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Svensson P, Malešević N, Wijk U, Björkman A, Antfolk C. The rubber hand illusion evaluated using different stimulation modalities. Front Neurosci 2023; 17:1237053. [PMID: 37781250 PMCID: PMC10536259 DOI: 10.3389/fnins.2023.1237053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 08/30/2023] [Indexed: 10/03/2023] Open
Abstract
Tactile feedback plays a vital role in inducing ownership and improving motor control of prosthetic hands. However, commercially available prosthetic hands typically do not provide tactile feedback and because of that the prosthetic user must rely on visual input to adjust the grip. The classical rubber hand illusion (RHI) where a brush is stroking the rubber hand, and the user's hidden hand synchronously can induce ownership of a rubber hand. In the classic RHI the stimulation is modality-matched, meaning that the stimulus on the real hand matches the stimulus on the rubber hand. The RHI has also been used in previous studies with a prosthetic hand as the "rubber hand," suggesting that a hand prosthesis can be incorporated within the amputee's body scheme. Interestingly, previous studies have shown that stimulation with a mismatched modality, where the rubber hand was brushed, and vibrations were felt on the hidden hand also induced the RHI. The aim of this study was to compare how well mechanotactile, vibrotactile, and electrotactile feedback induced the RHI in able-bodied participants and forearm amputees. 27 participants with intact hands and three transradial amputees took part in a modified RHI experiment. The rubber hand was stroked with a brush, and the participant's hidden hand/residual limb received stimulation with either brush stroking, electricity, pressure, or vibration. The three latter stimulations were modality mismatched with regard to the brushstroke. Participants were tested for ten different combinations (stimulation blocks) where the stimulations were applied on the volar (glabrous skin), and dorsal (hairy skin) sides of the hand. Outcome was assessed using two standard tests (questionnaire and proprioceptive drift). All types of stimulation induced RHI but electrical and vibration stimulation induced a stronger RHI than pressure. After completing more stimulation blocks, the proprioceptive drift test showed that the difference between pre- and post-test was reduced. This indicates that the illusion was drifting toward the rubber hand further into the session.
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Affiliation(s)
- Pamela Svensson
- Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden
| | - Nebojša Malešević
- Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden
| | - Ulrika Wijk
- Department of Translational Medicine – Hand Surgery, Lund University and Skåne University Hospital, Malmö, Sweden
| | - Anders Björkman
- Department of Hand Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Christian Antfolk
- Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden
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Olsson AE, Malešević N, Björkman A, Antfolk C. End-to-End Estimation of Hand- and Wrist Forces From Raw Intramuscular EMG Signals Using LSTM Networks. Front Neurosci 2021; 15:777329. [PMID: 34867175 PMCID: PMC8635710 DOI: 10.3389/fnins.2021.777329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 10/26/2021] [Indexed: 11/13/2022] Open
Abstract
Processing myoelectrical activity in the forearm has for long been considered a promising framework to allow transradial amputees to control motorized prostheses. In spite of expectations, contemporary muscle-computer interfaces built for this purpose typically fail to satisfy one or more important desiderata, such as accuracy, robustness, and/or naturalness of control, in part due to difficulties in acquiring high-quality signals continuously outside laboratory conditions. In light of such problems, surgically implanted electrodes have been made a viable option that allows for long-term acquisition of intramuscular electromyography (iEMG) measurements of spatially precise origin. As it stands, the question of how information embedded in such signals is best extracted and combined across multiple channels remains open. This study presents and evaluates an approach to this end that uses deep neural networks based on the Long Short-Term Memory (LSTMs) architecture to regress forces exerted by multiple degrees of freedom (DoFs) from multichannel iEMG. Three deep learning models, representing three distinct regression strategies, were evaluated: (I) One-to-One, wherein each DoF is separately estimated by an LSTM model processing a single iEMG channel, (II) All-to-One, wherein each DoF is separately estimated by an LSTM model processing all iEMG channels, and (III) All-to-All, wherein a single LSTM model with access to all iEMG channels estimates all DoFs simultaneously. All models operate on raw iEMG, with no preliminary feature extraction required. When evaluated on a dataset comprising six iEMG channels with concurrent force measurements acquired from 14 subjects, all LSTM strategies were found to significantly outperform a baseline feature-based linear control regression method. This finding indicates that recurrent neural networks can learn to transform raw forearm iEMG signals directly into representations that correlate with forces exerted at the level of the hand to a greater degree than simple features do. Furthermore, the All-to-All and All-to-One strategies were found to exhibit better performance than the One-to-One strategy. This finding suggests that, in spite of the spatially local nature of signals, iEMG from muscles not directly actuating the relevant DoF can provide contextual information that aid in decoding motor intent.
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Affiliation(s)
- Alexander E Olsson
- Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden
| | - Nebojša Malešević
- Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden
| | - Anders Björkman
- Department of Hand Surgery, Sahlgrenska University Hospital, Institute of Clinical Sciences, University of Gothenburg, Sahlgrenska Academy, Gothenburg, Sweden.,Department of Hand Surgery, Skåne University Hospital, Malmö, Sweden.,Department of Translational Medicine, Lund University, Lund, Sweden
| | - Christian Antfolk
- Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden
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Malešević N, Olsson A, Sager P, Andersson E, Cipriani C, Controzzi M, Björkman A, Antfolk C. A database of high-density surface electromyogram signals comprising 65 isometric hand gestures. Sci Data 2021; 8:63. [PMID: 33602931 PMCID: PMC7892548 DOI: 10.1038/s41597-021-00843-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 01/19/2021] [Indexed: 11/09/2022] Open
Abstract
Control of contemporary, multi-joint prosthetic hands is commonly realized by using electromyographic signals from the muscles remaining after amputation at the forearm level. Although this principle is trying to imitate the natural control structure where muscles control the joints of the hand, in practice, myoelectric control provides only basic hand functions to an amputee using a dexterous prosthesis. This study aims to provide an annotated database of high-density surface electromyographic signals to aid the efforts of designing robust and versatile electromyographic control interfaces for prosthetic hands. The electromyographic signals were recorded using 128 channels within two electrode grids positioned on the forearms of 20 able-bodied volunteers. The participants performed 65 different hand gestures in an isometric manner. The hand movements were strictly timed using an automated recording protocol which also synchronously recorded the electromyographic signals and hand joint forces. To assess the quality of the recorded signals several quantitative assessments were performed, such as frequency content analysis, channel crosstalk, and the detection of poor skin-electrode contacts.
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Affiliation(s)
- Nebojša Malešević
- Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden.
| | - Alexander Olsson
- Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden
| | - Paulina Sager
- Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden
| | - Elin Andersson
- Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden
| | - Christian Cipriani
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Marco Controzzi
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Anders Björkman
- Department of Hand Surgery, Clinical Sciences, Sahlgrenska Academy, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Christian Antfolk
- Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden.
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Olsson AE, Malešević N, Björkman A, Antfolk C. Learning regularized representations of categorically labelled surface EMG enables simultaneous and proportional myoelectric control. J Neuroeng Rehabil 2021; 18:35. [PMID: 33588868 PMCID: PMC7885418 DOI: 10.1186/s12984-021-00832-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 02/02/2021] [Indexed: 11/18/2022] Open
Abstract
Background Processing the surface electromyogram (sEMG) to decode movement intent is a promising approach for natural control of upper extremity prostheses. To this end, this paper introduces and evaluates a new framework which allows for simultaneous and proportional myoelectric control over multiple degrees of freedom (DoFs) in real-time. The framework uses multitask neural networks and domain-informed regularization in order to automatically find nonlinear mappings from the forearm sEMG envelope to multivariate and continuous encodings of concurrent hand- and wrist kinematics, despite only requiring categorical movement instruction stimuli signals for calibration. Methods Forearm sEMG with 8 channels was collected from healthy human subjects (N = 20) and used to calibrate two myoelectric control interfaces, each with two output DoFs. The interfaces were built from (I) the proposed framework, termed Myoelectric Representation Learning (MRL), and, to allow for comparisons, from (II) a standard pattern recognition framework based on Linear Discriminant Analysis (LDA). The online performances of both interfaces were assessed with a Fitts’s law type test generating 5 quantitative performance metrics. The temporal stabilities of the interfaces were evaluated by conducting identical tests without recalibration 7 days after the initial experiment session. Results Metric-wise two-way repeated measures ANOVA with factors method (MRL vs LDA) and session (day 1 vs day 7) revealed a significant (\documentclass[12pt]{minimal}
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\begin{document}$$\left|d\right|=1.13$$\end{document}d=1.13. No significant effect on any metric was detected for neither session nor interaction between method and session, indicating that none of the methods deteriorated significantly in control efficacy during one week of intermission. Conclusions The results suggest that MRL is able to successfully generate stable mappings from EMG to kinematics, thereby enabling myoelectric control with real-time performance superior to that of the current commercial standard for pattern recognition (as represented by LDA). It is thus postulated that the presented MRL approach can be of practical utility for muscle-computer interfaces.
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Affiliation(s)
- Alexander E Olsson
- Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden.
| | - Nebojša Malešević
- Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden
| | - Anders Björkman
- Department of Hand Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, Sahlgrenska University Hospital and University of Gothenburg, Gothenburg, Sweden.,Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden
| | - Christian Antfolk
- Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden.
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Malešević N, Petrović V, Belić M, Antfolk C, Mihajlović V, Janković M. Contactless Real-Time Heartbeat Detection via 24 GHz Continuous-Wave Doppler Radar Using Artificial Neural Networks. Sensors (Basel) 2020; 20:E2351. [PMID: 32326190 PMCID: PMC7219229 DOI: 10.3390/s20082351] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 04/16/2020] [Accepted: 04/19/2020] [Indexed: 11/22/2022]
Abstract
The measurement of human vital signs is a highly important task in a variety of environments and applications. Most notably, the electrocardiogram (ECG) is a versatile signal that could indicate various physical and psychological conditions, from signs of life to complex mental states. The measurement of the ECG relies on electrodes attached to the skin to acquire the electrical activity of the heart, which imposes certain limitations. Recently, due to the advancement of wireless technology, it has become possible to pick up heart activity in a contactless manner. Among the possible ways to wirelessly obtain information related to heart activity, methods based on mm-wave radars proved to be the most accurate in detecting the small mechanical oscillations of the human chest resulting from heartbeats. In this paper, we presented a method based on a continuous-wave Doppler radar coupled with an artificial neural network (ANN) to detect heartbeats as individual events. To keep the method computationally simple, the ANN took the raw radar signal as input, while the output was minimally processed, ensuring low latency operation (<1 s). The performance of the proposed method was evaluated with respect to an ECG reference ("ground truth") in an experiment involving 21 healthy volunteers, who were sitting on a cushioned seat and were refrained from making excessive body movements. The results indicated that the presented approach is viable for the fast detection of individual heartbeats without heavy signal preprocessing.
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Affiliation(s)
- Nebojša Malešević
- Department of Biomedical Engineering, Faculty of Engineering, Lund University, Box 118, 221 00 Lund, Sweden;
| | - Vladimir Petrović
- School of Electrical Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11120 Belgrade, Serbia
| | - Minja Belić
- Novelic, Veljka Dugoševića 54/A3, 11000 Belgrade, Serbia; (M.B.); (V.M.)
| | - Christian Antfolk
- Department of Biomedical Engineering, Faculty of Engineering, Lund University, Box 118, 221 00 Lund, Sweden;
| | - Veljko Mihajlović
- Novelic, Veljka Dugoševića 54/A3, 11000 Belgrade, Serbia; (M.B.); (V.M.)
| | - Milica Janković
- School of Electrical Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11120 Belgrade, Serbia
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Olsson AE, Sager P, Andersson E, Björkman A, Malešević N, Antfolk C. Extraction of Multi-Labelled Movement Information from the Raw HD-sEMG Image with Time-Domain Depth. Sci Rep 2019; 9:7244. [PMID: 31076600 PMCID: PMC6510898 DOI: 10.1038/s41598-019-43676-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Accepted: 04/27/2019] [Indexed: 11/09/2022] Open
Abstract
In contemporary muscle-computer interfaces for upper limb prosthetics there is often a trade-off between control robustness and range of executable movements. As a very low movement error rate is necessary in practical applications, this often results in a quite severe limitation of controllability; a problem growing ever more salient as the mechanical sophistication of multifunctional myoelectric prostheses continues to improve. A possible remedy for this could come from the use of multi-label machine learning methods, where complex movements can be expressed as the superposition of several simpler movements. Here, we investigate this claim by applying a multi-labeled classification scheme in the form of a deep convolutional neural network (CNN) to high density surface electromyography (HD-sEMG) recordings. We use 16 independent labels to model the movements of the hand and forearm state, representing its major degrees of freedom. By training the neural network on 16 × 8 sEMG image sequences 24 samples long with a sampling rate of 2048 Hz to detect these labels, we achieved a mean exact match rate of 78.7% and a mean Hamming loss of 2.9% across 14 healthy test subjects. With this, we demonstrate the feasibility of highly versatile and responsive sEMG control interfaces without loss of accuracy.
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Affiliation(s)
- Alexander E Olsson
- Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden
| | - Paulina Sager
- Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden
| | - Elin Andersson
- Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden
| | - Anders Björkman
- Department of Hand Surgery, Skåne University Hospital, Malmö, Sweden
| | - Nebojša Malešević
- Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden
| | - Christian Antfolk
- Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden.
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Dujović SD, Malešević J, Malešević N, Vidaković AS, Bijelić G, Keller T, Konstantinović L. Novel multi-pad functional electrical stimulation in stroke patients: A single-blind randomized study. NeuroRehabilitation 2018; 41:791-800. [PMID: 29254111 DOI: 10.3233/nre-172153] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Foot drop is common gait impairment after stroke. Functional electrical stimulation (FES) of the ankle dorsiflexor muscles during the swing phase of gait can help correcting foot drop. OBJECTIVE To evaluate efficacy of additional novel FES system to conventional therapy in facilitating motor recovery in the lower extremities and improving walking ability after stroke. METHODS Sixteen stroke patients were randomly allocated to the FES group (FES therapy plus conventional rehabilitation program) (n = 8), and control group (conventional rehabilitation program) n = 8. FES was delivered for 30 min during gait to induce ankle plantar and dorsiflexion. MAIN OUTCOME MEASURES gait speed using 10 Meter Walk Test (10 MWT), Fugl-Meyer Assessment (FMA), Berg Balance Scale (BBS) and modified Barthel Index (MBI). RESULTS Results showed a significant increase in gait speed in FES group (p < 0.001), higher than the minimal detected change. The FES group showed improvement in functional independence in the activities of daily living, motor recovery and gait performance. CONCLUSIONS The findings suggest that novel FES therapy combined with conventional rehabilitation is more effective on walking speed, mobility of the lower extremity, balance disability and activities of daily living compared to a conventional rehabilitation program only.
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Affiliation(s)
- Suzana Dedijer Dujović
- The University of Belgrade, Serbia and Clinic for rehabilitation "Dr M.Zotovic", Belgrade, Serbia
| | - Jovana Malešević
- The University of Belgrade and Tecnalia Serbia Ltd., Belgrade, Serbia
| | - Nebojša Malešević
- Department of Biomedical Engineering, Lund University, Belgrade, Serbia
| | - Aleksandra S Vidaković
- Faculty of Medicine, University of Belgrade and Clinic for rehabilitation "Dr M.Zotovic", Belgrade, Serbia
| | - Goran Bijelić
- Neurorehabilitation Area at the Health Division of TECNALIA, San Sebastian, Spain
| | - Thierry Keller
- Neurorehabilitation Area at the Health Division of TECNALIA, San Sebastian, Spain
| | - Ljubica Konstantinović
- Faculty of Medicine, University of Belgrade and Clinic for rehabilitation "Dr M.Zotovic", Belgrade, Serbia
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Malešević J, Dedijer Dujović S, Savić AM, Konstantinović L, Vidaković A, Bijelić G, Malešević N, Keller T. A decision support system for electrode shaping in multi-pad FES foot drop correction. J Neuroeng Rehabil 2017; 14:66. [PMID: 28673311 PMCID: PMC5496361 DOI: 10.1186/s12984-017-0275-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2016] [Accepted: 06/18/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Functional electrical stimulation (FES) can be applied as an assistive and therapeutic aid in the rehabilitation of foot drop. Transcutaneous multi-pad electrodes can increase the selectivity of stimulation; however, shaping the stimulation electrode becomes increasingly complex with an increasing number of possible stimulation sites. We described and tested a novel decision support system (DSS) to facilitate the process of multi-pad stimulation electrode shaping. The DSS is part of a system for drop foot treatment that comprises a custom-designed multi-pad electrode, an electrical stimulator, and an inertial measurement unit. METHODS The system was tested in ten stroke survivors (3-96 months post stroke) with foot drop over 20 daily sessions. The DSS output suggested stimulation pads and parameters based on muscle twitch responses to short stimulus trains. The DSS ranked combinations of pads and current amplitudes based on a novel measurement of the quality of the induced movement and classified them based on the movement direction (dorsiflexion, plantar flexion, eversion and inversion) of the paretic foot. The efficacy of the DSS in providing satisfactory pad-current amplitude choices for shaping the stimulation electrode was evaluated by trained clinicians. The range of paretic foot motion was used as a quality indicator for the chosen patterns. RESULTS The results suggest that the DSS output was highly effective in creating optimized FES patterns. The position and number of pads included showed pronounced inter-patient and inter-session variability; however, zones for inducing dorsiflexion and plantar flexion within the multi-pad electrode were clearly separated. The range of motion achieved with FES was significantly greater than the corresponding active range of motion (p < 0.05) during the first three weeks of therapy. CONCLUSIONS The proposed DSS in combination with a custom multi-pad electrode design covering the branches of peroneal and tibial nerves proved to be an effective tool for producing both the dorsiflexion and plantar flexion of a paretic foot. The results support the use of multi-pad electrode technology in combination with automatic electrode shaping algorithms for the rehabilitation of foot drop. TRIAL REGISTRATION This study was registered at the Current Controlled Trials website with ClinicalTrials.gov ID NCT02729636 on March 29, 2016.
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Affiliation(s)
- Jovana Malešević
- Tecnalia Serbia Ltd., Belgrade, Serbia. .,University of Belgrade, Belgrade, Serbia.
| | - Suzana Dedijer Dujović
- University of Belgrade, Belgrade, Serbia.,Clinic for Rehabilitation "Dr Miroslav Zotović", Belgrade, Serbia
| | - Andrej M Savić
- Tecnalia Serbia Ltd., Belgrade, Serbia.,University of Belgrade, School of Electrical Engineering, Belgrade, Serbia
| | - Ljubica Konstantinović
- Clinic for Rehabilitation "Dr Miroslav Zotović", Belgrade, Serbia.,University of Belgrade, Faculty of Medicine, Belgrade, Serbia
| | - Aleksandra Vidaković
- Clinic for Rehabilitation "Dr Miroslav Zotović", Belgrade, Serbia.,University of Belgrade, Faculty of Medicine, Belgrade, Serbia
| | - Goran Bijelić
- Tecnalia Research & Innovation - Health Division, Donostia-San Sebastián, Spain
| | - Nebojša Malešević
- University of Belgrade, School of Electrical Engineering, Belgrade, Serbia.,Department of Biomedical Engineering, Lund University, Lund, Sweden
| | - Thierry Keller
- Tecnalia Research & Innovation - Health Division, Donostia-San Sebastián, Spain
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Savic A, Malešević N, Popovic M. 11. Motor imagery based BCI for control of FES. Clin Neurophysiol 2013. [DOI: 10.1016/j.clinph.2012.12.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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