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Dimensionality reduction for classification of object weight from electromyography

Lashgari, Elnaz and Maoz, Uri (2021) Dimensionality reduction for classification of object weight from electromyography. PLoS ONE, 16 (8). Art. No. e0255926. ISSN 1932-6203. PMCID PMC8367006. doi:10.1371/journal.pone.0255926.

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Electromyography (EMG) is a simple, non-invasive, and cost-effective technology for measuring muscle activity. However, multi-muscle EMG is also a noisy, complex, and high-dimensional signal. It has nevertheless been widely used in a host of human-machine-interface applications (electrical wheelchairs, virtual computer mice, prosthesis, robotic fingers, etc.) and, in particular, to measure the reach-and-grasp motions of the human hand. Here, we developed an automated pipeline to predict object weight in a reach-grasp-lift task from an open dataset, relying only on EMG data. In doing so, we shifted the focus from manual feature-engineering to automated feature-extraction by using pre-processed EMG signals and thus letting the algorithms select the features. We further compared intrinsic EMG features, derived from several dimensionality-reduction methods, and then ran several classification algorithms on these low-dimensional representations. We found that the Laplacian Eigenmap algorithm generally outperformed other dimensionality-reduction methods. What is more, optimal classification accuracy was achieved using a combination of Laplacian Eigenmaps (simple-minded) and k-Nearest Neighbors (88% F1 score for 3-way classification). Our results, using EMG alone, are comparable to other researchers’, who used EMG and EEG together, in the literature. A running-window analysis further suggests that our method captures information in the EMG signal quickly and remains stable throughout the time that subjects grasp and move the object.

Item Type:Article
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URLURL TypeDescription CentralArticle Paper
Lashgari, Elnaz0000-0002-0302-8098
Maoz, Uri0000-0002-7899-1241
Alternate Title:Decoding Object Weight from Electromyography during Human Grasping
Additional Information:© 2021 Lashgari, Maoz. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Received: February 26, 2020; Accepted: July 27, 2021; Published: August 16, 2021. This publication was made possible in part through the support from an Investigator Sponsored Research grant from the Boston Scientific Corporation. This publication was also made possible in part through the support of the John Templeton Foundation and the Fetzer Institute. The opinions expressed in this publication are those of the author(s) and do not necessarily reflect the views of the John Templeton Foundation or the Fetzer Institute. Data Availability Statement: The WAY_EEG_GAL dataset is freely available and has become somewhat of a benchmark to test techniques to decode sensation, intention, and action from surface EMG and scalp EEG in humans performing a reach-and-grasp task ( We know of no conflicts of interest associated with this publication. Author Contributions: Conceptualization: Uri Maoz. Formal analysis: Elnaz Lashgari. Methodology: Elnaz Lashgari. Supervision: Uri Maoz. Validation: Elnaz Lashgari, Uri Maoz. Visualization: Elnaz Lashgari. Writing – original draft: Elnaz Lashgari. Writing – review & editing: Elnaz Lashgari, Uri Maoz.
Funding AgencyGrant Number
Boston Scientific CorporationUNSPECIFIED
John Templeton FoundationUNSPECIFIED
Fetzer InstituteUNSPECIFIED
Issue or Number:8
PubMed Central ID:PMC8367006
Record Number:CaltechAUTHORS:20210930-214220619
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Official Citation:Lashgari E, Maoz U (2021). Dimensionality reduction for classification of object weight from electromyography. PLoS ONE 16(8): e0255926.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:111140
Deposited By: Tony Diaz
Deposited On:04 Oct 2021 20:31
Last Modified:04 Oct 2021 20:37

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