants. Moreover, the current modelling approach augmented the signal-to-noise ratio of covert speech signals correlatively to their gamma-band correspondences. The proposed approach may pave the way toward a more efficient and user-friendly method of training covert speech BCIs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.002 | 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".