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Neurophysiological Analysis in Motor and Sensory Cortices for Improving Motor Imagination

Si-Hyun Kim, Sung-Jin Kim, Dae-Hyeok Lee

TL;DR

The neural signatures of motor execution and motor imagery tasks using EEG signals using EEG signals are explored, demonstrating that ME tasks achieved higher classification accuracies compared to MI tasks and insights into optimizing BCI applications by leveraging specific condition-induced neural activations are provided.

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.

Neurophysiological Analysis in Motor and Sensory Cortices for Improving Motor Imagination

TL;DR

The neural signatures of motor execution and motor imagery tasks using EEG signals using EEG signals are explored, demonstrating that ME tasks achieved higher classification accuracies compared to MI tasks and insights into optimizing BCI applications by leveraging specific condition-induced neural activations are provided.

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.

Paper Structure

This paper contains 10 sections, 1 equation, 3 figures, 1 table.

Table of Contents

  1. INTRODUCTION
  2. MATERIALS AND METHODS
  3. Subject
  4. Experimental Paradigms and Tasks
  5. Experimental Environment
  6. Power Spectral Density (PSD)
  7. RESULTS AND DISCUSSION
  8. Neurophysiological Analysis from EEG Signals
  9. Performance Evaluation with the Conventional Models
  10. CONCLUSION

Figures (3)

  • Figure 1: Experimental paradigms for acquiring ME-- and MI--based EEG signals. (a) The paradigm for acquiring ME--based EEG signals. (b) The paradigm for acquiring MI--based EEG signals.
  • Figure 2: Channel location for acquiring EEG signals.
  • Figure 3: Comparison of the sclap topographies on various conditions in ME and MI tasks. We divided conditions into sense--related (hot and cold) and motor--related conditions (pull and push).