Towards a user-friendly brain-computer interface: initial tests in ALS and PLS patients
Open accessAbstract
Objective Patients usually require long-term training for effective EEG-based brain-computer interface (BCI) control due to fatigue caused by the demands for focused attention during prolonged BCI operation. We intended to develop a user-friendly BCI requiring minimal training and less mental load.Methods Testing of BCI performance was investigated in three patients with amyotrophic lateral sclerosis (ALS) and three patients with primary lateral sclerosis (PLS), who had no previous BCI experience. All patients performed binary control of cursor movement. One ALS patient and one PLS patient performed four-directional cursor control in a two-dimensional domain under a BCI paradigm associated with human natural motor behavior using motor execution and motor imagery. Subjects practiced for 5-10min and then participated in a multi-session study of either binary control or four-directional control including online BCI game over 1.5-2h in a single visit.Results Event-related desynchronization and event-related synchronization in the beta band were observed in all patients during the production of voluntary movement either by motor execution or motor imagery. The online binary control of cursor movement was achieved with an average accuracy about 82.1+/-8.2% with motor execution and about 80% with motor imagery, whereas offline accuracy was achieved with 91.4+/-3.4% with motor execution and 83.3+/-8.9% with motor imagery after optimization. In addition, four-directional cursor control was achieved with an accuracy of 50-60% with motor execution and motor imagery.Conclusion Patients with ALS or PLS may achieve BCI control without extended training, and fatigue might be reduced during operation of a BCI associated with human natural motor behavior.Significance The development of a user-friendly BCI will promote practical BCI applications in paralyzed patients.
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Mutual learning has been claimed mostly on the grounds of adequate and improved BCI classification accuracy or application performances.
“Indeed, mutual learning has been claimed mostly on the grounds of adequate and improved BCI classification accuracy [25,27,30–35] or application performances [36,37].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [32]
Evidence that co-adaptive MI BCIs might also be able to promote and increase the ability of users to voluntarily modulate their brain signals (subject learning) is scarce, most often indirect and rather inconclusive.
“Evidence that co-adaptive MI BCIs might also be able to promote and increase the ability of the users to voluntary modulate their brain signals (subject learning) is, in fact, scarce, most often indirect and rather inconclusive.”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [32]
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- Authors
- Bai, O., Lin, P., Huang, D., Fei, D.-Y., & Floeter, M. K.
- Affiliations
- National Institutes of Health (US)
- Santa Clara Valley Medical Center (US)
- Virginia Commonwealth University (US)
- Corresponding author
- Bai, Ou
- Venue
- Clinical Neurophysiology
- Publisher
- Elsevier BV
- Published
- 2010-08
- Volume
- 121
- Issue
- 8
- Pages
- 1293-1303
- ISSN
- 1388-2457 (ISSN-L)
- PMC
- PMC2895010
- OpenAlex
- W2119206024
- Cited by
- 80
- References
- 61
- Citations by year
- 2026: 2 · 2025: 4 · 2023: 1 · 2022: 7 · 2021: 4 · 2020: 5 · 2019: 5 · 2018: 6 · 2017: 3 · 2016: 6 · 2015: 2 · 2014: 10 · 2013: 7 · 2012: 10
- FWCI
- 4.84
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Clinical Neurophysiology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Institute of Neurological Disorders and Stroke (ZIANS002976)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Keywords
- brain-computer interface, amyotrophic lateral sclerosis, primary lateral sclerosis, motor execution, motor imagery, beta band, event-related desynchronization, event-related synchronization, cursor control, fatigue reduction, paralysis
- MeSH terms
- Adult, Aged, Amyotrophic Lateral Sclerosis, Brain, Brain Mapping, Electroencephalography, Electromyography, Female, Humans, Imagination, Male, Middle Aged, Motor Activity, Movement, Psychomotor Performance, Signal Processing, Computer-Assisted, User-Computer Interface
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