Jaime Ibáñez

Multimodal BCI-mediated FES suppression of pathological tremor

Tremor constitutes the most common movement disorder; in fact 14.5% of population between 50 to 89 years old suffers from it. Moreover, 65% of patients with upper limb tremor report disability when performing their activities of daily living (ADL). Unfortunately, 25% of patients do not respond to drugs or neurosurgery. In this regard, TREMOR project proposes functional compensation of upper limb tremors with a soft wearable robot that applies biomechanical loads through functional electrical stimulation (FES) of muscles. This wearable robot is driven by a Brain Neural Computer Interface (BNCI). This paper presents a multimodal BCI to assess generation, transmission and execution of both volitional and tremorous movements based on electroencephalography (EEG), electromyography (EMG) and inertial sensors (IMUs). These signals are combined to obtain: 1) the intention to perform a voluntary movement from cortical activity (EEG), 2) tremor onset, and an estimation of tremor frequency from muscle activation (EMG), and 3) instantaneous tremor amplitude and frequency from kinematic measurements (IMUs). Integration of this information will provide control signals to drive the FES-based wearable robot.

An EEG-based design for the online detection of movement intention

The development of EEG-based wearable technologies for real-life environments has experienced an increasing interest over the last years. During activities of daily living, these systems need to be able to distinguish predefined mental states from the ongoing EEG signal, and these states of interest can be given after long periods of inactivity. A detector of the intention to move that is conceived to be used in real-time is proposed and offline validated with an experimental protocol with long intervals of inactivity that are also used for the detectors validation.

Converging clinical and engineering research on neurorehabilitation

Converging clinical and engineering research on neurorehabilitation / , Converging clinical and engineering research on neurorehabilitation / , کتابخانه دیجیتال جندی شاپور اهواز

A soft wearable robot for tremor assessment and suppression

Tremor constitutes the most common motor disorder, and poses a functional problem to a large number of patients. Despite of the considerable experience in tremor management, current treatment based on drugs or surgery does not attain an effective attenuation in 25 % of patients, motivating the need for research in new therapeutic alternatives. In this context, this paper presents the concept design, development, and preliminary validation of a soft wearable robot for tremor assessment and suppression. The TREMOR neurorobot comprises a Brain Neural Computer Interface that monitors the whole neuromusculoskeletal system, aiming at characterizing both voluntary movement and tremor, and a Functional Electrical Stimulation system that compensates for tremulous movements without impeding the user perform functional tasks. First results demonstrate the performance of the TREMOR neurorobot as a novel means of assessing and attenuating pathological tremors.

Feedback error learning controller for functional electrical stimulation assistance in a hybrid robotic system for reaching rehabilitation

Hybrid robotic systems represent a novel research field, where functional electrical stimulation (FES) is combined with a robotic device for rehabilitation of motor impairment. Under this approach, the design of robust FES controllers still remains an open challenge. In this work, we aimed at developing a learning FES controller to assist in the performance of reaching movements in a simple hybrid robotic system setting. We implemented a Feedback Error Learning (FEL) control strategy consisting of a feedback PID controller and a feedforward controller based on a neural network. A passive exoskeleton complemented the FES controller by compensating the effects of gravity. We carried out experiments with healthy subjects to validate the performance of the system. Results show that the FEL control strategy is able to adjust the FES intensity to track the desired trajectory accurately without the need of a previous mathematical model.

An asynchronous BMI system for online single-trial detection of movement intention

This paper presents an approach for an asynchronous BMI proposed as a switching part of a tremor suppression system developed for real-time continuous conditions. The main purpose of this BMI-switch is to anticipate the execution of self-initiated movements performed after relatively long periods of inactivity. The performance indicators used for the detector validation are specially suited for the continuous characteristic of the paradigm used and it is demonstrated that our ERD-based bayesian classifier solution is a reliable option, detecting a high rate of positive cases and generating very few false positives during long intervals of inactivity. The subjects analyzed for our detector validation were patients with neurological tremor caused by different pathologies in order to assure the adaptability of our system.

