Combining Brain–Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges

By J. Millán, R. Rupp, Gernot R. Müller-Putz, Roderick Murray-Smith, Claudio Giugliemma, Michael W. Tangermann, Carmen Vidaurre, Febo Cincotti, Andrea Kübler, Robert Leeb and 3 more. Frontiers in Neuroscience, 2010

919 citations recorded by the source corpus.

In recent years, new research has brought the field of electroencephalogram (EEG)-based brain–computer interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely, “Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user–machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human–computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/32f18ddc6910d38eda0739aa986248a7cacdf065

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Citations in the corpus

Control strategies for active lower extremity prosthetics and orthotics: a review (2015, positive): “Even so, a recent pilot study investigated the use of an fNIRS-BCI to detect the preparation for movement of the hip in seated stroke subjects, which may indicate its suitability in shared control with severely impaired subjects [50].”
Visual and Auditory Brain–Computer Interfaces (2014, background): “In the BCI literature, the published review papers tend to focus on specific engineering aspects such as signal processing [24, 25], classification [26, 27, 28], general system design [29, 30, 31, 32, 33], or applications [34, 35].”
An overview of the Internet of Things for people with disabilities (2012, background): “Disabled people will benefit from the advancements in BCI technology combined with assistive technologies in four basic application areas (Millán et al., 2010): communication and control (Internet browsing, e-mails), motor substitution (in particular grasping and assistive mobility), entertainment…”
Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals (2011, neutral): “in combination with other assitive technology [1].”
Brain-machine interfaces in neurorehabilitation of stroke. (2015, positive): “Encouraged by these findings, a larger clinical trial with 32 chronic stroke survivors without residual finger movements (12.22 ± 1.51 out of 54 points according to a combined hand and modified arm FMA, cFMA, indicating severe upper-limb motor impairment) was conducted and showed that motor improvem”
Robust artifactual independent component classification for BCI practitioners (2014, background): “The exclusive use of brain signals in BCI must typically be dropped when it comes to practical tests with end-users in need, as hybrid BCI approaches [5, 6] provide a richer and more reliable control than pure BCIs.”
BCILAB: a platform for brain–computer interface development (2013, neutral): “severely disabled persons) [11, 12], passive assessment of”
EEG-Based Brain-Controlled Mobile Robots: A Survey (2013, background): “However, they are not yet mature, and some researchers have doubts about what physiological signals these systems actually acquire [3].”
Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review (2021, background): “In general, for noninvasive BCIs, EEG is used extensively and is preferred over other recording techniques due to its ease of use, portability, low cost, and high temporal resolution [43].”
Brain-Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives (2014, neutral): “Other applications include those for entertainment purpose [118], which, however, is not the focus of this”