Neurophysiological Analysis in Motor and Sensory Cortices for Improving Motor Imagination

TL;DR

This study investigates neural signatures in motor and sensory cortices during motor execution and imagery tasks using EEG, revealing spatial activation patterns and evaluating neural network models to enhance BCI performance.

Contribution

It identifies condition-specific activation patterns in the sensorimotor cortex and compares neural network models for classifying motor and sensory tasks, advancing BCI development.

Findings

Sense-related conditions activate posterior sensorimotor cortex regions.

Motor-related conditions activate anterior sensorimotor cortex regions.

DeepConvNet achieves highest accuracy in classifying motor tasks.

Abstract

Brain-computer interface (BCI) enables direct communication between the brain and external devices by decoding neural signals, offering potential solutions for individuals with motor impairments. This study explores the neural signatures of motor execution (ME) and motor imagery (MI) tasks using EEG signals, focusing on four conditions categorized as sense-related (hot and cold) and motor-related (pull and push) conditions. We conducted scalp topography analysis to examine activation patterns in the sensorimotor cortex, revealing distinct regional differences: sense--related conditions primarily activated the posterior region of the sensorimotor cortex, while motor--related conditions activated the anterior region of the sensorimotor cortex. These spatial distinctions align with neurophysiological principles, suggesting condition-specific functional subdivisions within the sensorimotor cortex. We further evaluated the performances of three neural network models-EEGNet, ShallowConvNet, and DeepConvNet-demonstrating that ME tasks achieved higher classification accuracies compared to MI tasks. Specifically, in sense-related conditions, the highest accuracy was observed in the cold condition. In motor-related conditions, the pull condition showed the highest performance, with DeepConvNet yielding the highest results. These findings provide insights into optimizing BCI applications by leveraging specific condition-induced neural activations.