المؤلفون: Saruco, Elodie¹ (AUTHOR), Saimpont, Arnaud¹ (AUTHOR), Di Rienzo, Franck¹ (AUTHOR), De Witte, Benjamin¹ (AUTHOR), Laroyenne, Isabelle² (AUTHOR), Matéo, Fanny² (AUTHOR), Lapenderie, Marion² (AUTHOR), Solard, Sarah Goutte² (AUTHOR), Perretant, Isabelle² (AUTHOR), Frenot, Charlotte² (AUTHOR), Jackson, Philip L.³ (AUTHOR), Guillot, Aymeric¹ (AUTHOR) aymeric.guillot@univ-lyon1.fr

المصدر: Journal of NeuroEngineering & Rehabilitation (JNER). 4/15/2024, Vol. 21 Issue 1, p1-12. 12p.

مصطلحات موضوعية: *MOTOR imagery (Cognition), *PROSTHETICS, *AMPUTATION, *CENTRAL nervous system, *REHABILITATION centers, *PHYSICAL therapy

مستخلص: Background: The therapeutic benefits of motor imagery (MI) are now well-established in different populations of persons suffering from central nervous system impairments. However, research on similar efficacy of MI interventions after amputation remains scarce, and experimental studies were primarily designed to explore the effects of MI after upper-limb amputations. Objectives: The present comparative study therefore aimed to assess the effects of MI on locomotion recovery following unilateral lower-limb amputation. Methods: Nineteen participants were assigned either to a MI group (n = 9) or a control group (n = 10). In addition to the course of physical therapy, they respectively performed 10 min per day of locomotor MI training or neutral cognitive exercises, five days per week. Participants' locomotion functions were assessed through two functional tasks: 10 m walking and the Timed Up and Go Test. Force of the amputated limb and functional level score reflecting the required assistance for walking were also measured. Evaluations were scheduled at the arrival at the rehabilitation center (right after amputation), after prosthesis fitting (three weeks later), and at the end of the rehabilitation program. A retention test was also programed after 6 weeks. Results: While there was no additional effect of MI on pain management, data revealed an early positive impact of MI for the 10 m walking task during the pre-prosthetic phase, and greater performance during the Timed Up and Go Test during the prosthetic phase. Also, a lower proportion of participants still needed a walking aid after MI training. Finally, the force of the amputated limb was greater at the end of rehabilitation for the MI group. Conclusion: Taken together, these data support the integration of MI within the course of physical therapy in persons suffering from lower-limb amputations. [ABSTRACT FROM AUTHOR]

المؤلفون: Tanamachi, Kenya^1,2 (AUTHOR), Kuwahara, Wataru^1,2 (AUTHOR), Okawada, Megumi^1,2 (AUTHOR), Sasaki, Shun² (AUTHOR), Kaneko, Fuminari^1,2 (AUTHOR) f-kaneko@tmu.ac.jp

المصدر: Journal of NeuroEngineering & Rehabilitation (JNER). 11/18/2023, Vol. 20 Issue 1, p1-14. 14p.

مصطلحات موضوعية: *FUNCTIONAL magnetic resonance imaging, *MOTOR imagery (Cognition), *FUNCTIONAL connectivity, *LARGE-scale brain networks, *STROKE patients, *PERCEPTUAL illusions, *TRANSCRANIAL magnetic stimulation, *TRANSCRANIAL direct current stimulation

