Spatio-temporal warping for myoelectric control: an offline, feasibility study

Milad Jabbari, Rami Khushaba, Kianoush Nazarpour

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Objective. The efficacy of an adopted feature extraction method directly affects the classification of the electromyographic (EMG) signals in myoelectric control applications. Most methods attempt to extract the dynamics of the multi-channel EMG signals in the time domain and on a channel-by-channel, or at best pairs of channels, basis. However, considering multi-channel information to build a similarity matrix has not been taken into account. Approach. Combining methods of long and short-term memory (LSTM) and dynamic temporal warping, we developed a new feature, called spatio-temporal warping (STW), for myoelectric signals. This method captures the spatio-temporal relationships of multi-channels EMG signals. Main results. Across four online databases, we show that in terms of average classification error and standard deviation values, the STW feature outperforms traditional features by 5%–17%. In comparison to the more recent deep learning models, e.g. convolutional neural networks (CNNs), STW outperformed by 5%–18%. Also, STW showed enhanced performance when compared to the CNN + LSTM model by 2%–14%. All differences were statistically significant with a large effect size. Significance. This feasibility study provides evidence supporting the hypothesis that the STW feature of the EMG signals can enhance the classification accuracy in an explainable way when compared to recent deep learning methods. Future work includes real-time implementation of the method and testing for prosthesis control.
Original languageEnglish
Article number066028
Number of pages14
JournalJournal of Neural Engineering
Issue number6
Publication statusPublished - 1 Dec 2021

Keywords / Materials (for Non-textual outputs)

  • electromyographic signals (EMG)
  • spatio-temporal information
  • feature extraction
  • deep learning
  • myoelectric control


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