The Spatial and Temporal Resolution of Motor Intention in Multi-Target Prediction

TL;DR

This study demonstrates that human motor intentions can be accurately decoded from EMG signals with high spatial and temporal resolution, enabling anticipatory control in rehabilitation and assistive devices.

Contribution

It introduces a computational pipeline combining temporal segmentation and deep learning for predicting motor intentions from EMG data during different movement phases.

Findings

Random Forest achieves 80% accuracy across 25 targets.

CNN achieves 75% accuracy across 25 targets.

Motor intention can be decoded with reduced data, enhancing efficiency.

Abstract

Reaching for grasping, and manipulating objects are essential motor functions in everyday life. Decoding human motor intentions is a central challenge for rehabilitation and assistive technologies. This study focuses on predicting intentions by inferring movement direction and target location from multichannel electromyography (EMG) signals, and investigating how spatially and temporally accurate such information can be detected relative to movement onset. We present a computational pipeline that combines data-driven temporal segmentation with classical and deep learning classifiers in order to analyse EMG data recorded during the planning, early execution, and target contact phases of a delayed reaching task. Early intention prediction enables devices to anticipate user actions, improving responsiveness and supporting active motor recovery in adaptive rehabilitation systems. Random Forest achieves $80\%$ accuracy and Convolutional Neural Network $75\%$ accuracy across $25$ spatial targets, each separated by $14^\circ$ azimuth/altitude. Furthermore, a systematic evaluation of EMG channels, feature sets, and temporal windows demonstrates that motor intention can be efficiently decoded even with drastically reduced data. This work sheds light on the temporal and spatial evolution of motor intention, paving the way for anticipatory control in adaptive rehabilitation systems and driving advancements in computational approaches to motor neuroscience.