Browsing by Keyword "neurorehabilitation"
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Item BEAGLE - A Kinematic Sensory System for Objective Hand Function Assessment in Technology-Mediated Rehabilitation(2021) Malesevic, Jovana; Kostic, Milos; Kojic, Vladimir; Dordevic, Olivera; Konstantinovic, Ljubica; Keller, Thierry; Strbac, Matija; SG; Tecnalia Research & InnovationWe present a hand functions assessment system (BEAGLE) for kinematic tracking of hand and finger movements, envisioned as a technology-mediated rehabilitation tool. The system is custom-designed for fast and easy placement on an impaired hand (spastic or flaccid), featuring inertial sensors integrated into simple finger caps and a hand strap. An algorithm for a range of motion (ROM) estimation was implemented to provide an objective assessment of hand functions. The efficacy and feasibility of the BEAGLE system were examined in a pilot clinical study performed with ten stroke survivors in the subacute phase. Participants received therapy within two consecutive intensity-matched rehabilitation cycles. The first consisted of conventional therapy, while the second involved a combination of conventional therapy and advanced functional electrical stimulation. Assessments were performed before and after each phase. These included BEAGLE estimates of active voluntary ROM for wrist and various digits, as well as two referent clinical measures for hand functions assessment, Fugl-Meyer and Action Research Arm Test. The results indicate that the ROM assessments can detect change with sensitivity comparable to the standardized clinical scales. Statistically significant changes between the beginning and the end of the second cycle existed in all observed measures, whereas none of these measurements showed a statistically significant improvement in the first therapy cycle. The noted usability metrics indicate that the BEAGLE could be integrated into the rehabilitation workflow in a clinical environment.Item Brain-Machine Interface in Chronic Stroke: Randomized Trial Long-Term Follow-up(2019-03) Ramos-Murguialday, Ander; Curado, Marco R.; Broetz, Doris; Yilmaz, Özge; Brasil, Fabricio L.; Liberati, Giulia; Garcia-Cossio, Eliana; Cho, Woosang; Caria, Andrea; Cohen, Leonardo G.; Birbaumer, Niels; Medical TechnologiesBackground. Brain-machine interfaces (BMIs) have been recently proposed as a new tool to induce functional recovery in stroke patients. Objective. Here we evaluated long-term effects of BMI training and physiotherapy in motor function of severely paralyzed chronic stroke patients 6 months after intervention. Methods. A total of 30 chronic stroke patients with severe hand paresis from our previous study were invited, and 28 underwent follow-up assessments. BMI training included voluntary desynchronization of ipsilesional EEG-sensorimotor rhythms triggering paretic upper-limb movements via robotic orthoses (experimental group, n = 16) or random orthoses movements (sham group, n = 12). Both groups received identical physiotherapy following BMI sessions and a home-based training program after intervention. Upper-limb motor assessment scores, electromyography (EMG), and functional magnetic resonance imaging (fMRI) were assessed before (Pre), immediately after (Post1), and 6 months after intervention (Post2). Results. The experimental group presented with upper-limb Fugl-Meyer assessment (cFMA) scores significantly higher in Post2 (13.44 ± 1.96) as compared with the Pre session (11.16 ± 1.73; P =.015) and no significant changes between Post1 and Post2 sessions. The Sham group showed no significant changes on cFMA scores. Ashworth scores and EMG activity in both groups increased from Post1 to Post2. Moreover, fMRI-BOLD laterality index showed no significant difference from Pre or Post1 to Post2 sessions. Conclusions. BMI-based rehabilitation promotes long-lasting improvements in motor function of chronic stroke patients with severe paresis and represents a promising strategy in severe stroke neurorehabilitation.Item Cortical processing during robot and functional electrical stimulation(2023) Cho, Woosang; Vidaurre, Carmen; An, Jinung; Birbaumer, Niels; Ramos-Murguialday, Ander; Tecnalia Research & Innovation; Medical TechnologiesIntroduction: Like alpha rhythm, the somatosensory mu rhythm is suppressed in the presence of somatosensory inputs by implying cortical excitation. Sensorimotor rhythm (SMR) can be classified into two oscillatory frequency components: mu rhythm (8–13 Hz) and beta rhythm (14–25 Hz). The suppressed/enhanced SMR is a neural correlate of cortical activation related to efferent and afferent movement information. Therefore, it would be necessary to understand cortical information processing in diverse movement situations for clinical applications. Methods: In this work, the EEG of 10 healthy volunteers was recorded while fingers were moved passively under different kinetic and kinematic conditions for proprioceptive stimulation. For the kinetics aspect, afferent brain activity (no simultaneous volition) was compared under two conditions of finger extension: (1) generated by an orthosis and (2) generated