مستخلص: Background: In clinical practice, motor imagery has been proposed as a treatment modality for stroke owing to its feasibility in patients with severe motor impairment. Motor imagery-based interventions can be categorized as open- or closed-loop. Closed-loop intervention is based on voluntary motor imagery and induced peripheral sensory afferent (e.g., Brain Computer Interface (BCI)-based interventions). Meanwhile, open-loop interventions include methods without voluntary motor imagery or sensory afferent. Resting-state functional connectivity (rs-FC) is defined as a significant temporal correlated signal among functionally related brain regions without any stimulus. rs-FC is a powerful tool for exploring the baseline characteristics of brain connectivity. Previous studies reported changes in rs-FC after motor imagery interventions. Systematic reviews also reported the effects of motor imagery-based interventions at the behavioral level. This study aimed to review and describe the relationship between the improvement in motor function and changes in rs-FC after motor imagery in patients with stroke. Review process: The literature review was based on Arksey and O'Malley's framework. PubMed, Ovid MEDLINE, Cochrane Central Register of Controlled Trials, and Web of Science were searched up to September 30, 2023. The included studies covered the following topics: illusion without voluntary action, motor imagery, action imitation, and BCI-based interventions. The correlation between rs-FC and motor function before and after the intervention was analyzed. After screening by two independent researchers, 13 studies on BCI-based intervention, motor imagery intervention, and kinesthetic illusion induced by visual stimulation therapy were included. Conclusion: All studies relating to motor imagery in this review reported improvement in motor function post-intervention. Furthermore, all those studies demonstrated a significant relationship between the change in motor function and rs-FC (e.g., sensorimotor network and parietal cortex). [ABSTRACT FROM AUTHOR]

المؤلفون: Ferrero, Laura, Soriano-Segura, Paula, Navarro, Jacobo, Jones, Oscar, Ortiz, Mario, Iáñez, Eduardo, Azorín, José M., Contreras-Vidal, José L.

المصدر: Journal of NeuroEngineering & Rehabilitation (JNER); 4/5/2024, Vol. 21 Issue 1, p1-14, 14p

مصطلحات موضوعية: DEEP learning, BRAIN-computer interfaces, ROBOTIC exoskeletons, MACHINE learning, MOTOR imagery (Cognition), FEATURE extraction

مستخلص: Background: This research focused on the development of a motor imagery (MI) based brain–machine interface (BMI) using deep learning algorithms to control a lower-limb robotic exoskeleton. The study aimed to overcome the limitations of traditional BMI approaches by leveraging the advantages of deep learning, such as automated feature extraction and transfer learning. The experimental protocol to evaluate the BMI was designed as asynchronous, allowing subjects to perform mental tasks at their own will. Methods: A total of five healthy able-bodied subjects were enrolled in this study to participate in a series of experimental sessions. The brain signals from two of these sessions were used to develop a generic deep learning model through transfer learning. Subsequently, this model was fine-tuned during the remaining sessions and subjected to evaluation. Three distinct deep learning approaches were compared: one that did not undergo fine-tuning, another that fine-tuned all layers of the model, and a third one that fine-tuned only the last three layers. The evaluation phase involved the exclusive closed-loop control of the exoskeleton device by the participants' neural activity using the second deep learning approach for the decoding. Results: The three deep learning approaches were assessed in comparison to an approach based on spatial features that was trained for each subject and experimental session, demonstrating their superior performance. Interestingly, the deep learning approach without fine-tuning achieved comparable performance to the features-based approach, indicating that a generic model trained on data from different individuals and previous sessions can yield similar efficacy. Among the three deep learning approaches compared, fine-tuning all layer weights demonstrated the highest performance. Conclusion: This research represents an initial stride toward future calibration-free methods. Despite the efforts to diminish calibration time by leveraging data from other subjects, complete elimination proved unattainable. The study's discoveries hold notable significance for advancing calibration-free approaches, offering the promise of minimizing the need for training trials. Furthermore, the experimental evaluation protocol employed in this study aimed to replicate real-life scenarios, granting participants a higher degree of autonomy in decision-making regarding actions such as walking or stopping gait. [ABSTRACT FROM AUTHOR]

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المؤلفون: Brunner, Iris, Lundquist, Camilla Biering, Pedersen, Asger Roer, Spaich, Erika G., Dosen, Strahinja, Savic, Andrej

