P-81 Decoding phantom arm movement using superficial electromyography

TY - JOUR

T1 - P-81 Decoding phantom arm movement using superficial electromyography

AU - Scaliti, Eugenio

AU - Panzeri, Stefano

AU - Gruppioni, Emanuele

AU - Becchio, Cristina

PY - 2023

Y1 - 2023

N2 - Background: After limb amputation, many amputees report beingable to perform voluntary movements with their phantom hand.Commonly reported voluntary movements of the phantom arm/hand include reaching for an object, forming a fist and moving oneor more individual fingers. This phenomenon raises the question ofhow phantom movements are planned and controlled (Scalitiet al., 2020). Here we used surface electromyography (sEMG) andhigh-density sEMG (HDsEMG) in combination with a decodingapproach to examine the distinctiveness of activity patterns associatedwith large set of arm and finger phantom movements.Objective: (1) Decoding phantom hand and finger movements fromstump sEMG activity and (2) revealing muscular activation patternsassociated with specific phantom movements with high spatialresolution.Methods: Unilateral transradial amputees (N=3; mean age±standarddeviation of the mean=43±13) performed three sets of phantommovements in a fixed order: Hand/wrist phantom movements (includingwrist flexion, wrist extension, close hand, open hand andpinch with three fingers), flexion of individual phantom fingers,and extension of individual phantom fingers. After performing theinstructed movement, amputees were asked to hold the positionfor 3seconds. We recorded sEMG activity from six superficial electrodesembedded in a thermoplastic socket customized for eachamputee. To reveal phantom movement sEMG patterns with highspatial resolution, we also recorded high density sEMG (HDsEMG)signals from one amputee. sEMG and HDsEMG data were fed intoa Linear Discriminant Anlysis (LDA) to classify phantom movements.Results: For each amputee, the decoding of phantom movementsfrom sEMG was well above chance for each set of movements (meanaccuracy±SD=87%±9%, chance level: 20%). HDsEMG revealed movement-specific spatial activity maps over the extensor and flexormuscles of the stump, yielding to a classification accuracy of 85%for 18 movements (chance level: 5.6%).Conclusions: Our results highlight the potential of sEMG andHDsEMG for studying phantom movements. Elucidating the relationshipbetween phantom movements and sEMG not only enhancesour basic understanding of phantom movements but also providespotential avenues for design of novel, more intuitive prosthetic controlstrategies.

AB - Background: After limb amputation, many amputees report beingable to perform voluntary movements with their phantom hand.Commonly reported voluntary movements of the phantom arm/hand include reaching for an object, forming a fist and moving oneor more individual fingers. This phenomenon raises the question ofhow phantom movements are planned and controlled (Scalitiet al., 2020). Here we used surface electromyography (sEMG) andhigh-density sEMG (HDsEMG) in combination with a decodingapproach to examine the distinctiveness of activity patterns associatedwith large set of arm and finger phantom movements.Objective: (1) Decoding phantom hand and finger movements fromstump sEMG activity and (2) revealing muscular activation patternsassociated with specific phantom movements with high spatialresolution.Methods: Unilateral transradial amputees (N=3; mean age±standarddeviation of the mean=43±13) performed three sets of phantommovements in a fixed order: Hand/wrist phantom movements (includingwrist flexion, wrist extension, close hand, open hand andpinch with three fingers), flexion of individual phantom fingers,and extension of individual phantom fingers. After performing theinstructed movement, amputees were asked to hold the positionfor 3seconds. We recorded sEMG activity from six superficial electrodesembedded in a thermoplastic socket customized for eachamputee. To reveal phantom movement sEMG patterns with highspatial resolution, we also recorded high density sEMG (HDsEMG)signals from one amputee. sEMG and HDsEMG data were fed intoa Linear Discriminant Anlysis (LDA) to classify phantom movements.Results: For each amputee, the decoding of phantom movementsfrom sEMG was well above chance for each set of movements (meanaccuracy±SD=87%±9%, chance level: 20%). HDsEMG revealed movement-specific spatial activity maps over the extensor and flexormuscles of the stump, yielding to a classification accuracy of 85%for 18 movements (chance level: 5.6%).Conclusions: Our results highlight the potential of sEMG andHDsEMG for studying phantom movements. Elucidating the relationshipbetween phantom movements and sEMG not only enhancesour basic understanding of phantom movements but also providespotential avenues for design of novel, more intuitive prosthetic controlstrategies.

U2 - 10.1016/j.clinph.2023.02.098

DO - 10.1016/j.clinph.2023.02.098

M3 - Conference abstract in journal

VL - 148

SP - e45

JO - CLIN NEUROPHYSIOL

JF - CLIN NEUROPHYSIOL

SN - 1388-2457

ER -