Towards simultaneous decoding of kinetic and kinematic movement parameters during grasp and lift task by noninvasive brain imaging
2026-07-27 • Human-Computer Interaction
Human-Computer InteractionArtificial IntelligenceEmerging Technologies
AI summaryⓘ
The authors studied ways to decode different movement signals from brain activity using EEG, to help create better brain-machine interfaces for people with limited mobility. They tested three computer models and found that an attention-based model worked best when trying to decode multiple movement signals at once, showing good accuracy and speed. However, this model was less effective when decoding just one signal. Another simpler model was more consistent but less accurate overall. Their work suggests that attention-based models could improve real-time control of devices by interpreting multiple commands from the brain simultaneously.
Brain-machine interface (BMI)Electroencephalography (EEG)Regression modelsPartial least squaresMultilayer perceptronAttention mechanismKinematic parametersKinetic parametersReal-time decodingWAY EEG GAL dataset
Authors
Parth G. Dangi, Yogesh Kumaar Meena
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.