Zapisane w:
| 1. autor: | |
|---|---|
| Format: | Recurso digital |
| Język: | angielski |
| Wydane: |
Zenodo
2025
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| Hasła przedmiotowe: | |
| Dostęp online: | https://doi.org/10.5281/zenodo.15292221 |
| Etykiety: |
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Spis treści:
- <p>Poster presented at the conference "Meetings of the Alhambra: The Cognitive Neuroscience of Consciousness" in Granada (Spain), 25-26 April 2025. </p> <p>Abstract: Brain-computer interfaces (BCIs) are a robust and powerful tool that enables the integration<br>of observed neural activity with direct manipulations of both this activity and behavior. BCIs<br>have been widely used for years in clinical research, particularly with patients suffering from<br>motor deficits caused by strokes or neurodegenerative diseases. Moreover, BCIs also have<br>promising applications in basic neuroscience, with recent literature demonstrating their<br>effectiveness in motor control, mental imagery, and attention.<br>In a previous study we found that relevant information related to conscious perception<br>processes can be decoded from electroencephalography (EEG) data using machine learning<br>(ML) algorithms. Specifically, our results showed that the presence or absence of a stimulus<br>could be decoded from the EEG data after target onset. Similar results were also obtained for<br>participants’ reported perception. Furthermore, we also observed significantly different<br>patterns of activation for present targets depending on whether they were reported by<br>participants as seen or as unseen.<br>Based on this perceptual task, we are developing a closed-loop system to detect perceptual<br>failures (i.e. participants reporting a stimulus as ‘unseen’ when it was presented) to alert<br>participants with an attentional signal in order to improve their performance in the task. This<br>neurofeedback pipeline includes real-time preprocessing of the recorded EEG signal, training<br>of a ML classifier, and deploying the model to continuously analyze registered data. Our<br>closed-loop system is able to perform predictions of the performance on the behavioral task<br>to decide whether to send an alert tone to the participant. Here, we present some preliminary<br>results of this neurofeedback system, and lay the groundwork for future steps.</p>