Upper-limb muscular electrical stimulation driven by EEG-based detections of the intentions to move: a proposed intervention for patients with stroke

This study proposes an intervention for stroke patients in which electrical stimulation of muscles in the affected arm is supplied when movement intention is detected from the electroencephalographic signal. The detection relies on the combined analysis of two movement related cortical patterns: the event-related desynchronization and the bereitschaftspotential. Results with two healthy subjects and three chronic stroke patients show that reliable EEG-based estimations of the movement onsets can be generated (on average, 66.9 ± 26.4 % of the movements are detected with 0.42 ± 0.17 false activations per minute) which in turn give rise to electrical stimuli providing sensory feedback tightly associated to the movement planning (average detection latency of the onsets of the movements was 54.4 ± 287.9 ms).

Detection of the Onset of Voluntary Movements Based on the Combination of ERD and BP Cortical Patterns

The electroencephalographic activity allows the characterization of movement-related cortical processes. This information may lead to novel rehabilitation technologies with the patients’ cortical activity taking an active role during the intervention. For such applications, the reliability of the estimations based on the electroencephalographic activity is critical both in terms of specificity and temporal accuracy. In this study, a detector of the onset of voluntary upper-limb reaching movements based on cortical rhythms and slow cortical potentials is proposed. To that end, upper-limb movements and cortical activity were recorded while participants performed self-paced movements. A logistic regression combined the output of two classifiers: a) a naive Bayes trained to detect the event-related desynchronization at the movement onset, and b) a matched filter detecting the bereitschaftspotential. On average, 74.5±10.8 % of the movements were detected and 1.32 ± 0.87 false detections were generated per minute. The detections were performed with an average latency of -89.9 ± 349.2 ms with respect to the actual movements. Therefore, the combination of two different sources of information (event-related desynchronization and bereitschaftspotential) is proposed as a way to boost the performance of this kind of systems.

Single-Trial Detection of the Event-Related Desynchronization to Locate with Temporal Precision the Onset of Voluntary Movements in Stroke Patients

Stroke patients may present motor impairments that in many cases require an intensive rehabilitation process with experts helping the patient to recover the functionality of the affected limb. A target during this rehabilitation process is to induce neural plasticity in brain regions associated with the motor control of the affected limb. Electrical stimulation tightly synchronized with the intention to perform a movement has proven to be an effectiveway of enhancing cortical excitability in healthy subjects. The electroencephalogram can help to detect voluntary movements online.We propose here an Electroencephalographybased system aimed to detect the instants atwhich stroke patients attempt to start voluntary movements with the affected upperlimb. To accomplish this, the analysis of the cortical rhythms and their variations are used. In the preliminary results obtained with 3 chronic stroke patients, 63±14% of the movements were detected with a temporal precision in the detections of the onsets of the movements of -126±313 ms.

Combining a hybrid robotic system with a bain-machine interface for the rehabilitation of reaching movements: A case study with a stroke patient

Reaching and grasping are two of the most affected functions after stroke. Hybrid rehabilitation systems combining Functional Electrical Stimulation with Robotic devices have been proposed in the literature to improve rehabilitation outcomes. In this work, we present the combined use of a hybrid robotic system with an EEG-based Brain-Machine Interface to detect the users movement intentions to trigger the assistance. The platform has been tested in a single session with a stroke patient. The results show how the patient could successfully interact with the BMI and command the assistance of the hybrid system with low latencies. Also, the Feedback Error Learning controller implemented in this system could adjust the required FES intensity to perform the task.Reaching and grasping are two of the most affected functions after stroke. Hybrid rehabilitation systems combining Functional Electrical Stimulation with Robotic devices have been proposed in the literature to improve rehabilitation outcomes. In this work, we present the combined use of a hybrid robotic system with an EEG-based Brain-Machine Interface to detect the users movement intentions to trigger the assistance. The platform has been tested in a single session with a stroke patient. The results show how the patient could successfully interact with the BMI and command the assistance of the hybrid system with low latencies. Also, the Feedback Error Learning controller implemented in this system could adjust the required FES intensity to perform the task.