EEG and Motor Effects of Multimodal Feedback to Train Functional Grasp after Traumatic Brain Injury

Mingxiao Liu; Samuel Wilder; Sean Sanford; Sophie Dewil; Soha Saleh; Raviraj Nataraj

doi:10.1109/CBMS58004.2023.00246

EEG and Motor Effects of Multimodal Feedback to Train Functional Grasp after Traumatic Brain Injury

Mingxiao Liu
, Samuel Wilder
, Sean Sanford
, Sophie Dewil
, Soha Saleh
, Raviraj Nataraj

Department of Biomedical Engineering

Stevens Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Physical therapy is crucial to rehabilitating hand function needed for activities of daily living after neurological traumas such as traumatic brain injury (TBI). Virtual reality (VR) can motivate participation in motor rehabilitation therapies. Computerized interfaces like VR could be further leveraged to provide augmented feedback during training that accelerates motor learning. In prior work, our lab demonstrated improved performance of a functional grasp task after training with augmented feedback from a 'smart' glove. The glove has modules and computational intelligence to detect and cue users about secure grasp. In this study, we incorporated VR with the glove to enhance the augmented feedback. We then examined how multimodal (audio and visual cues) feedback impacted performance and neurological responses compared to unimodal feedback (audio cues only) after TBI (n=5) versus neurotypicals (n=10). After training with multimodal feedback for a grasp-and-place task, electroencephalography (EEG) alpha power significantly increased for TBI and neurotypical groups. However, only the TBI group significantly improved performance (i.e., reduced motion pathlength). These results suggest a neurological basis for the benefits of training with multimodal feedback. Adding sensory cues may better consolidate early motor learning in the presence of neurological dysfunction, as indicated by higher alpha activity with improved performance. Computerized interfaces such as virtual reality offer a powerful platform to customize rehabilitative training and improve functional outcomes according to specific neurological states.

Original language	American English
Title of host publication	Proceedings - 2023 IEEE 36th International Symposium on Computer-Based Medical Systems, CBMS 2023
Editors	Rosa Sicilia, Bridget Kane, Joao Rafael Almeida, Myra Spiliopoulou, Jose Alberto Benitez Andrades, Giuseppe Placidi, Alejandro Rodriguez Gonzalez
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	374-377
Number of pages	4
ISBN (Electronic)	9798350312249
DOIs	https://doi.org/10.1109/CBMS58004.2023.00246
State	Published - 2023
Event	36th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2023 - L�Aquila, Italy Duration: Jun 22 2023 → Jun 24 2023

Publication series

Name	Proceedings - IEEE Symposium on Computer-Based Medical Systems
Volume	2023-June

Conference

Conference	36th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2023
Country/Territory	Italy
City	L�Aquila
Period	6/22/23 → 6/24/23

ASJC Scopus subject areas

Radiology Nuclear Medicine and imaging
Computer Science Applications

Keywords

grasp
rehabilitation
traumatic brain injury
virtual reality

Access to Document

10.1109/CBMS58004.2023.00246

Cite this

Liu, M., Wilder, S., Sanford, S., Dewil, S., Saleh, S., & Nataraj, R. (2023). EEG and Motor Effects of Multimodal Feedback to Train Functional Grasp after Traumatic Brain Injury. In R. Sicilia, B. Kane, J. R. Almeida, M. Spiliopoulou, J. A. B. Andrades, G. Placidi, & A. R. Gonzalez (Eds.), Proceedings - 2023 IEEE 36th International Symposium on Computer-Based Medical Systems, CBMS 2023 (pp. 374-377). (Proceedings - IEEE Symposium on Computer-Based Medical Systems; Vol. 2023-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CBMS58004.2023.00246

Liu, Mingxiao ; Wilder, Samuel ; Sanford, Sean et al. / EEG and Motor Effects of Multimodal Feedback to Train Functional Grasp after Traumatic Brain Injury. Proceedings - 2023 IEEE 36th International Symposium on Computer-Based Medical Systems, CBMS 2023. editor / Rosa Sicilia ; Bridget Kane ; Joao Rafael Almeida ; Myra Spiliopoulou ; Jose Alberto Benitez Andrades ; Giuseppe Placidi ; Alejandro Rodriguez Gonzalez. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 374-377 (Proceedings - IEEE Symposium on Computer-Based Medical Systems).

@inproceedings{e02c52ec156d46e3b6de123daa78c696,

title = "EEG and Motor Effects of Multimodal Feedback to Train Functional Grasp after Traumatic Brain Injury",

abstract = "Physical therapy is crucial to rehabilitating hand function needed for activities of daily living after neurological traumas such as traumatic brain injury (TBI). Virtual reality (VR) can motivate participation in motor rehabilitation therapies. Computerized interfaces like VR could be further leveraged to provide augmented feedback during training that accelerates motor learning. In prior work, our lab demonstrated improved performance of a functional grasp task after training with augmented feedback from a 'smart' glove. The glove has modules and computational intelligence to detect and cue users about secure grasp. In this study, we incorporated VR with the glove to enhance the augmented feedback. We then examined how multimodal (audio and visual cues) feedback impacted performance and neurological responses compared to unimodal feedback (audio cues only) after TBI (n=5) versus neurotypicals (n=10). After training with multimodal feedback for a grasp-and-place task, electroencephalography (EEG) alpha power significantly increased for TBI and neurotypical groups. However, only the TBI group significantly improved performance (i.e., reduced motion pathlength). These results suggest a neurological basis for the benefits of training with multimodal feedback. Adding sensory cues may better consolidate early motor learning in the presence of neurological dysfunction, as indicated by higher alpha activity with improved performance. Computerized interfaces such as virtual reality offer a powerful platform to customize rehabilitative training and improve functional outcomes according to specific neurological states.",

