Interpreting spatial and temporal neural activity through a recurrent neural network brain-machine interface.

• We propose the use of optimized brain-machine interface (BMI) models for interpreting the spatial and temporal neural activity generated in motor tasks. In this study, a nonlinear dynamical neural network is trained to predict the hand position of primates from neural recordings in a reaching task paradigm. We first develop a method to reveal the role attributed by the model to the sampled motor, premotor, and parietal cortices in generating hand movements. Next, using the trained model weights, we derive a temporal sensitivity measure to asses how the model utilized the sampled cortices and neurons in real-time during BMI testing.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Medicinska och farmaceutiska grundvetenskaper -- Fysiologi (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Basic Medicine -- Physiology (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)

NATURVETENSKAP  -- Biologi -- Bioinformatik och systembiologi (hsv//swe)

NATURAL SCIENCES  -- Biological Sciences -- Bioinformatics and Systems Biology (hsv//eng)

Nyckelord

Algorithms

Animals

Aotidae

Artificial Intelligence

Brain Mapping

methods

Cerebral Cortex

physiology

Diagnosis

Computer-Assisted

methods

Electroencephalography

methods

Evoked Potentials

Motor

physiology

Feedback

Hand

innervation

physiology

Movement

physiology

Neural Networks (Computer)

Neurons

physiology

Pattern Recognition

Automated

methods

Time Factors

User-Computer Interface

Publikations- och innehållstyp

ref (ämneskategori)

art (ämneskategori)