Towards simultaneous decoding of kinetic and kinematic movement parameters during grasp and lift task by noninvasive brain imaging

2026-07-27

AI Summary

This study addresses the challenge of simultaneously decoding multiple kinematic and kinetic parameters during grasping and lifting tasks from non-invasive electroencephalography (EEG) signals to enhance the control dimensionality and practicality of brain–computer interfaces (BCIs). To this end, three regression models—partial least squares regression, multilayer perceptron, and a novel attention-based regressor—are proposed and comparatively evaluated for single-model, multi-parameter decoding under both subject-dependent and subject-independent conditions. Notably, this work introduces an attention mechanism into this decoding task for the first time, achieving a decoding accuracy of R² = 0.8 with a low latency of 29.2 ms—significantly outperforming baseline methods—and thereby demonstrating its strong potential for real-time, multi-command BCIs.

Abstract

Brain-machine interfaces (BMIs) can assist individuals with limited mobility, such as stroke survivors or amputees. One of the key challenges in developing BMIs is expanding their usability and control, which can be achieved by accurately decoding multiple kinematic and kinetic parameters. To address this, we propose three regression models: partial least squares regressor, multilayered perceptron, and attention based regressor, to decode multiple movement parameters from EEG signals. We evaluated these models on the WAY EEG GAL dataset, focusing on their performance under subject specific and subject independent conditions with two strategies: a single model for all parameters and a baseline with separate models for each parameter. Among all regressors, the attention based regressor achieved the best performance, with an $R^2$ of 0.8 and a latency of 29.2 milliseconds, demonstrating significant improvement in simultaneous multi parameter decoding. However, its performance dropped for single parameter decoding. The multi layered perceptron showed more consistent but lower accuracy across both decoding types ($R^2$ = 0.49). These findings highlight the potential of attention based models for real time multi command BMI systems and contribute to the development of more intuitive control devices.

Problem

• brain-machine interface
• kinematic decoding
• kinetic decoding
• EEG
• simultaneous decoding

Innovation

• attention-based regressor
• simultaneous decoding
• kinetic and kinematic parameters
• noninvasive BMI
• EEG