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

TL;DR

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 sheds light on the temporal and spatial evolution of motor intention.

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.

Figures (10)

• Figure 1: Experimental setup and task structure: a) VR environment from the participant’s perspective. b) target classes. c) placement of motion capture markers and EMG electrodes. d) EMG and End-EFfector trajectory, with vertical lines at task events. e) EEF trajectory. f) task event timeline with colored markers indicating task states.
• Figure 1: Random Forest optimization.
• Figure 2: EMG channel relevance for single channel and leave one channel out.
• Figure 3: Random forest for $12$ targets: a) target distribution c) performance per subject.
• Figure 3: Window wise permutation importance.