Decoding native cortical representations for flexion and extension at upper limb joints using electrocorticography

Tessy M. Thomas, Daniel N. Candrea, Matthew S. Fifer, David P. McMullen, William S Anderson, Nitish V Thakor, Nathan E Crone

Research output: Contribution to journalArticle

Abstract

Brain-machine interface (BMI) researchers have traditionally focused on modeling endpoint reaching tasks to provide control of neurally-driven prosthetic arms. Most previous research has focused on achieving endpoint control through a Cartesian-coordinate-centered approach. However, a joint-centered approach could potentially be used to intuitively control a wide range of limb movements. We systematically investigated the feasibility of discriminating between flexion and extension of different upper limb joints using electrocorticography (ECoG) recordings from sensorimotor cortex. Four subjects implanted with macro- ECoG (10 mm spacing), HD-ECoG (5 mm spacing), and/or micro-ECoG arrays (0.9 mm spacing, 4x4 mm coverage), performed randomly cued flexions or extensions of the fingers, wrist, or elbow contralateral to the implanted hemisphere. We trained a linear model to classify six movements using averaged high-gamma power (70-110 Hz) modulations at different latencies with respect to movement onset, and within a time interval restricted to flexion or extension at each joint. Offline decoding models for each subject classified these movements with accuracies of 62-83%. Our results suggest that widespread ECoG coverage of sensorimotor cortex could allow a whole limb BMI to sample native cortical representations in order to control flexion and extension at multiple joints.

Original languageEnglish (US)
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
DOIs
StateAccepted/In press - Jan 1 2019

Fingerprint

Upper Extremity
Decoding
Joints
Brain-Computer Interfaces
Brain
Extremities
Prosthetics
Macros
Elbow
Wrist
Modulation
Fingers
Linear Models
Arm
Research Personnel
Electrocorticography
Research
Sensorimotor Cortex

Keywords

  • Brain modeling
  • Classification
  • Decoding
  • Elbow
  • Electrocorticography
  • Electrodes
  • Extension
  • Flexion
  • Sensor arrays
  • Task analysis
  • Upper Limb
  • Wrist

ASJC Scopus subject areas

  • Neuroscience(all)
  • Biomedical Engineering
  • Computer Science Applications

Cite this

@article{668a4797e12442b988b1fb34c87b7a58,
title = "Decoding native cortical representations for flexion and extension at upper limb joints using electrocorticography",
abstract = "Brain-machine interface (BMI) researchers have traditionally focused on modeling endpoint reaching tasks to provide control of neurally-driven prosthetic arms. Most previous research has focused on achieving endpoint control through a Cartesian-coordinate-centered approach. However, a joint-centered approach could potentially be used to intuitively control a wide range of limb movements. We systematically investigated the feasibility of discriminating between flexion and extension of different upper limb joints using electrocorticography (ECoG) recordings from sensorimotor cortex. Four subjects implanted with macro- ECoG (10 mm spacing), HD-ECoG (5 mm spacing), and/or micro-ECoG arrays (0.9 mm spacing, 4x4 mm coverage), performed randomly cued flexions or extensions of the fingers, wrist, or elbow contralateral to the implanted hemisphere. We trained a linear model to classify six movements using averaged high-gamma power (70-110 Hz) modulations at different latencies with respect to movement onset, and within a time interval restricted to flexion or extension at each joint. Offline decoding models for each subject classified these movements with accuracies of 62-83{\%}. Our results suggest that widespread ECoG coverage of sensorimotor cortex could allow a whole limb BMI to sample native cortical representations in order to control flexion and extension at multiple joints.",
keywords = "Brain modeling, Classification, Decoding, Elbow, Electrocorticography, Electrodes, Extension, Flexion, Sensor arrays, Task analysis, Upper Limb, Wrist",
author = "Thomas, {Tessy M.} and Candrea, {Daniel N.} and Fifer, {Matthew S.} and McMullen, {David P.} and Anderson, {William S} and Thakor, {Nitish V} and Crone, {Nathan E}",
year = "2019",
month = "1",
day = "1",
doi = "10.1109/TNSRE.2019.2891362",
language = "English (US)",
journal = "IEEE Transactions on Neural Systems and Rehabilitation Engineering",
issn = "1534-4320",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - Decoding native cortical representations for flexion and extension at upper limb joints using electrocorticography

AU - Thomas, Tessy M.

AU - Candrea, Daniel N.

AU - Fifer, Matthew S.

AU - McMullen, David P.

AU - Anderson, William S

AU - Thakor, Nitish V

AU - Crone, Nathan E

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Brain-machine interface (BMI) researchers have traditionally focused on modeling endpoint reaching tasks to provide control of neurally-driven prosthetic arms. Most previous research has focused on achieving endpoint control through a Cartesian-coordinate-centered approach. However, a joint-centered approach could potentially be used to intuitively control a wide range of limb movements. We systematically investigated the feasibility of discriminating between flexion and extension of different upper limb joints using electrocorticography (ECoG) recordings from sensorimotor cortex. Four subjects implanted with macro- ECoG (10 mm spacing), HD-ECoG (5 mm spacing), and/or micro-ECoG arrays (0.9 mm spacing, 4x4 mm coverage), performed randomly cued flexions or extensions of the fingers, wrist, or elbow contralateral to the implanted hemisphere. We trained a linear model to classify six movements using averaged high-gamma power (70-110 Hz) modulations at different latencies with respect to movement onset, and within a time interval restricted to flexion or extension at each joint. Offline decoding models for each subject classified these movements with accuracies of 62-83%. Our results suggest that widespread ECoG coverage of sensorimotor cortex could allow a whole limb BMI to sample native cortical representations in order to control flexion and extension at multiple joints.

AB - Brain-machine interface (BMI) researchers have traditionally focused on modeling endpoint reaching tasks to provide control of neurally-driven prosthetic arms. Most previous research has focused on achieving endpoint control through a Cartesian-coordinate-centered approach. However, a joint-centered approach could potentially be used to intuitively control a wide range of limb movements. We systematically investigated the feasibility of discriminating between flexion and extension of different upper limb joints using electrocorticography (ECoG) recordings from sensorimotor cortex. Four subjects implanted with macro- ECoG (10 mm spacing), HD-ECoG (5 mm spacing), and/or micro-ECoG arrays (0.9 mm spacing, 4x4 mm coverage), performed randomly cued flexions or extensions of the fingers, wrist, or elbow contralateral to the implanted hemisphere. We trained a linear model to classify six movements using averaged high-gamma power (70-110 Hz) modulations at different latencies with respect to movement onset, and within a time interval restricted to flexion or extension at each joint. Offline decoding models for each subject classified these movements with accuracies of 62-83%. Our results suggest that widespread ECoG coverage of sensorimotor cortex could allow a whole limb BMI to sample native cortical representations in order to control flexion and extension at multiple joints.

KW - Brain modeling

KW - Classification

KW - Decoding

KW - Elbow

KW - Electrocorticography

KW - Electrodes

KW - Extension

KW - Flexion

KW - Sensor arrays

KW - Task analysis

KW - Upper Limb

KW - Wrist

UR - http://www.scopus.com/inward/record.url?scp=85059812285&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85059812285&partnerID=8YFLogxK

U2 - 10.1109/TNSRE.2019.2891362

DO - 10.1109/TNSRE.2019.2891362

M3 - Article

C2 - 30624221

AN - SCOPUS:85059812285

JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering

JF - IEEE Transactions on Neural Systems and Rehabilitation Engineering

SN - 1534-4320

ER -