A visual brain-computer interface as communication aid for patients with amyotrophic lateral sclerosis

Verbaarschot, Ceci; Tump, Daniëlle; Lutu, Andreea; Borhanazad, Marzieh; Thielen, Jordy; Broek, Philip van den; Farquhar, Jason; Weikamp, Janneke G.; Raaphorst, Joost; Groothuis, Jan T.; Desain, Peter

doi:10.1016/j.clinph.2021.07.012

Cited by 24 publications

(26 citation statements)

References 26 publications

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“…As shown, a 63-bit m-sequence is usually sufficient for modulating up to 32 commands. Applications that require more commands, however, must rely on longer m-sequences [73], increasing the selection time; or on multi-sequence paradigms, in which (1) different bit-sequences modulate subgroups of commands [23,89,94,95], or where (2) each command is modulated by a different bit-sequence [22,24,26,34,35,46,48,49,55,62,64,92,93,[97][98][99]. In the latter case, it is recommended to prevent cross-talk between neighboring commands by distributing each bit-sequence in function of their crosscorrelation properties [24].…”

Section: Bit-sequencesmentioning

confidence: 99%

“…For multi-sequence systems, using sets of msequences is not convenient because they do not guarantee low cross-correlations between them [21]. By contrast, Gold [22,24,43,44,87,[91][92][93] and Kasami [89] sequence sets are easy to generate and present acceptable auto-correlation and periodic cross-correlation properties, making them especially suitable for this purpose [21]. In a nutshell, both sets are derived from combinations of preferred pairs of m-sequences.…”

Section: Bit-sequencesmentioning

confidence: 99%

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Brain–computer interfaces based on code-modulated visual evoked potentials (c-VEP): a literature review

et al. 2021

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Objective. Code-modulated visual evoked potentials (c-VEP) have been consolidated in recent years as robust control signals capable of providing non-invasive brain–computer interfaces (BCIs) for reliable, high-speed communication. Their usefulness for communication and control purposes has been reflected in an exponential increase of related articles in the last decade. The aim of this review is to provide a comprehensive overview of the literature to gain understanding of the existing research on c-VEP-based BCIs, since its inception (1984) until today (2021), as well as to identify promising future research lines. Approach. The literature review was conducted according to the Preferred Reporting Items for Systematic reviews and Meta-Analysis guidelines. After assessing the eligibility of journal manuscripts, conferences, book chapters and non-indexed documents, a total of 70 studies were included. A comprehensive analysis of the main characteristics and design choices of c-VEP-based BCIs was discussed, including stimulation paradigms, signal processing, modeling responses, applications, etc. Main results. The literature review showed that state-of-the-art c-VEP-based BCIs are able to provide an accurate control of the system with a large number of commands, high selection speeds and even without calibration. In general, a lack of validation in real setups was observed, especially regarding the validation with disabled populations. Future work should be focused toward developing self-paced c-VEP-based portable BCIs applied in real-world environments that could exploit the unique benefits of c-VEP paradigms. Some aspects such as asynchrony, unsupervised training, or code optimization still require further research and development. Significance. Despite the growing popularity of c-VEP-based BCIs, to the best of our knowledge, this is the first literature review on the topic. In addition to providing a joint discussion of the advances in the field, some future lines of research are suggested to contribute to the development of reliable plug-and-play c-VEP-based BCIs.

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Section: Bit-sequencesmentioning

confidence: 99%

Section: Bit-sequencesmentioning

confidence: 99%

Brain–computer interfaces based on code-modulated visual evoked potentials (c-VEP): a literature review

et al. 2021

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“…Pored stabilnih vizuelnih evociranih potencijala, pokazano je da i druga vrsta vizuelnih potencijala može biti korisna kod osoba sa ALS (npr. code-modulated Visual Evoked Potentials -cVEP) (Verbaarschot et al, 2021). Rezultati studije pokazali su da osobe sa ALS mogu da koriste uređaj za spelovanje uz kontrolu cVEP signala, s obzirom na to da je 27 od 30 učesnika ove studije bilo uspešno u tom zadatku, sa 79% tačnosti.…”

Section: Razvoj Mozak-računar Interfejs Sistema U Komunikaciji Osoba ...unclassified

Application of brain-computer interface system in communication of people with severe motor deficits