المصدر: Journal of NeuroEngineering & Rehabilitation (JNER); 1/20/2024, Vol. 21 Issue 1, p1-12, 12p

مصطلحات موضوعية: ELECTRIC stimulation, WRIST, MOTOR imagery (Cognition), COMPUTER interfaces, STROKE, RANDOMIZED controlled trials, RESISTANCE training

مستخلص: Background: Restorative Brain–Computer Interfaces (BCI) that combine motor imagery with visual feedback and functional electrical stimulation (FES) may offer much-needed treatment alternatives for patients with severely impaired upper limb (UL) function after a stroke. Objectives: This study aimed to examine if BCI-based training, combining motor imagery with FES targeting finger/wrist extensors, is more effective in improving severely impaired UL motor function than conventional therapy in the subacute phase after stroke, and if patients with preserved cortical-spinal tract (CST) integrity benefit more from BCI training. Methods: Forty patients with severe UL paresis (< 13 on Action Research Arm Test (ARAT) were randomized to either a 12-session BCI training as part of their rehabilitation or conventional UL rehabilitation. BCI sessions were conducted 3–4 times weekly for 3–4 weeks. At baseline, Transcranial Magnetic Stimulation (TMS) was performed to examine CST integrity. The main endpoint was the ARAT at 3 months post-stroke. A binominal logistic regression was conducted to examine the effect of treatment group and CST integrity on achieving meaningful improvement. In the BCI group, electroencephalographic (EEG) data were analyzed to investigate changes in event-related desynchronization (ERD) during the course of therapy. Results: Data from 35 patients (15 in the BCI group and 20 in the control group) were analyzed at 3-month follow-up. Few patients (10/35) improved above the minimally clinically important difference of 6 points on ARAT, 5/15 in the BCI group, 5/20 in control. An independent-samples Mann–Whitney U test revealed no differences between the two groups, p = 0.382. In the logistic regression only CST integrity was a significant predictor for improving UL motor function, p = 0.007. The EEG analysis showed significant changes in ERD of the affected hemisphere and its lateralization only during unaffected UL motor imagery at the end of the therapy. Conclusion: This is the first RCT examining BCI training in the subacute phase where only patients with severe UL paresis were included. Though more patients in the BCI group improved relative to the group size, the difference between the groups was not significant. In the present study, preserved CTS integrity was much more vital for UL improvement than which type of intervention the patients received. Larger studies including only patients with some preserved CST integrity should be attempted. [ABSTRACT FROM AUTHOR]

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المؤلفون: Won, Kyungho, Kim, Heegyu, Gwon, Daeun, Ahn, Minkyu, Nam, Chang S., Jun, Sung Chan

المصدر: Journal of NeuroEngineering & Rehabilitation (JNER); 5/4/2023, Vol. 20 Issue 1, p1-16, 16p

مصطلحات موضوعية: VIBROTACTILE stimulation, TRANSCRANIAL direct current stimulation, MOTOR imagery (Cognition), BRAIN-computer interfaces, LANDSAT satellites

مستخلص: Brain-computer interface (BCI) has helped people by allowing them to control a computer or machine through brain activity without actual body movement. Despite this advantage, BCI cannot be used widely because some people cannot achieve controllable performance. To solve this problem, researchers have proposed stimulation methods to modulate relevant brain activity to improve BCI performance. However, multiple studies have reported mixed results following stimulation, and the comparative study of different stimulation modalities has been overlooked. Accordingly, this study was designed to compare vibrotactile stimulation and transcranial direct current stimulation's (tDCS) effects on brain activity modulation and motor imagery BCI performance among inefficient BCI users. We recruited 44 subjects and divided them into sham, vibrotactile stimulation, and tDCS groups, and low performers were selected from each stimulation group. We found that the latter's BCI performance in the vibrotactile stimulation group increased significantly by 9.13% (p < 0.01), and while the tDCS group subjects' performance increased by 5.13%, it was not significant. In contrast, sham group subjects showed no increased performance. In addition to BCI performance, pre-stimulus alpha band power and the phase locking values (PLVs) averaged over sensory motor areas showed significant increases in low performers following stimulation in the vibrotactile stimulation and tDCS groups, while sham stimulation group subjects and high performers showed no significant stimulation effects across all groups. Our findings suggest that stimulation effects may differ depending upon BCI efficiency, and inefficient BCI users have greater plasticity than efficient BCI users. [ABSTRACT FROM AUTHOR]

