Browsing by Keyword "Neuroprostheses"
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Item Brain oscillatory signatures of motor tasks(2015-06-01) Ramos-Murguialday, Ander; Birbaumer, Niels; Medical TechnologiesNoninvasive brain-computer-interfaces (BCI) coupled with prosthetic devices were recently introduced in the rehabilitation of chronic stroke and other disorders of the motor system. These BCI systems and motor rehabilitation in general involve several motor tasks for training. This study investigates the neurophysiological bases of an EEG-oscillation- driven BCI combined with a neuroprosthetic device to define the specific oscillatory signature of the BCI task. Controlling movements of a hand robotic orthosis with motor imagery of the same movement generates sensorimotor rhythm oscillation changes and involves three elements of tasks also used in stroke motor rehabilitation: passive and active movement, motor imagery, and motor intention. We recorded EEG while nine healthy participants performed five different motor tasks consisting of closing and opening of the hand as follows: 1) motor imagery without any external feedback and without overt hand movement, 2) motor imagery that moves the orthosis proportional to the produced brain oscillation change with online proprioceptive and visual feedback of the hand moving through a neuroprosthetic device (BCI condition), 3) passive and 4) active movement of the hand with feedback (seeing and feeling the hand moving), and 5) rest. During the BCI condition, participants received contingent online feedback of the decrease of power of the sensorimotor rhythm, which induced orthosis movement and therefore proprioceptive and visual information from the moving hand. We analyzed brain activity during the five conditions using time-frequency domain bootstrap-based statistical comparisons and Morlet transforms. Activity during rest was used as a reference. Significant contralateral and ipsilateral event-related desynchronization of sensorimotor rhythm was present during all motor tasks, largest in contralateral-postcentral, medio-central, and ipsilateral- precentral areas identifying the ipsilateral precentral cortex as an integral part of motor regulation. Changes in task-specific frequency power compared with rest were similar between motor tasks, and only significant differences in the time course and some narrow specific frequency bands were observed between motor tasks. We identified EEG features representing active and passive proprioception (with and without muscle contraction) and active intention and passive involvement (with and without voluntary effort) differentiating brain oscillations during motor tasks that could substantially support the design of novel motor BCI-based rehabilitation therapies. The BCI task induced significantly different brain activity compared with the other motor tasks, indicating neural processes unique to the use of body actuators control in a BCI context.Item Greifen mit hilfe von neuroprothesen - Implantierbare und nicht-invasive systeme(2004-02) Mangold, Sabine; Keller, Thierry; Tecnalia Research & InnovationNeuroprostheses are used in patients suffering from tetraplegia and hemiplegia as grasping aids for mastering life tasks as well as in therapy for training voluntary movement. A variety of invasive and non-invasive systems have been developed. The selection of a suitable system for an individual or for a therapeutic practice can be made based on the description of the technical properties of the various neuroprostheses available.Item On the design of EEG-based movement decoders for completely paralyzed stroke patients(2018-11-20) Spüler, Martin; López-Larraz, Eduardo; Ramos-Murguialday, Ander; Tecnalia Research & Innovation; Medical TechnologiesBackground: Brain machine interface (BMI) technology has demonstrated its efficacy for rehabilitation of paralyzed chronic stroke patients. The critical component in BMI-training consists of the associative connection (contingency) between the intention and the feedback provided. However, the relationship between the BMI design and its performance in stroke patients is still an open question. Methods: In this study we compare different methodologies to design a BMI for rehabilitation and evaluate their effects on movement intention decoding performance. We analyze the data of 37 chronic stroke patients who underwent 4 weeks of BMI intervention with different types of association between their brain activity and the proprioceptive feedback. We simulate the pseudo-online performance that a BMI would have under different conditions, varying: (1) the cortical source of activity (i.e., ipsilesional, contralesional, bihemispheric), (2) the type of spatial filter applied, (3) the EEG frequency band, (4) the type of classifier; and also evaluated the use of residual EMG activity to decode the movement intentions. Results: We observed a significant influence of the different BMI designs on the obtained performances. Our results revealed that using bihemispheric beta activity with a common average reference and an adaptive support vector machine led to the best classification results. Furthermore, the decoding results based on brain activity were significantly higher than those based on muscle activity. Conclusions: This paper underscores the relevance of the different parameters used to decode movement, using EEG in severely paralyzed stroke patients. We demonstrated significant differences in performance for the different designs, which supports further research that should elucidate if those approaches leading to higher accuracies also induce higher motor recovery in paralyzed stroke patients.Item Proprioceptive feedback in BCI(2009) Ramos, A.; Halder, S.; Birbaumer, N.; Medical TechnologiesMotor imagery based brain computer interface (BCI) technology can be used in motor neurorehabilitation. The use of a BCI as a neuroprosthetic for paralyzed limb assistance implies afferent information flow caused by the feedback. It is an open question whether the proprioceptive feedback causes a bias in the modulation of a motor imagery based BCI control signal. We used a BCI coupled with a robotic orthosis fixed to the subjects hand for flexing or extending the subjects fingers. We studied the proprioceptive feedback neurocorrelates and the performance of 2 subjects by compairing their accuracy using a BCI platform in 2 different tasks; motor imagery task without feedback and motor imagery task with fake, proprioceptive feedback. The proprioceptive feedback increased the performance considerably for both subjects. There is a clear desynchronization potentiation of the mu and beta rhythms while the subjects hand was being moved by the orthosis. These findings could be very relevant for the motor neurorehabilitation field.Item Reinforcement Learning for Hand Grasp with Surface Multi-field Neuroprostheses(Springer International Publishing, 2017) Imatz-Ojanguren, Eukene; Irigoyen, Eloy; Keller, Thierry; Lopez-Guede, Jose Manuel; Herrero, Alvaro; Quintian, Hector; Grana, Manuel; Etxaniz, Oier; Corchado, Emilio; Tecnalia Research & InnovationHand grasp is a complex system that plays an important role in the activities of daily living. Upper-limb neuroprostheses aim at restor- ing lost reaching and grasping functions on people su ering from neural disorders. However, the dimensionality and complexity of the upper-limb makes the neuroprostheses modeling and control challenging. In this work we present preliminary results for checking the feasibility of using a re- inforcement learning (RL) approach for achieving grasp functions with a surface multi- eld neuroprosthesis for grasping. Grasps from 20 healthy subjects were recorded to build a reference for the RL system and then two di erent award strategies were tested on simulations based on neuro- fuzzy models of hemiplegic patients. These rst results suggest that RL might be a possible solution for obtaining grasp function by means of multi- eld neuroprostheses in the near future.