Jovanović-Simić¹,

Arsenić²,

Daničić³

2022

Spec eduk i rehabilitacija

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Introduction. Maintaining and improving communication skills of people with severe motor deficits are the main goals of speech therapy. Brain-computer interface systems can be used as a means of communication in this group of users, since they allow them to control external devices with the help of brain activity. This assistive technology represents a new communication channel between the human brain and computers, or other devices. These systems translate the user's intention, reflected in brain signals, into the output used to control an external device, without muscular activity. Objective. The aim of this paper was to describe and list basic types and divisions of the brain-computer interface system, as well as their development for communication in people with severe motor deficits. Methods. A systematic review of the available literature was performed by searching electronic databases available through the service of the Serbian Library Consortium for Coordinated Acquisition - KOBSON, as well as through Google Scholar and Research Gate. Conclusion. Early communication systems, based on the interpretation of brain waves, provided answers to simple "yes/no" questions. After that, they were also used in spelling methods, whose speed became higher over time. The development of a brain-computer interface system aims to reduce the length of training, as time-consuming training is one of the main obstacles for the users. In addition, research in this area is aimed at improving performance in all parts of the communication system, as well as developing a reliable interface between users and computers.

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“…BMI is a novel technique that captures brain activity while performing or attempting to perform motor and/or cognitive tasks, and processing it into useful feedback (Chamola et al, 2020 ). This feedback can be expressed in many forms, including feedback of a visual (Verbaarschot et al, 2021 ), auditory (Hubner et al, 2018 ) or tactile nature (Fleury et al, 2020 ), or even control signals for external devices (Vilela and Hochberg, 2020 ). BMI can be invasive or non-invasive and due to safety and ethical issues, non-invasive may be more promising for clinical applications.…”

Section: Introductionmentioning

confidence: 99%

Brain-machine interface-based training for improving upper extremity function after stroke: A meta-analysis of randomized controlled trials

et al. 2022

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BackgroundUpper extremity dysfunction after stroke is an urgent clinical problem that greatly affects patients' daily life and reduces their quality of life. As an emerging rehabilitation method, brain-machine interface (BMI)-based training can extract brain signals and provide feedback to form a closed-loop rehabilitation, which is currently being studied for functional restoration after stroke. However, there is no reliable medical evidence to support the effect of BMI-based training on upper extremity function after stroke. This review aimed to evaluate the efficacy and safety of BMI-based training for improving upper extremity function after stroke, as well as potential differences in efficacy of different external devices.MethodsEnglish-language literature published before April 1, 2022, was searched in five electronic databases using search terms including “brain-computer/machine interface”, “stroke” and “upper extremity.” The identified articles were screened, data were extracted, and the methodological quality of the included trials was assessed. Meta-analysis was performed using RevMan 5.4.1 software. The GRADE method was used to assess the quality of the evidence.ResultsA total of 17 studies with 410 post-stroke patients were included. Meta-analysis showed that BMI-based training significantly improved upper extremity motor function [standardized mean difference (SMD) = 0.62; 95% confidence interval (CI) (0.34, 0.90); I2 = 38%; p < 0.0001; n = 385; random-effects model; moderate-quality evidence]. Subgroup meta-analysis indicated that BMI-based training significantly improves upper extremity motor function in both chronic [SMD = 0.68; 95% CI (0.32, 1.03), I2 = 46%; p = 0.0002, random-effects model] and subacute [SMD = 1.11; 95%CI (0.22, 1.99); I2 = 76%; p = 0.01; random-effects model] stroke patients compared with control interventions, and using functional electrical stimulation (FES) [SMD = 1.11; 95% CI (0.67, 1.54); I2 = 11%; p < 0.00001; random-effects model]or visual feedback [SMD = 0.66; 95% CI (0.2, 1.12); I2 = 4%; p = 0.005; random-effects model;] as the feedback devices in BMI training was more effective than using robot. In addition, BMI-based training was more effective in improving patients' activities of daily living (ADL) than control interventions [SMD = 1.12; 95% CI (0.65, 1.60); I2 = 0%; p < 0.00001; n = 80; random-effects model]. There was no statistical difference in the dropout rate and adverse effects between the BMI-based training group and the control group.ConclusionBMI-based training improved upper limb motor function and ADL in post-stroke patients. BMI combined with FES or visual feedback may be a better combination for functional recovery than robot. BMI-based trainings are well-tolerated and associated with mild adverse effects.

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A visual brain-computer interface as communication aid for patients with amyotrophic lateral sclerosis

Cited by 24 publications

References 26 publications

Brain–computer interfaces based on code-modulated visual evoked potentials (c-VEP): a literature review

Brain–computer interfaces based on code-modulated visual evoked potentials (c-VEP): a literature review

Application of brain-computer interface system in communication of people with severe motor deficits

Brain-machine interface-based training for improving upper extremity function after stroke: A meta-analysis of randomized controlled trials

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