: Copyright of Journal of NeuroEngineering & Rehabilitation (JNER) is the property of BioMed Central and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

المؤلفون: Habashi, Ahmed G., Azab, Ahmed M., Eldawlatly, Seif, Aly, Gamal M.

المصدر: Journal of NeuroEngineering & Rehabilitation (JNER); 4/11/2023, Vol. 20 Issue 1, p1-24, 24p

مصطلحات موضوعية: GENERATIVE adversarial networks, ELECTROENCEPHALOGRAPHY, DATA augmentation, MOTOR imagery (Cognition), EMOTION recognition, BRAIN-computer interfaces, WAKEFULNESS

مستخلص: Electroencephalogram (EEG) signals have been utilized in a variety of medical as well as engineering applications. However, one of the challenges associated with recording EEG data is the difficulty of recording large amounts of data. Consequently, data augmentation is a potential solution to overcome this challenge in which the objective is to increase the amount of data. Inspired by the success of Generative Adversarial Networks (GANs) in image processing applications, generating artificial EEG data from the limited recorded data using GANs has seen recent success. This article provides an overview of various techniques and approaches of GANs for augmenting EEG signals. We focus on the utility of GANs in different applications including Brain-Computer Interface (BCI) paradigms such as motor imagery and P300-based systems, in addition to emotion recognition, epileptic seizures detection and prediction, and various other applications. We address in this article how GANs have been used in each study, the impact of using GANs on the model performance, the limitations of each algorithm, and future possibilities for developing new algorithms. We emphasize the utility of GANs in augmenting the limited EEG data typically available in the studied applications. [ABSTRACT FROM AUTHOR]

: Copyright of Journal of NeuroEngineering & Rehabilitation (JNER) is the property of BioMed Central and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

المؤلفون: Davis, Kevin C., Meschede-Krasa, Benyamin, Cajigas, Iahn, Prins, Noeline W., Alver, Charles, Gallo, Sebastian, Bhatia, Shovan, Abel, John H., Naeem, Jasim A., Fisher, Letitia, Raza, Fouzia, Rifai, Wesley R., Morrison, Matthew, Ivan, Michael E., Brown, Emery N., Jagid, Jonathan R., Prasad, Abhishek

المصدر: Journal of NeuroEngineering & Rehabilitation (JNER); 6/3/2022, Vol. 19 Issue 1, p1-14, 14p

مصطلحات موضوعية: BRAIN-computer interfaces, SPINAL cord injuries, SENSORIMOTOR cortex, CERVICAL cord, ELECTRIC stimulation, USER interfaces, MOTOR imagery (Cognition), MOBILE apps

