Eye-Tracking and BCI Integration for Assistive Communication in Locked-In Syndrome: Pilot Study with Healthy Participants
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912612232462336 |
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| author | Pinto, Ana Patrícia Bettencourt, Rute Nunes, Urbano J. Pires, Gabriel |
| author_facet | Pinto, Ana Patrícia Bettencourt, Rute Nunes, Urbano J. Pires, Gabriel |
| contents | Patients with Amyotrophic Lateral Sclerosis (ALS) progressively lose voluntary motor control, often leading to a Locked-In State (LIS), or in severe cases, a Completely Locked-in State (CLIS). Eye-tracking (ET) systems are common communication tools in early LIS but become ineffective as oculomotor function declines. EEG-based Brain-Computer Interfaces (BCIs) offer a non-muscular communication alternative, but delayed adoption may reduce performance due to diminished goal-directed thinking. This study presents a preliminary hybrid BCI framework combining ET and BCI to support a gradual transition between modalities. A group of five healthy participants tested a modified P300-based BCI. Gaze and EEG data were processed in real time, and an ET-BCI fusion algorithm was proposed to enhance detection of user intention. Results indicate that combining both modalities may maintain high accuracy and offers insights on how to potentially improve communication continuity for patients transitioning from LIS to CLIS. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_23518 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Eye-Tracking and BCI Integration for Assistive Communication in Locked-In Syndrome: Pilot Study with Healthy Participants Pinto, Ana Patrícia Bettencourt, Rute Nunes, Urbano J. Pires, Gabriel Human-Computer Interaction Signal Processing Patients with Amyotrophic Lateral Sclerosis (ALS) progressively lose voluntary motor control, often leading to a Locked-In State (LIS), or in severe cases, a Completely Locked-in State (CLIS). Eye-tracking (ET) systems are common communication tools in early LIS but become ineffective as oculomotor function declines. EEG-based Brain-Computer Interfaces (BCIs) offer a non-muscular communication alternative, but delayed adoption may reduce performance due to diminished goal-directed thinking. This study presents a preliminary hybrid BCI framework combining ET and BCI to support a gradual transition between modalities. A group of five healthy participants tested a modified P300-based BCI. Gaze and EEG data were processed in real time, and an ET-BCI fusion algorithm was proposed to enhance detection of user intention. Results indicate that combining both modalities may maintain high accuracy and offers insights on how to potentially improve communication continuity for patients transitioning from LIS to CLIS. |
| title | Eye-Tracking and BCI Integration for Assistive Communication in Locked-In Syndrome: Pilot Study with Healthy Participants |
| topic | Human-Computer Interaction Signal Processing |
| url | https://arxiv.org/abs/2509.23518 |