Academic Journal
Calibration‐Free Electromyography Motor Intent Decoding Using Large‐Scale Supervised Pretraining.
| Title: | Calibration‐Free Electromyography Motor Intent Decoding Using Large‐Scale Supervised Pretraining. |
|---|---|
| Authors: | Olsson, Alexander E., Maleševic, Nebojša, Björkman, Anders, Antfolk, Christian |
| Source: | Advanced Intelligent Systems (2640-4567); Jun2026, Vol. 8 Issue 6, p1-16, 16p |
| Subject Terms: | Electromyography, Supervised learning, Machine learning, Artificial neural networks, Human-computer interaction, Pattern perception |
| Abstract: | Machine learning algorithms for myoelectric pattern recognition require substantial user‐specific training data, limiting broader applications of electromyography (EMG) in human–computer interfacing. Here, we present a framework for EMG‐mediated motor intent decoding designed to function for new users without collecting user‐specific training data. We introduce a Transformer‐based architecture, termed the Spatially Aware Feature‐learning Transformer (SAFT), which processes EMG time windows with variable numbers of channels from arbitrary spatial electrode configurations by combining channel‐wise temporal feature extraction with learned spatial encoding of electrode positions and attention across channels. This enables training of a single model across heterogeneous EMG datasets. In the present study, large‐scale supervised pretraining refers to pretraining on a pooled corpus of 29 public EMG databases comprising 506 subjects, 108 movement classes, and ≈9.9 million nonrest EMG windows after preprocessing. A pretrained SAFT model was fine‐tuned on a held‐out database and evaluated for cross‐user performance. On the 3DC benchmark, the pretrained‐only model achieved 28.7% balanced accuracy (vs. 10% chance), while pretrained and fine‐tuned cross‐user SAFT models achieved 81.8% balanced accuracy, comparable to conventional user‐specific linear discriminant analysis (LDA) models (82.9%). These findings indicate the feasibility of EMG intent decoding models that work "out of the box" without end‐user calibration. [ABSTRACT FROM AUTHOR] |
| Copyright of Advanced Intelligent Systems (2640-4567) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Calibration‐Free Electromyography Motor Intent Decoding Using Large‐Scale Supervised Pretraining. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Olsson%2C+Alexander+E%2E%22">Olsson, Alexander E.</searchLink><br /><searchLink fieldCode="AR" term="%22Maleševic%2C+Nebojša%22">Maleševic, Nebojša</searchLink><br /><searchLink fieldCode="AR" term="%22Björkman%2C+Anders%22">Björkman, Anders</searchLink><br /><searchLink fieldCode="AR" term="%22Antfolk%2C+Christian%22">Antfolk, Christian</searchLink> – Name: TitleSource Label: Source Group: Src Data: Advanced Intelligent Systems (2640-4567); Jun2026, Vol. 8 Issue 6, p1-16, 16p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Electromyography%22">Electromyography</searchLink><br /><searchLink fieldCode="DE" term="%22Supervised+learning%22">Supervised learning</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Human-computer+interaction%22">Human-computer interaction</searchLink><br /><searchLink fieldCode="DE" term="%22Pattern+perception%22">Pattern perception</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Machine learning algorithms for myoelectric pattern recognition require substantial user‐specific training data, limiting broader applications of electromyography (EMG) in human–computer interfacing. Here, we present a framework for EMG‐mediated motor intent decoding designed to function for new users without collecting user‐specific training data. We introduce a Transformer‐based architecture, termed the Spatially Aware Feature‐learning Transformer (SAFT), which processes EMG time windows with variable numbers of channels from arbitrary spatial electrode configurations by combining channel‐wise temporal feature extraction with learned spatial encoding of electrode positions and attention across channels. This enables training of a single model across heterogeneous EMG datasets. In the present study, large‐scale supervised pretraining refers to pretraining on a pooled corpus of 29 public EMG databases comprising 506 subjects, 108 movement classes, and ≈9.9 million nonrest EMG windows after preprocessing. A pretrained SAFT model was fine‐tuned on a held‐out database and evaluated for cross‐user performance. On the 3DC benchmark, the pretrained‐only model achieved 28.7% balanced accuracy (vs. 10% chance), while pretrained and fine‐tuned cross‐user SAFT models achieved 81.8% balanced accuracy, comparable to conventional user‐specific linear discriminant analysis (LDA) models (82.9%). These findings indicate the feasibility of EMG intent decoding models that work "out of the box" without end‐user calibration. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Advanced Intelligent Systems (2640-4567) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/aisy.202500791 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 1 Subjects: – SubjectFull: Electromyography Type: general – SubjectFull: Supervised learning Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Human-computer interaction Type: general – SubjectFull: Pattern perception Type: general Titles: – TitleFull: Calibration‐Free Electromyography Motor Intent Decoding Using Large‐Scale Supervised Pretraining. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Olsson, Alexander E. – PersonEntity: Name: NameFull: Maleševic, Nebojša – PersonEntity: Name: NameFull: Björkman, Anders – PersonEntity: Name: NameFull: Antfolk, Christian IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 26404567 Numbering: – Type: volume Value: 8 – Type: issue Value: 6 Titles: – TitleFull: Advanced Intelligent Systems (2640-4567) Type: main |
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