Non-invasive control interfaces for intention detection in active movement-assistive devices

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Abstract

Active movement-assistive devices aim to increase the quality of life for patients with neuromusculoskeletal disorders. This technology requires interaction between the user and the device through a control interface that detects the user’s movement intention. Researchers have explored a wide variety of invasive and non-invasive control interfaces. To summarize the wide spectrum of strategies, this paper presents a comprehensive review focused on non-invasive control interfaces used to operate active movement-assistive devices. A novel systematic classification method is proposed to categorize the control interfaces based on: (I) the source of the physiological signal, (II) the physiological phenomena responsible for generating the signal, and (III) the sensors used to measure the physiological signal. The proposed classification method can successfully categorize all the existing control interfaces providing a comprehensive overview of the state of the art. Each sensing modality is briefly described in the body of the paper following the same structure used in the classification method. Furthermore, we discuss several design considerations, challenges, and future directions of non-invasive control interfaces for active movement-assistive devices.

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The online version of this article (doi:10.1186/1743-0003-11-168) contains supplementary material, which is available to authorized users.

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Brain Computer Interfaces, a Review

Luis Nicolas-Alonso, Jaime Gomez-Gil (2012)

A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or ‘locked in’ by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.

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Targeted muscle reinnervation for real-time myoelectric control of multifunction artificial arms.

Todd Kuiken, Guanglin Li, Blair A. Lock … (2009)

Improving the function of prosthetic arms remains a challenge, because access to the neural-control information for the arm is lost during amputation. A surgical technique called targeted muscle reinnervation (TMR) transfers residual arm nerves to alternative muscle sites. After reinnervation, these target muscles produce electromyogram (EMG) signals on the surface of the skin that can be measured and used to control prosthetic arms. To assess the performance of patients with upper-limb amputation who had undergone TMR surgery, using a pattern-recognition algorithm to decode EMG signals and control prosthetic-arm motions. Study conducted between January 2007 and January 2008 at the Rehabilitation Institute of Chicago among 5 patients with shoulder-disarticulation or transhumeral amputations who underwent TMR surgery between February 2002 and October 2006 and 5 control participants without amputation. Surface EMG signals were recorded from all participants and decoded using a pattern-recognition algorithm. The decoding program controlled the movement of a virtual prosthetic arm. All participants were instructed to perform various arm movements, and their abilities to control the virtual prosthetic arm were measured. In addition, TMR patients used the same control system to operate advanced arm prosthesis prototypes. Performance metrics measured during virtual arm movements included motion selection time, motion completion time, and motion completion ("success") rate. The TMR patients were able to repeatedly perform 10 different elbow, wrist, and hand motions with the virtual prosthetic arm. For these patients, the mean motion selection and motion completion times for elbow and wrist movements were 0.22 seconds (SD, 0.06) and 1.29 seconds (SD, 0.15), respectively. These times were 0.06 seconds and 0.21 seconds longer than the mean times for control participants. For TMR patients, the mean motion selection and motion completion times for hand-grasp patterns were 0.38 seconds (SD, 0.12) and 1.54 seconds (SD, 0.27), respectively. These patients successfully completed a mean of 96.3% (SD, 3.8) of elbow and wrist movements and 86.9% (SD, 13.9) of hand movements within 5 seconds, compared with 100% (SD, 0) and 96.7% (SD, 4.7) completed by controls. Three of the patients were able to demonstrate the use of this control system in advanced prostheses, including motorized shoulders, elbows, wrists, and hands. These results suggest that reinnervated muscles can produce sufficient EMG information for real-time control of advanced artificial arms.

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Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke.

