Papers › Performance of OpenBCI EEG Binary Intent Classification with Laryngeal Imagery
Performance of OpenBCI EEG Binary Intent Classification with Laryngeal Imagery
Samuel Kuhn, Nathan George
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
One of the greatest goals of neuroscience in recent decades has been to rehabilitate individuals who no longer have a functional relationship between their mind and their body. Although neuroscience has produced technologies which allow the brains of paralyzed patients to accomplish tasks such as spell words or control a motorized wheelchair, these technologies utilize parts of the brain which may not be optimal for simultaneous use. For example, if you needed to look at flashing lights to spell words for communication, it would be difficult to simultaneously look at where you are moving. To improve upon this issue, this study developed and tested the foundation for a speech prosthesis paradigm which would utilize the innate neurophysiology of the human brain's speech system. In this experiment, two participants were asked to respond to a yes or no question via an EEG-based BCI of three different types; SSVEP-based, motor imagery-based, and laryngeal-imagery-based. By comparing the accuracy of the two established BCI paradigms to the novel laryngeal-imagery paradigm, we can establish the relative effectiveness of the novel paradigm. Machine learning algorithms were used to classify the EEG signals which had been transformed into frequency space (spectrograms) and common spatial pattern (CSP) dimensions. The SSVEP control task was able to be classified with better accuracy (62.5\%) than the no information rate of 50\% on the test set, but motor activity/imagery and laryngeal activity/imagery control tasks were not. Although the laryngeal methods did not produce accuracies above the no information rate, it is possible that with a larger amount of higher-quality data, this could prove otherwise. In the future, similar research should focus on reproducing the methods used here with better quality and more data.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections