Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges

Millán, José del R.; Rupp, Rüdiger; Müller-Putz, Gernot; Murray-Smith, Roderick; Giugliemma, Claudio; Tangermann, Michael; Vidaurre, Carmen; Cincotti, Febo; Kübler, Andrea; Leeb, Robert; Neuper, Christa; Müller, Klaus Robert; Mattia, Donatella

doi:10.3389/fnins.2010.00161

Cited by 593 publications

(440 citation statements)

References 191 publications

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“…The present data show that the computer-controlled induction of hand ownership alters -at least partly -the same fronto-parietal oscillations that can be used in non-invasive brain computer interfaces. These approaches have utilized motor imagery for motor control via online exploitation of spectral features from the mu-and beta-bands (8-26 Hz) over sensorimotor cortex (Lebedev and Nicolelis, 2006;Millán et al, 2010;Pfurtscheller and Neuper, 2001;Pfurtscheller et al, 1997b;Wolpaw and McFarland, 2004). Based on the present findings, we argue that automatized illusory hand ownership may be used to guide or improve control of external devices including robotic arms using non-invasive brain computer interface technology as well as to control prosthetic arms that are interfaced with the peripheral nervous system (Marasco et al, 2011;Navarro et al, 2005).…”

Section: Shared Spectral and Anatomical Mechanisms Between Motor Imagmentioning

confidence: 60%

Shared electrophysiology mechanisms of body ownership and motor imagery

Evans

Blanke

2013

NeuroImage

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Although we feel, see, and experience our hands as our own (body or hand ownership), recent research has shown that illusory hand ownership can be induced for fake or virtual hands and may be useful for neuroprosthetics and brain-computer interfaces. Despite the vast amount of behavioral data on illusory hand ownership, neuroimaging studies are rare, in particular electrophysiological studies. Thus, while the neural systems underlying hand ownership are relatively well described, the spectral signatures of body ownership as measured by electroencephalography (EEG) remain elusive. Here we induced illusory hand ownership in an automated, computer-controlled manner using virtual reality while recording 64-channel EEG and found that illusory hand ownership is reflected by a body-specific modulation in the mu-band over fronto-parietal cortex. In a second experiment in the same subjects, we then show that mu as well as beta-band activity in highly similar fronto-parietal regions was also modulated during a motor imagery task often used in paradigms employing non-invasive brain-computer interface technology. These data provide insights into the electrophysiological brain mechanisms of illusory hand ownership and their strongly overlapping mechanisms with motor imagery in fronto-parietal cortex. They also highlight the potential of combining high-resolution EEG with virtual reality setups and automatized stimulation protocols for systematic, reproducible stimulus presentation in cognitive neuroscience, and may inform the design of non-invasive brain-computer interfaces.

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Section: Shared Spectral and Anatomical Mechanisms Between Motor Imagmentioning

confidence: 60%

Shared electrophysiology mechanisms of body ownership and motor imagery

Evans

Blanke

2013

NeuroImage

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“…This approach could also be potentially useful for online applications, such as in the field of brain-computer interfaces (BCI; (Millan et al, 2010)). BCI applications usually require a much higher decoding performance than the one we obtained here, but nevertheless, the detection of the critical t* for each single decision could provide a realistic upper bound of when to stop accumulating EEG activity for accurately predicting a decision.…”

Section: Comparison With Other Single-trial Methodsmentioning

confidence: 99%

Quantifying the time for accurate EEG decoding of single value-based decisions

Tzovara

Chavarriaga

Lucia

2015

Journal of Neuroscience Methods

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h i g h l i g h t s• We outline a novel method for assessing time-unlocked topographic EEG activity.• We apply this method for accurately predicting accept vs. reject decisions.• The time when decisions have been made is modulated by task difficulty.• It occurs well before subjects' button press (∼340 ms).• This time is compatible with a diffusion process, evaluated at behavioral level. a r t i c l e i n f o b s t r a c tBackground: Recent neuroimaging studies suggest that value-based decision-making may rely on mechanisms of evidence accumulation. However no studies have explicitly investigated the time when single decisions are taken based on such an accumulation process. New method: Here, we outline a novel electroencephalography (EEG) decoding technique which is based on accumulating the probability of appearance of prototypical voltage topographies and can be used for predicting subjects' decisions. We use this approach for studying the time-course of single decisions, during a task where subjects were asked to compare reward vs. loss points for accepting or rejecting offers. Results: We show that based on this new method, we can accurately decode decisions for the majority of the subjects. The typical time-period for accurate decoding was modulated by task difficulty on a trial-bytrial basis. Typical latencies of when decisions are made were detected at ∼500 ms for 'Easy' vs. ∼700 ms for 'Hard' decisions, well before subjects' response (∼340 ms). Importantly, this decision time correlated with the drift rates of a diffusion model, evaluated independently at the behavioral level. Comparison with existing method(s):We compare the performance of our algorithm with logistic regression and support vector machine and show that we obtain significant results for a higher number of subjects than with these two approaches. We also carry out analyses at the average event-related potential level, for comparison with previous studies on decision-making. Conclusions: We present a novel approach for studying the timing of value-based decision-making, by accumulating patterns of topographic EEG activity at single-trial level.

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“…Indeed, the possibility of voluntarily modulating specific brain patterns through MI has been widely used as a control input for artificial devices [27]. In many of these studies control commands are delivered to devices on largely granular time-scales.…”

Section: Brain-machinementioning

confidence: 99%

mano: A Wearable Hand Exoskeleton for Activities of Daily Living and Neurorehabilitation

Randazzo

Iturrate

Perdikis

et al. 2018

IEEE Robot. Autom. Lett.

117

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Abstract-Hand sensorimotor impairments are among the most common consequences of injuries affecting the central and peripheral nervous systems, leading to a drastic reduction in the quality of life for affected individuals. Combining wearable robotic exoskeletons and human-machine interfaces is a promising avenue for the restoration and substitution of lost and impaired functions for these users. In this study, we present a novel hand exoskeleton, mano, designed to assist and restore hand functions of people with motor disabilities during activities of daily living (ADL) and in neurorehabilitative scenarios. Compared to state-of-the-art devices, our system is fully wearable, portable and minimally obtrusive on the hand. The exoskeleton can actively control flexion and extension of all fingers, while allowing natural somatosensorial interactions with the environment surrounding the users. We evaluated the device from four different perspectives. A mechanical characterization, showing that the exoskeleton can cover more than 70% of healthy hand workspace and it can achieve forces at the fingertips sufficient for ADL. A functional characterization, where we showed how two users who suffered from spinal cord injuries were able to perform several ADL for the first time since their accidents. Thirdly, we evaluated the system from a neuroimaging perspective, showing that the device can elicit EEG brain patterns typical of natural hand motions. We finally exemplified the control of the hand exoskeleton within an exemplar framework, a brain-machine interface scenario, showing how motor intention can be successfully decoded for a continuous control of the device. Overall, our results showed that the device represents an ecological solution for use both in ADL and in scenarios aimed at promoting sensorimotor recovery.

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Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges

Cited by 593 publications

References 191 publications

Shared electrophysiology mechanisms of body ownership and motor imagery

Shared electrophysiology mechanisms of body ownership and motor imagery

Quantifying the time for accurate EEG decoding of single value-based decisions

mano: A Wearable Hand Exoskeleton for Activities of Daily Living and Neurorehabilitation

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