ArticleComputational and mathematical methods in medicine2014
Preprocessing by a Bayesian single-trial event-related potential estimation technique allows feasibility of an assistive single-channel P300-based brain-computer interface.
Article in Computational and mathematical methods in medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 6 citations in OpenAlex.
- Evolution Trend of Brain Science Research: An Integrated Bibliometric and Mapping Approach.Brain and behavior · 2025Review
- Evaluation of Single-Trial Classification to Control a Visual ERP-BCI under a Situation Awareness Scenario.Brain sciences · 2023Article
- Time-Shift Correlation Algorithm for P300 Event Related Potential Brain-Computer Interface Implementation.Computational intelligence and neuroscience · 2016Article
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6 authors at 2 institutions in 1 country.
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Abstract
A major clinical goal of brain-computer interfaces (BCIs) is to allow severely paralyzed patients to communicate their needs and thoughts during their everyday lives. Among others, P300-based BCIs, which resort to EEG measurements, have been successfully operated by people with severe neuromuscular disabilities. Besides reducing the number of stimuli repetitions needed to detect the P300, a current challenge in P300-based BCI research is the simplification of system's setup and maintenance by lowering the number N of recording channels. By using offline data collected in 30 subjects (21 amyotrophic lateral sclerosis patients and 9 controls) through a clinical BCI with N = 5 channels, in the present paper we show that a preprocessing approach based on a Bayesian single-trial ERP estimation technique allows reducing N to 1 without affecting the system's accuracy. The potentially great benefit for the practical usability of BCI devices (including patient acceptance) that would be given by the reduction of the number N of channels encourages further development of the present study, for example, in an online setting.
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