Restoring the sense of touch with a prosthetic hand through a brain interface.

Proc Natl Acad Sci U S A

Committee on Computational Neuroscience and Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL 60637.

Published: November 2013

Our ability to manipulate objects dexterously relies fundamentally on sensory signals originating from the hand. To restore motor function with upper-limb neuroprostheses requires that somatosensory feedback be provided to the tetraplegic patient or amputee. Given the complexity of state-of-the-art prosthetic limbs and, thus, the huge state space they can traverse, it is desirable to minimize the need for the patient to learn associations between events impinging on the limb and arbitrary sensations. Accordingly, we have developed approaches to intuitively convey sensory information that is critical for object manipulation--information about contact location, pressure, and timing--through intracortical microstimulation of primary somatosensory cortex. In experiments with nonhuman primates, we show that we can elicit percepts that are projected to a localized patch of skin and that track the pressure exerted on the skin. In a real-time application, we demonstrate that animals can perform a tactile discrimination task equally well whether mechanical stimuli are delivered to their native fingers or to a prosthetic one. Finally, we propose that the timing of contact events can be signaled through phasic intracortical microstimulation at the onset and offset of object contact that mimics the ubiquitous on and off responses observed in primary somatosensory cortex to complement slowly varying pressure-related feedback. We anticipate that the proposed biomimetic feedback will considerably increase the dexterity and embodiment of upper-limb neuroprostheses and will constitute an important step in restoring touch to individuals who have lost it.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3831459PMC
http://dx.doi.org/10.1073/pnas.1221113110DOI Listing

Publication Analysis

Top Keywords

upper-limb neuroprostheses
8
intracortical microstimulation
8
primary somatosensory
8
somatosensory cortex
8
restoring sense
4
sense touch
4
touch prosthetic
4
prosthetic hand
4
hand brain
4
brain interface
4

Similar Publications

Unraveling EEG correlates of unimanual finger movements: insights from non-repetitive flexion and extension tasks.

J Neuroeng Rehabil

December 2024

Laboratory for Neuro- & Psychophysiology, Department of Neurosciences, KU Leuven, Leuven, Belgium.

Background: The loss of finger control in individuals with neuromuscular disorders significantly impacts their quality of life. Electroencephalography (EEG)-based brain-computer interfaces that actuate neuroprostheses directly via decoded motor intentions can help restore lost finger mobility. However, the extent to which finger movements exhibit distinct and decodable EEG correlates remains unresolved.

View Article and Find Full Text PDF

Accurate neural control of a hand prosthesis by posture-related activity in the primate grasping circuit.

Neuron

December 2024

Neurobiology Laboratory, Deutsches Primatenzentrum GmbH, Göttingen 37077, Germany; Faculty of Biology and Psychology, University of Göttingen, Göttingen 37073, Germany. Electronic address:

Brain-computer interfaces (BCIs) have the potential to restore hand movement for people with paralysis, but current devices still lack the fine control required to interact with objects of daily living. Following our understanding of cortical activity during arm reaches, hand BCI studies have focused primarily on velocity control. However, mounting evidence suggests that posture, and not velocity, dominates in hand-related areas.

View Article and Find Full Text PDF

Effects of end-effector robotic arm reach training with functional electrical stimulation for chronic stroke survivors.

Top Stroke Rehabil

October 2024

Department of Rehabilitative & Assistive Technology, National Rehabilitation Research Institute, National Rehabilitation Center, Seoul, Republic of Korea.

Background: Upper-extremity dysfunction significantly affects dependence in the daily lives of stroke survivors, limiting their participation in the social environment and reducing their quality of life.

Objectives: This study aimed to investigate the effect of end-effector robotic arm reach training (RAT) with functional electrical stimulation (FES) on upper-limb motor recovery in chronic stroke survivors.

Methods: In this single-blinded randomized controlled trial, 28 chronic stroke survivors were randomized to receive RAT-with-FES and RAT-without-FES for 40 min/day, three times per week over a 4-week period, and the data of 26 participants were used in the final analysis.

View Article and Find Full Text PDF

The neurophysiology of sensorimotor prosthetic control.

BMC Biomed Eng

October 2024

Department of Biomedical, Industrial and Human Factors Engineering, College of Engineering and Computer Science, Wright State University, Dayton, OH, USA.

Movement is a central behavior of daily living; thus lost or compromised movement due to disease, injury, or amputation causes enormous loss of productivity and quality of life. While prosthetics have evolved enormously over the years, restoring natural sensorimotor (SM) control via a prosthesis is a difficult problem which neuroengineering has yet to solve. With a focus on upper limb prosthetics, this perspective article discusses the neurophysiology of motor control under healthy conditions and after amputation, the development of upper limb prostheses from early generations to current state-of-the art sensorimotor neuroprostheses, and how postinjury changes could complicate prosthetic control.

View Article and Find Full Text PDF

Myoelectric indices forecasting is important for muscle fatigue monitoring in wearable technologies, adaptive control of assistive devices like exoskeletons and prostheses, functional electrical stimulation (FES)-based Neuroprostheses, and more. Non-stationary temporal development of these indices in dynamic contractions makes forecasting difficult. This study aims at incorporating transfer learning into a deep learning model, Myoelectric Fatigue Forecasting Network (MEFFNet), to forecast myoelectric indices of fatigue (both time and frequency domain) obtained during voluntary and FES-induced dynamic contractions in healthy and post-stroke subjects respectively.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!