Noninvasive brain-computer interface enables communication after brainstem stroke

Sellers, Eric W.; Ryan, David B.; Hauser, Christopher K.

doi:10.1126/scitranslmed.3007801

Cited by 112 publications

(79 citation statements)

References 23 publications

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“…An EEG-based BCI translated purposeful modulation of SCP into binary selections of letters or words on a screen (Birbaumer et al, 1999. Recently, such system was successfully used in a tetraplegic patient with brainstem stroke (Sellers et al, 2014). While restoration of communication was the only relevant BMI application for a long time, the impact and relevance of both non-invasive and implantable assistive BMIs for restoration of movement were negligible.…”

Section: Brain-machine Interfaces (Bmis)mentioning

confidence: 99%

Brain–machine interfaces in neurorehabilitation of stroke

Soekadar

Birbaumer

Slutzky

et al. 2015

Neurobiology of Disease

277

180

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Stroke is among the leading causes of long-term disabilities leaving an increasing number of people with cognitive, affective and motor impairments depending on assistance in their daily life. While function after stroke can significantly improve in the first weeks and months, further recovery is often slow or non-existent in the more severe cases encompassing 30-50% of all stroke victims. The neurobiological mechanisms underlying recovery in those patients are incompletely understood. However, recent studies demonstrated the brain's remarkable capacity for functional and structural plasticity and recovery even in severe chronic stroke. As all established rehabilitation strategies require some remaining motor function, there is currently no standardized and accepted treatment for patients with complete chronic muscle paralysis. The development of brain-machine interfaces (BMIs) that translate brain activity into control signals of computers or external devices provides two new strategies to overcome stroke-related motor paralysis. First, BMIs can establish continuous high-dimensional brain-control of robotic devices or functional electric stimulation (FES) to assist in daily life activities (assistive BMI). Second, BMIs could facilitate neuroplasticity, thus enhancing motor learning and motor recovery (rehabilitative BMI). Advances in sensor technology, development of non-invasive and implantable wireless BMI-systems and their combination with brain stimulation, along with evidence for BMI systems' clinical efficacy suggest that BMI-related strategies will play an increasing role in neurorehabilitation of stroke.

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Section: Brain-machine Interfaces (Bmis)mentioning

confidence: 99%

Brain–machine interfaces in neurorehabilitation of stroke

Soekadar

Birbaumer

Slutzky

et al. 2015

Neurobiology of Disease

277

180

View full text Add to dashboard Cite

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“…On the other hand, there are single reports of successful BCI system transitions to end-users’ homes [23-25]. End-users in these studies not only fulfilled internal prerequisites for successful BCI use, but were also supported by caregivers or family members and thereby share beneficial external preconditions.…”

Section: End-user-related Issuesmentioning

confidence: 99%

“…So far, only rare cases of long-term independent BCI use were reported [23-25], which might be due to limited resources available. Not only is the hardware expensive, also experts need time to adjust the system properly.…”

Section: End-user-related Issuesmentioning

confidence: 99%

Heading for new shores! Overcoming pitfalls in BCI design

Chavarriaga

Fried-Oken²,

Kleih

et al. 2016

Brain-Computer Interfaces

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Research in brain-computer interfaces has achieved impressive progress towards implementing assistive technologies for restoration or substitution of lost motor capabilities, as well as supporting technologies for able-bodied subjects. Notwithstanding this progress, effective translation of these interfaces from proof-of concept prototypes into reliable applications remains elusive. As a matter of fact, most of the current BCI systems cannot be used independently for long periods of time by their intended end-users. Multiple factors that impair achieving this goal have already been identified. However, it is not clear how do they affect the overall BCI performance or how they should be tackled. This is worsened by the publication bias where only positive results are disseminated, preventing the research community from learning from its errors. This paper is the result of a workshop held at the 6th International BCI meeting in Asilomar. We summarize here the discussion on concrete research avenues and guidelines that may help overcoming common pitfalls and make BCIs become a useful alternative communication device.

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“…Despite the difficulties, a considerable number of studies, although with low sample sizes, were conducted to fulfil exactly this request and refer explicitly to the UCD [11][12][13][14][15][16][17][18][19]. A few more were also conducted with patient end-users as a focus but without referring to the UCD [4,[20][21][22]. The late 2014 and 2015 papers may have been not available when Nijboer's manuscript went in press, but the earlier ones were.…”

mentioning

confidence: 94%

User-centred design in brain–computer interface research and development

Kübler

Müller-Putz

Mattia

2015

Annals of Physical and Rehabilitation Medicine

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In a recent article [1], Nijboer addressed an important topic for brain-computer interface (BCI) research and development: How can we develop a technology that will in the end be used by the target population? [1]. We have few recent studies demonstrating the independent home use of BCI [2][3][4]. Although these were single case studies, they do show the potential feasibility of BCI for the target population and provide valuable information on the prerequisites for transferring BCI to patients' homes and for reliable operation.Despite these exemplary success stories, indeed a wide gap exists between the amount of research conducted in laboratory settings with healthy subjects and translational research with targeted end-users, specifically in their home environment and without experts being present. Fig. 1 illustrates this gap: the overall number of BCI-related publications is high and has been almost exponentially increasing in the past 2 decades; however, those including or referring to patients are about 10-fold lower but are fortunately also increasing.As one measure to address the issue of technology transfer, the user-centred design (UCD) is recommended and requested to be adopted. In this iterative process, the developers and users communicate about the requirements of a product and its implementation, with the aim of the final product being used in the daily life of the target population. We agree that the UCD provides an excellent framework to foster BCI development together with the target users. Valuable work in this direction has been performed in recent years by the BCI community; for example, the first paper introducing the UCD in a BCI study with patient end-users was published in 2011 [5]. Kü bler and colleagues adapted the UCD to BCI with a sample of 19 patients with severe motor paralysis and the locked-in state (see Fig. 2) [6]. As requested by Nijboer, usability as defined by the ISO 9241-210 [7] was addressed with its components effectiveness, efficiency, and satisfaction. Effectiveness (i.e., how well the task can be mastered by the target group) was defined as accuracy. Accuracy indicates how often a correct selection occurred in relation to the total number of attempts. Efficiency (i.e., how much effort is needed to be effective) was operationalised as information transfer rate (ITR) and workload. ITR is an objective measure that captures how much information can be transferred per time unit and takes into account how many options for selection exist. Additionally, the utility metric was suggested, because if accuracy is < 50%, practically, no information can be conveyed if more than every other selection is wrong. Workload as a subjective measure can be assessed with the NASA Task Load Index [8]. Satisfaction was suggested to be measured by the Quebec User Evaluation of Satisfaction [9], which was adapted to BCI in that 4 BCI-relevant items were included: reliability, speed, learnability, and aesthetic design [5], all features considered important by Nijboer. Visual analogue scales were s...

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Noninvasive brain-computer interface enables communication after brainstem stroke

Cited by 112 publications

References 23 publications

Brain–machine interfaces in neurorehabilitation of stroke

Brain–machine interfaces in neurorehabilitation of stroke

Heading for new shores! Overcoming pitfalls in BCI design

User-centred design in brain–computer interface research and development

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