by the orthosis simultaneously combined and assisted with functional electrical stimulation (FES) applied at the forearm muscles related to finger extension. For the kinematic aspect, the finger extension was divided into two phases: (1) dynamic extension and (2) static extension (holding the extended position). Results: In the kinematic aspect, both mu and beta rhythms were more suppressed during a dynamic than a static condition. However, only the mu rhythm showed a significant difference between kinetic conditions (with and without FES) affected by attention to proprioception after transitioning from dynamic to static state, but the beta rhythm was not. Discussion: Our results indicate that mu rhythm was influenced considerably by muscle kinetics during finger movement produced by external devices, which has relevant implications for the design of neuromodulation and neurorehabilitation interventions.Item ERD-based online brain-machine interfaces (BMI) in the context of neurorehabilitation: Optimizing BMI learning and performance(2011-10) Soekadar, Surjo R.; Witkowski, Matthias; Mellinger, Jürgen; Ramos, Ander; Birbaumer, Niels; Cohen, Leonardo G.; Medical TechnologiesEvent-related desynchronization (ERD) of sensori-motor rhythms (SMR) can be used for online brain-machine interface (BMI) control, but yields challenges related to the stability of ERD and feedback strategy to optimize BMI learning. Here, we compared two approaches to this challenge in 20 right-handed healthy subjects (HS, five sessions each, S1-S5) and four stroke patients (SP, 15 sessions each, S1-S15). ERD was recorded from a 275-sensor MEG system. During daily training, motor imagery-induced ERD led to visual and proprioceptive feedback delivered through an orthotic device attached to the subjects' hand and fingers. Group A trained with a heterogeneous reference value (RV) for ERD detection with binary feedback and Group B with a homogenous RV and graded feedback (10 HS and 2 SP in each group). HS in Group B showed better BMI performance than Group A (p < 0.001) and improved BMI control from S1 to S5 ( p=0.012) while Group A did not. In spite of the small n, SP in Group B showed a trend for a higher BMI performance (p = 0.06) and learning was significantly better (p < 0.05). Using a homogeneous RV and graded feedback led to improved modulation of ipsilesional activity resulting in superior BMI learning relative to use of a heterogeneous RV and binary feedback.Item Is EMG a Viable Alternative to BCI for Detecting Movement Intention in Severe Stroke?(2018-12) Balasubramanian, Sivakumar; Garcia-Cossio, Eliana; Birbaumer, Niels; Burdet, Etienne; Ramos-Murguialday, Ander; Medical TechnologiesObjective: In light of the shortcomings of current restorative brain-computer interfaces (BCI), this study investigated the possibility of using EMG to detect hand/wrist extension movement intention to trigger robot-assisted training in individuals without residual movements. Methods: We compared movement intention detection using an EMG detector with a sensorimotor rhythm based EEG-BCI using only ipsilesional activity. This was carried out on data of 30 severely affected chronic stroke patients from a randomized control trial using an EEG-BCI for robot-assisted training. Results: The results indicate the feasibility of using EMG to detect movement intention in this severely handicapped population; probability of detecting EMG when patients attempted to move was higher (p < 0.001) than at rest. Interestingly, 22 out of 30 (or 73%) patients had sufficiently strong EMG in their finger/wrist extensors. Furthermore, in patients with detectable EMG, there was poor agreement between the EEG and EMG intent detectors, which indicates that these modalities may detect different processes. Conclusion : A substantial segment of severely affected stroke patients may benefit from EMG-based assisted therapy. When compared to EEG, a surface EMG interface requires less preparation time, which is easier to don/doff, and is more compact in size. Significance: This study shows that a large proportion of severely affected stroke patients have residual EMG, which yields a direct and practical way to trigger robot-assisted training.Item Platform for integration of internet games for the training of upper extremities after stroke(Institute of Electrical and Electronics Engineers Inc., 2014-01-15) Okošanović, Milena T.; Kljajić, Jelena; Kostić, Miloš D.; Reljin, Branimir; Stankovic, Srdan; SGThe gaming system used during the process of neurorehabilitation is developed. It is composed of: acquisition component - Wacom Intuos4 XL tablet, and a computer which processes data and uses them as control signals in Internet game. Monitoring of either personal progress or success of other users using the presented platform is enabled by special evaluation system which provides comparison of the results. Quality of movement is assessed by the method which employs method of Probability Tubes. The result of this assessment is reflected in the form of a control signal adapted to playing online games Darts. The system supports point to point movements in horizontal plane.