keywords = "grasp, rehabilitation, traumatic brain injury, virtual reality",

author = "Mingxiao Liu and Samuel Wilder and Sean Sanford and Sophie Dewil and Soha Saleh and Raviraj Nataraj",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 36th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2023 ; Conference date: 22-06-2023 Through 24-06-2023",

year = "2023",

doi = "10.1109/CBMS58004.2023.00246",

language = "American English",

series = "Proceedings - IEEE Symposium on Computer-Based Medical Systems",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "374--377",

editor = "Rosa Sicilia and Bridget Kane and Almeida, \{Joao Rafael\} and Myra Spiliopoulou and Andrades, \{Jose Alberto Benitez\} and Giuseppe Placidi and Gonzalez, \{Alejandro Rodriguez\}",

booktitle = "Proceedings - 2023 IEEE 36th International Symposium on Computer-Based Medical Systems, CBMS 2023",

address = "United States",

}

Liu, M, Wilder, S, Sanford, S, Dewil, S, Saleh, S & Nataraj, R 2023, EEG and Motor Effects of Multimodal Feedback to Train Functional Grasp after Traumatic Brain Injury. in R Sicilia, B Kane, JR Almeida, M Spiliopoulou, JAB Andrades, G Placidi & AR Gonzalez (eds), Proceedings - 2023 IEEE 36th International Symposium on Computer-Based Medical Systems, CBMS 2023. Proceedings - IEEE Symposium on Computer-Based Medical Systems, vol. 2023-June, Institute of Electrical and Electronics Engineers Inc., pp. 374-377, 36th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2023, L�Aquila, Italy, 6/22/23. https://doi.org/10.1109/CBMS58004.2023.00246

EEG and Motor Effects of Multimodal Feedback to Train Functional Grasp after Traumatic Brain Injury. / Liu, Mingxiao; Wilder, Samuel; Sanford, Sean et al.
Proceedings - 2023 IEEE 36th International Symposium on Computer-Based Medical Systems, CBMS 2023. ed. / Rosa Sicilia; Bridget Kane; Joao Rafael Almeida; Myra Spiliopoulou; Jose Alberto Benitez Andrades; Giuseppe Placidi; Alejandro Rodriguez Gonzalez. Institute of Electrical and Electronics Engineers Inc., 2023. p. 374-377 (Proceedings - IEEE Symposium on Computer-Based Medical Systems; Vol. 2023-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - EEG and Motor Effects of Multimodal Feedback to Train Functional Grasp after Traumatic Brain Injury

AU - Liu, Mingxiao

AU - Wilder, Samuel

AU - Sanford, Sean

AU - Dewil, Sophie

AU - Saleh, Soha

AU - Nataraj, Raviraj

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AB - Physical therapy is crucial to rehabilitating hand function needed for activities of daily living after neurological traumas such as traumatic brain injury (TBI). Virtual reality (VR) can motivate participation in motor rehabilitation therapies. Computerized interfaces like VR could be further leveraged to provide augmented feedback during training that accelerates motor learning. In prior work, our lab demonstrated improved performance of a functional grasp task after training with augmented feedback from a 'smart' glove. The glove has modules and computational intelligence to detect and cue users about secure grasp. In this study, we incorporated VR with the glove to enhance the augmented feedback. We then examined how multimodal (audio and visual cues) feedback impacted performance and neurological responses compared to unimodal feedback (audio cues only) after TBI (n=5) versus neurotypicals (n=10). After training with multimodal feedback for a grasp-and-place task, electroencephalography (EEG) alpha power significantly increased for TBI and neurotypical groups. However, only the TBI group significantly improved performance (i.e., reduced motion pathlength). These results suggest a neurological basis for the benefits of training with multimodal feedback. Adding sensory cues may better consolidate early motor learning in the presence of neurological dysfunction, as indicated by higher alpha activity with improved performance. Computerized interfaces such as virtual reality offer a powerful platform to customize rehabilitative training and improve functional outcomes according to specific neurological states.

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Liu M, Wilder S, Sanford S, Dewil S, Saleh S , Nataraj R. EEG and Motor Effects of Multimodal Feedback to Train Functional Grasp after Traumatic Brain Injury. In Sicilia R, Kane B, Almeida JR, Spiliopoulou M, Andrades JAB, Placidi G, Gonzalez AR, editors, Proceedings - 2023 IEEE 36th International Symposium on Computer-Based Medical Systems, CBMS 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 374-377. (Proceedings - IEEE Symposium on Computer-Based Medical Systems). doi: 10.1109/CBMS58004.2023.00246

EEG and Motor Effects of Multimodal Feedback to Train Functional Grasp after Traumatic Brain Injury

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ASJC Scopus subject areas

Keywords

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