مستخلص: Objective: The objective of this study was to develop a portable and modular brain-computer interface (BCI) software platform independent of input and output devices. We implemented this platform in a case study of a subject with cervical spinal cord injury (C5 ASIA A).Background: BCIs can restore independence for individuals with paralysis by using brain signals to control prosthetics or trigger functional electrical stimulation. Though several studies have successfully implemented this technology in the laboratory and the home, portability, device configuration, and caregiver setup remain challenges that limit deployment to the home environment. Portability is essential for transitioning BCI from the laboratory to the home.Methods: The BCI platform implementation consisted of an Activa PC + S generator with two subdural four-contact electrodes implanted over the dominant left hand-arm region of the sensorimotor cortex, a minicomputer fixed to the back of the subject's wheelchair, a custom mobile phone application, and a mechanical glove as the end effector. To quantify the performance for this at-home implementation of the BCI, we quantified system setup time at home, chronic (14-month) decoding accuracy, hardware and software profiling, and Bluetooth communication latency between the App and the minicomputer. We created a dataset of motor-imagery labeled signals to train a binary motor imagery classifier on a remote computer for online, at-home use.Results: Average bluetooth data transmission delay between the minicomputer and mobile App was 23 ± 0.014 ms. The average setup time for the subject's caregiver was 5.6 ± 0.83 min. The average times to acquire and decode neural signals and to send those decoded signals to the end-effector were respectively 404.1 ms and 1.02 ms. The 14-month median accuracy of the trained motor imagery classifier was 87.5 ± 4.71% without retraining.Conclusions: The study presents the feasibility of an at-home BCI system that subjects can seamlessly operate using a friendly mobile user interface, which does not require daily calibration nor the presence of a technical person for at-home setup. The study also describes the portability of the BCI system and the ability to plug-and-play multiple end effectors, providing the end-user the flexibility to choose the end effector to accomplish specific motor tasks for daily needs. Trial registration ClinicalTrials.gov: NCT02564419. First posted on 9/30/2015. [ABSTRACT FROM AUTHOR]

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المؤلفون: Kun Wang^1,2, Zhongpeng Wang^1,2, Yi Guo^1,2, Feng He^1,2, Hongzhi Qi^1,2 qhz@tju.edu.cn, Minpeng Xu^1,2, Dong Ming^1,2 richardming@tju.edu.cn, Wang, Kun^1,2 (AUTHOR), Wang, Zhongpeng^1,2 (AUTHOR), Guo, Yi^1,2 (AUTHOR), He, Feng^1,2 (AUTHOR), Qi, Hongzhi^1,2 (AUTHOR), Xu, Minpeng^1,2 (AUTHOR), Ming, Dong^1,2 (AUTHOR)

المصدر: Journal of NeuroEngineering & Rehabilitation (JNER). 9/11/2017, Vol. 14 Issue 1, p1-10. 10p.

مصطلحات موضوعية: *BRAIN-computer interfaces, *MOTOR imagery (Cognition), *ELECTROENCEPHALOGRAPHY, *HAND, *MOVEMENT disorders, *MECHANICAL loads, *HAND physiology, *ALGORITHMS, *ELECTROMYOGRAPHY, *ENERGY metabolism, *EVOKED potentials (Electrophysiology), *IMAGINATION, *KINEMATICS, *MUSCLE contraction, *ONLINE information services, *PILOT projects, *BODY movement, *HUMAN research subjects

مستخلص: Background: Motor imagery (MI) induced EEG patterns are widely used as control signals for brain-computer interfaces (BCIs). Kinetic and kinematic factors have been proved to be able to change EEG patterns during motor execution and motor imagery. However, to our knowledge, there is still no literature reporting an effective online MI-BCI using kinetic factor regulated EEG oscillations. This study proposed a novel MI-BCI paradigm in which users can online output multiple commands by imagining clenching their right hand with different force loads.Methods: Eleven subjects participated in this study. During the experiment, they were asked to imagine clenching their right hands with two different force loads (30% maximum voluntary contraction (MVC) and 10% MVC). Multi-Common spatial patterns (Multi-CSPs) and support vector machines (SVMs) were used to build the classifier for recognizing three commands corresponding to high load MI, low load MI and relaxed status respectively. EMG were monitored to avoid voluntary muscle activities during the BCI operation. The event-related spectral perturbation (ERSP) method was used to analyse EEG variation during multiple load MI tasks.Results: All subjects were able to drive BCI systems using motor imagery of different force loads in online experiments. We achieved an average online accuracy of 70.9%, with the highest accuracy of 83.3%, which was much higher than the chance level (33%). The event-related desynchronization (ERD) phenomenon during high load tasks was significantly higher than it was during low load tasks both in terms of intensity at electrode positions C3 (p < 0.05) and spatial distribution.Conclusions: This paper demonstrated the feasibility of the proposed MI-BCI paradigm based on multi-force loads on the same limb through online studies. This paradigm could not only enlarge the command set of MI-BCI, but also provide a promising approach to rehabilitate patients with motor disabilities. [ABSTRACT FROM AUTHOR]