Ethan Buch, Cornelia Weber, Leonardo Cohen … (2008)

Stroke is a leading cause of long-term motor disability among adults. Present rehabilitative interventions are largely unsuccessful in improving the most severe cases of motor impairment, particularly in relation to hand function. Here we tested the hypothesis that patients experiencing hand plegia as a result of a single, unilateral subcortical, cortical or mixed stroke occurring at least 1 year previously, could be trained to operate a mechanical hand orthosis through a brain-computer interface (BCI). Eight patients with chronic hand plegia resulting from stroke (residual finger extension function rated on the Medical Research Council scale=0/5) were recruited from the Stroke Neurorehabilitation Clinic, Human Cortical Physiology Section of the National Institute for Neurological Disorders and Stroke (NINDS) (n=5) and the Clinic of Neurology of the University of Tübingen (n=3). Diagnostic MRIs revealed single, unilateral subcortical, cortical or mixed lesions in all patients. A magnetoencephalography-based BCI system was used for this study. Patients participated in between 13 to 22 training sessions geared to volitionally modulate micro rhythm amplitude originating in sensorimotor areas of the cortex, which in turn raised or lowered a screen cursor in the direction of a target displayed on the screen through the BCI interface. Performance feedback was provided visually in real-time. Successful trials (in which the cursor made contact with the target) resulted in opening/closing of an orthosis attached to the paralyzed hand. Training resulted in successful BCI control in 6 of 8 patients. This control was associated with increased range and specificity of mu rhythm modulation as recorded from sensors overlying central ipsilesional (4 patients) or contralesional (2 patients) regions of the array. Clinical scales used to rate hand function showed no significant improvement after training. These results suggest that volitional control of neuromagnetic activity features recorded over central scalp regions can be achieved with BCI training after stroke, and used to control grasping actions through a mechanical hand orthosis.

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Author and article information

Contributors

Joan Lobo-Prat: j.loboprat@utwente.nl

Peter N Kooren: p.kooren@vumc.nl

Arno HA Stienen: arnostienen@gmail.com

Just L Herder: j.l.herder@tudelft.nl

Bart FJM Koopman: h.f.j.m.koopman@utwente.nl

Peter H Veltink: p.h.veltink@utwente.nl

Journal

Journal ID (nlm-ta): J Neuroeng Rehabil

Journal ID (iso-abbrev): J Neuroeng Rehabil

Title: Journal of NeuroEngineering and Rehabilitation

Publisher: BioMed Central (London )

ISSN (Electronic): 1743-0003

Publication date (Electronic): 17 December 2014

Publication date PMC-release: 17 December 2014

Publication date Collection: 2014

Volume: 11

Electronic Location Identifier: 168

Affiliations

[ ]Department of Biomechanical Engineering, University of Twente, Drienerlolaan 5, 7522, NB Enschede, The Netherlands

[ ]Department of Physics and Medical Technology, VU University Medical Center, Van der Boechorststraat 7, 1081 BT Amsterdam, The Netherlands

[ ]Department of Physical Therapy and Human Movement Sciences, Northwestern University, 645 N. Michigan Ave. Suite 1100, 60611 Chicago, IL USA

[ ]Department of Precision and Microsystems Engineering, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands

[ ]Department Mechanical Automation and Mechatronics, University of Twente, Drienerlolaan 5, 7500 AE Enschede, The Netherlands

[ ]Department of Biomedical Signals and Systems, University of Twente, Drienerlolaan 5, 7500 AE Enschede, The Netherlands

Article

Publisher ID: 714

DOI: 10.1186/1743-0003-11-168

PMC ID: 4459663

PubMed ID: 25516421

SO-VID: 5c72d81f-6716-44cb-b93d-83dbf40f49fe

License:

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 10 June 2014

Date accepted : 5 December 2014

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ScienceOpen disciplines: Neurosciences

Keywords: non-invasive control interface,active movement-assistive devices,motion intention detection,biomechatronics,human movement control system

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ScienceOpen disciplines: Neurosciences

Keywords: non-invasive control interface, active movement-assistive devices, motion intention detection, biomechatronics, human movement control system

Non-invasive control interfaces for intention detection in active movement-assistive devices

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The Dynamic Brain

Most cited references 182

Brain Computer Interfaces, a Review

Targeted muscle reinnervation for real-time myoelectric control of multifunction artificial arms.

Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke.

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