ArticleJournal of neuroengineering and rehabilitation2019
Electroencephalography-based endogenous brain-computer interface for online communication with a completely locked-in patient.
Article in Journal of neuroengineering and rehabilitation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 66 citations in OpenAlex.
- Motor-Imagery EEG-Based BCIs in Wheelchair Movement and Control: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Advancing EEG-based assessment of consciousness and cognition in prolonged disorders of consciousness.Communications medicine · 2026Article
- Disorder of consciousness rather than complete Locked-In Syndrome for end stage Amyotrophic Lateral Sclerosis: a case series.Communications medicine · 2025Article
- A review on the performance of brain-computer interface systems used for patients with locked-in and completely locked-in syndrome.Cognitive neurodynamics · 2024Review
- Considerations and discussions on the clear definition and definite scope of brain-computer interfaces.Frontiers in neuroscience · 2024Review
- The Objective Assessment of Event-Related Potentials: An Influence of Chronic Pain on ERP Parameters.Neuroscience bulletin · 2023Article
- Recent Progress in Wearable Brain-Computer Interface (BCI) Devices Based on Electroencephalogram (EEG) for Medical Applications: A Review.Health data science · 2023Review
- Locked-in syndrome revisited.Therapeutic advances in neurological disorders · 2023Review
- Article
- Improved classification performance of EEG-fNIRS multimodal brain-computer interface based on multi-domain features and multi-level progressive learning.Frontiers in human neuroscience · 2022Article
- Article
- Binary Semantic Classification Using Cortical Activation with Pavlovian-Conditioned Vestibular Responses in Healthy and Locked-In Individuals.Cerebral cortex communications · 2021Article
- Enhancing Communication for People in Late-Stage ALS Using an fNIRS-Based BCI System.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2020Article
- Induced Gamma-Band Activity during Actual and Imaginary Movements: EEG Analysis.Sensors (Basel, Switzerland) · 2020Article
- Development of a Brain-Computer Interface Toggle Switch with Low False-Positive Rate Using Respiration-Modulated Photoplethysmography.Sensors (Basel, Switzerland) · 2020Article
- Article
- Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review.Frontiers in neurorobotics · 2020Review
- Brain-Based Binary Communication Using Spatiotemporal Features of fNIRS Responses.Frontiers in human neuroscience · 2020Article
- Motor Imagery Under Distraction- An Open Access BCI Dataset.Frontiers in neuroscience · 2020Article
- Hyperscanning EEG and Classification Based on Riemannian Geometry for Festive and Violent Mental State Discrimination.Frontiers in neuroscience · 2020Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundBrain-computer interfaces (BCIs) have demonstrated the potential to provide paralyzed individuals with new means of communication, but an electroencephalography (EEG)-based endogenous BCI has never been successfully used for communication with a patient in a completely locked-in state (CLIS).
methodsIn this study, we investigated the possibility of using an EEG-based endogenous BCI paradigm for online binary communication by a patient in CLIS. A female patient in CLIS participated in this study. She had not communicated even with her family for more than one year with complete loss of motor function. Offline and online experiments were conducted to validate the feasibility of the proposed BCI system. In the offline experiment, we determined the best combination of mental tasks and the optimal classification strategy leading to the best performance. In the online experiment, we investigated whether our BCI system could be potentially used for real-time communication with the patient.
resultsAn online classification accuracy of 87.5% was achieved when Riemannian geometry-based classification was applied to real-time EEG data recorded while the patient was performing one of two mental-imagery tasks for 5 s.
conclusionsOur results suggest that an EEG-based endogenous BCI has the potential to be used for online communication with a patient in CLIS.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.