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Updated: May 17, 2026

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Decoding sign language finger flexions from high-density electrocorticography using graph-optimized block term tensor

Axel Faes1, Eva Calvo Merino1, Mariana P Branco2

  • 1KU Leuven-University of Leuven, Department of Neurosciences, Laboratory for Neuro- & Psychophysiology, B-3000 Leuven, Belgium.

Journal of Neural Engineering
|April 16, 2025
PubMed
Summary
This summary is machine-generated.

A new method, graph-optimized block-term tensor regression (Go-BTTR), decodes sign language finger movements from electrocorticography (ECoG) data. Go-BTTR improves predictions of complex hand gestures by accounting for finger co-activations, offering computational efficiency for brain-computer interfaces.

Keywords:
BTTRECoGfingergesturesregressionssign language

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Electrocorticography (ECoG) records brain activity, offering potential for brain-computer interfaces (BCIs).
  • Decoding complex motor intentions like sign language from ECoG is challenging due to non-linear relationships and finger co-activations.

Purpose of the Study:

  • Introduce a novel method, graph-optimized block-term tensor regression (Go-BTTR), for regressing sign language finger movements from ECoG data.
  • Improve the accuracy and efficiency of decoding complex hand gestures for BCI applications.

Main Methods:

  • Go-BTTR combines a deflation-based regression model (Tucker decomposition) with a causal graph process (CGP).
  • CGP dynamically groups or separates fingers based on their relationships, informing the regression model (BTTR or eBTTR).
  • The method was validated on two ECoG datasets involving American and Flemish sign language gestures.

Main Results:

  • Go-BTTR demonstrated superior joint finger trajectory predictions compared to existing methods (eBTTR, BTTR).
  • Average correlations achieved were 0.73 (American Sign Language) and 0.37 (Flemish Sign Language) for Go-BTTR.
  • The method effectively accounts for non-linear ECoG relationships and unintentional finger co-activations.

Conclusions:

  • Go-BTTR successfully decodes complex hand gestures from sign language alphabets using ECoG data.
  • The method offers computational efficiency, beneficial for pre-surgical evaluation and BCI development.
  • Go-BTTR represents a significant advancement in decoding intricate motor intentions for BCI applications.