Decoding Imagined 3D Arm Movement Trajectories from EEG to Control Two Virtual Arms - A Pilot Study

Research output: Contribution to journalArticle

Abstract

Background: Realization of online control of an artificial or virtual arm using information decoded from EEG normally occurs by classifying different activation states or voluntary modulation of the sensorimotor activity linked to different overt actions of the subject. However, using a more natural control scheme, such as decoding the trajectory of imagined 3D arm movements to move a prosthetic, robotic, or virtual arm has been reported in a limited amount of studies, all using offline feed-forward control schemes. Objective: In this study, we report the first attempt to realize online control of two virtual arms generating movements toward three targets/arm in 3D space. The 3D trajectory of imagined arm movements was decoded from power spectral density of mu, low beta, high beta, and low gamma EEG oscillations using multiple linear regression. The analysis was performed on a dataset recorded from three subjects in seven sessions wherein each session comprised three experimental blocks: an offline calibration block and two online feedback blocks. Target classification accuracy using predicted trajectories of the virtual arms was computed and compared with results of a filter-bank common spatial patterns (FBCSP) based multi-class classification method involving mutual information (MI) selection and linear discriminant analysis (LDA) modules. Main Results: Target classification accuracy from predicted trajectory of imagined 3D arm movements in the offline runs for two subjects (mean 45%, std 5%) was significantly higher (p < 0.05) than chance level (33.3%). Nevertheless, the accuracy during real-time control of the virtual arms using the trajectory decoded directly from EEG was in the range of chance level (33.3%). However, the results of two subjects show that false-positive feedback may increase the accuracy in closed-loop. The FBCSP based multi-class classification method distinguished imagined movements of left and right arm with reasonable accuracy for two of the three subjects (mean 70%, std 5% compared to 50% chance level). However, classification of the imagined arm movement toward three targets was not successful with the FBCSP classifier as the achieved accuracy (mean 33%, std 5%) was similar to the chance level (33.3%). Sub-optimal components of the multi-session experimental paradigm were identified, and an improved paradigm proposed.

LanguageEnglish
Article number94
Number of pages22
JournalFrontiers in Neurorobotics
Volume13
Issue numberNovember
DOIs
Publication statusPublished - 14 Nov 2019

Fingerprint

Electroencephalography
Decoding
Trajectories
Filter banks
Feedback
Feedforward control
Power spectral density
Real time control
Discriminant analysis
Prosthetics
Linear regression
Robotics
Classifiers
Chemical activation
Modulation
Calibration

Keywords

  • Brain-computer interface (BCI)
  • Electroencephalography
  • Filter-bank common spatial Patterns (FBCSP)
  • Imagined 3D arm movements
  • Multiple linear regression (mLR)
  • Online motion trajectory prediction
  • Virtual robotic arm

Cite this

@article{4f5a68a1c61743d38ca9edc5d158bf70,
title = "Decoding Imagined 3D Arm Movement Trajectories from EEG to Control Two Virtual Arms - A Pilot Study",
abstract = "Background: Realization of online control of an artificial or virtual arm using information decoded from EEG normally occurs by classifying different activation states or voluntary modulation of the sensorimotor activity linked to different overt actions of the subject. However, using a more natural control scheme, such as decoding the trajectory of imagined 3D arm movements to move a prosthetic, robotic, or virtual arm has been reported in a limited amount of studies, all using offline feed-forward control schemes. Objective: In this study, we report the first attempt to realize online control of two virtual arms generating movements toward three targets/arm in 3D space. The 3D trajectory of imagined arm movements was decoded from power spectral density of mu, low beta, high beta, and low gamma EEG oscillations using multiple linear regression. The analysis was performed on a dataset recorded from three subjects in seven sessions wherein each session comprised three experimental blocks: an offline calibration block and two online feedback blocks. Target classification accuracy using predicted trajectories of the virtual arms was computed and compared with results of a filter-bank common spatial patterns (FBCSP) based multi-class classification method involving mutual information (MI) selection and linear discriminant analysis (LDA) modules. Main Results: Target classification accuracy from predicted trajectory of imagined 3D arm movements in the offline runs for two subjects (mean 45{\%}, std 5{\%}) was significantly higher (p < 0.05) than chance level (33.3{\%}). Nevertheless, the accuracy during real-time control of the virtual arms using the trajectory decoded directly from EEG was in the range of chance level (33.3{\%}). However, the results of two subjects show that false-positive feedback may increase the accuracy in closed-loop. The FBCSP based multi-class classification method distinguished imagined movements of left and right arm with reasonable accuracy for two of the three subjects (mean 70{\%}, std 5{\%} compared to 50{\%} chance level). However, classification of the imagined arm movement toward three targets was not successful with the FBCSP classifier as the achieved accuracy (mean 33{\%}, std 5{\%}) was similar to the chance level (33.3{\%}). Sub-optimal components of the multi-session experimental paradigm were identified, and an improved paradigm proposed.",
keywords = "Brain-computer interface (BCI), Electroencephalography, Filter-bank common spatial Patterns (FBCSP), Imagined 3D arm movements, Multiple linear regression (mLR), Online motion trajectory prediction, Virtual robotic arm",
author = "Attila Korik and Ronen Sosnik and Nazmul Siddique and Damien Coyle",
year = "2019",
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doi = "10.3389/fnbot.2019.00094",
language = "English",
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Decoding Imagined 3D Arm Movement Trajectories from EEG to Control Two Virtual Arms - A Pilot Study. / Korik, Attila; Sosnik, Ronen; Siddique, Nazmul; Coyle, Damien.