المؤلفون: Andrade, João¹, Cecílio, José², Simões1,3, Marco, Sales, Francisco³, Castelo-Branco, Miguel^1,4 mcbranco@fmed.uc.pt, Simões, Marco^2,5 (AUTHOR)

المصدر: Journal of NeuroEngineering & Rehabilitation (JNER). 6/26/2017, Vol. 14 Issue 1, p1-13. 13p.

مصطلحات موضوعية: *MOTOR imagery (Cognition), *MOTOR ability, *VISUALIZATION, *AUTISM, *MECHANICAL ability

مستخلص: Background: We aimed to investigate the separability of the neural correlates of 2 types of motor imagery, self and third person (actions owned by the participant himself vs. another individual). If possible this would allow for the development of BCI interfaces to train disorders of action and intention understanding beyond simple imitation, such as autism.Methods: We used EEG recordings from 20 healthy participants, as well as electrocorticography (ECoG) in one, based on a virtual reality setup. To test feasibility of discrimination between each type of imagery at the single trial level, time-frequency and source analysis were performed and further assessed by data-driven statistical classification using Support Vector Machines.Results: The main observed differences between self-other imagery conditions in topographic maps were found in Frontal and Parieto-Occipital regions, in agreement with the presence of 2 independent non μ related contributions in the low alpha frequency range. ECOG corroborated such separability. Source analysis also showed differences near the temporo-parietal junction and single-trial average classification accuracy between both types of motor imagery was 67 ± 1%, and raised above 70% when 3 trials were used. The single-trial classification accuracy was significantly above chance level for all the participants of this study (p < 0.02).Conclusions: The observed pattern of results show that Self and Third Person MI use distinct electrophysiological mechanisms detectable at the scalp (and ECOG) at the single trial level, with separable levels of involvement of the mirror neuron system in different regions. These observations provide a promising step to develop new BCI training/rehabilitation paradigms for patients with neurodevelopmental disorders of action understanding beyond simple imitation, such as autism, who would benefit from training and anticipation of the perceived intention of others as opposed to own intentions in social contexts. [ABSTRACT FROM AUTHOR]

المؤلفون: Bodranghien, Florian¹, Manto, Mario^1,2,3 mmanto@ulb.ac.be, Lebon, Florent^4,5

المصدر: Journal of NeuroEngineering & Rehabilitation (JNER). 6/1/2016, Vol. 13 Issue 1, p1-3. 3p.

مصطلحات موضوعية: *DIRECT currents, *MOTOR imagery (Cognition), *CENTRAL nervous system, *NEUROREHABILITATION, *KINESTHETIC method (Education), *FRONTAL lobe, *EVOKED potentials (Electrophysiology), *ILLUSION (Philosophy), *VISUALIZATION, *TRANSCRANIAL direct current stimulation, *PHYSIOLOGY

مستخلص: Background: Transcranial direct current stimulation is a safe technique which is now part of the therapeutic armamentarium for the neuromodulation of motor functions and cognitive operations. It is currently considered that tDCS is an intervention that might promote functional recovery after a lesion in the central nervous system, thus reducing long-term disability and associated socio-economic burden.Discussion: A recent study shows that kinesthetic illusion and motor imagery prolong the effects of tDCS on corticospinal excitability, overcoming one of the limitations of this intervention.Conclusion: Because changes in excitability anticipate changes in structural plasticity in the CNS, this interesting multi-modal approach might very soon find applications in neurorehabilitation. [ABSTRACT FROM AUTHOR]