In: Frontiers in Neurorobotics, Vol. 13, No. November, 94, 14.11.2019.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Decoding Imagined 3D Arm Movement Trajectories from EEG to Control Two Virtual Arms - A Pilot Study

AU - Korik, Attila

AU - Sosnik, Ronen

AU - Siddique, Nazmul

AU - Coyle, Damien

PY - 2019/11/14

Y1 - 2019/11/14

N2 - Background: Realization of online control of an artificial or virtual arm using information decoded from EEG normally occurs by classifying different activation states or voluntary modulation of the sensorimotor activity linked to different overt actions of the subject. However, using a more natural control scheme, such as decoding the trajectory of imagined 3D arm movements to move a prosthetic, robotic, or virtual arm has been reported in a limited amount of studies, all using offline feed-forward control schemes. Objective: In this study, we report the first attempt to realize online control of two virtual arms generating movements toward three targets/arm in 3D space. The 3D trajectory of imagined arm movements was decoded from power spectral density of mu, low beta, high beta, and low gamma EEG oscillations using multiple linear regression. The analysis was performed on a dataset recorded from three subjects in seven sessions wherein each session comprised three experimental blocks: an offline calibration block and two online feedback blocks. Target classification accuracy using predicted trajectories of the virtual arms was computed and compared with results of a filter-bank common spatial patterns (FBCSP) based multi-class classification method involving mutual information (MI) selection and linear discriminant analysis (LDA) modules. Main Results: Target classification accuracy from predicted trajectory of imagined 3D arm movements in the offline runs for two subjects (mean 45%, std 5%) was significantly higher (p < 0.05) than chance level (33.3%). Nevertheless, the accuracy during real-time control of the virtual arms using the trajectory decoded directly from EEG was in the range of chance level (33.3%). However, the results of two subjects show that false-positive feedback may increase the accuracy in closed-loop. The FBCSP based multi-class classification method distinguished imagined movements of left and right arm with reasonable accuracy for two of the three subjects (mean 70%, std 5% compared to 50% chance level). However, classification of the imagined arm movement toward three targets was not successful with the FBCSP classifier as the achieved accuracy (mean 33%, std 5%) was similar to the chance level (33.3%). Sub-optimal components of the multi-session experimental paradigm were identified, and an improved paradigm proposed.

AB - Background: Realization of online control of an artificial or virtual arm using information decoded from EEG normally occurs by classifying different activation states or voluntary modulation of the sensorimotor activity linked to different overt actions of the subject. However, using a more natural control scheme, such as decoding the trajectory of imagined 3D arm movements to move a prosthetic, robotic, or virtual arm has been reported in a limited amount of studies, all using offline feed-forward control schemes. Objective: In this study, we report the first attempt to realize online control of two virtual arms generating movements toward three targets/arm in 3D space. The 3D trajectory of imagined arm movements was decoded from power spectral density of mu, low beta, high beta, and low gamma EEG oscillations using multiple linear regression. The analysis was performed on a dataset recorded from three subjects in seven sessions wherein each session comprised three experimental blocks: an offline calibration block and two online feedback blocks. Target classification accuracy using predicted trajectories of the virtual arms was computed and compared with results of a filter-bank common spatial patterns (FBCSP) based multi-class classification method involving mutual information (MI) selection and linear discriminant analysis (LDA) modules. Main Results: Target classification accuracy from predicted trajectory of imagined 3D arm movements in the offline runs for two subjects (mean 45%, std 5%) was significantly higher (p < 0.05) than chance level (33.3%). Nevertheless, the accuracy during real-time control of the virtual arms using the trajectory decoded directly from EEG was in the range of chance level (33.3%). However, the results of two subjects show that false-positive feedback may increase the accuracy in closed-loop. The FBCSP based multi-class classification method distinguished imagined movements of left and right arm with reasonable accuracy for two of the three subjects (mean 70%, std 5% compared to 50% chance level). However, classification of the imagined arm movement toward three targets was not successful with the FBCSP classifier as the achieved accuracy (mean 33%, std 5%) was similar to the chance level (33.3%). Sub-optimal components of the multi-session experimental paradigm were identified, and an improved paradigm proposed.

KW - Brain-computer interface (BCI)

KW - Electroencephalography

KW - Filter-bank common spatial Patterns (FBCSP)

KW - Imagined 3D arm movements

KW - Multiple linear regression (mLR)

KW - Online motion trajectory prediction

KW - Virtual robotic arm

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DO - 10.3389/fnbot.2019.00094

M3 - Article

VL - 13

JO - Frontiers in Neurorobotics

T2 - Frontiers in Neurorobotics

JF - Frontiers in Neurorobotics

SN - 1662-5218

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