Estimating the Relationship Between EMG Signals and EEG Signal Connections Using Convolutional Neural Networks

Elham  Samadi; Fereidoun Nowshiravan  Rahatabad; Ali Motie  Nasrabadi; Nader Jafarnia  Dabanloo

doi:10.18502/fbt.v13i1.20759

Elham Samadi Department of Medical Science and Technology, SR.C., Islamic Azad University, Tehran, Iran
Fereidoun Nowshiravan Rahatabad Department of Medical Science and Technology, SR.C., Islamic Azad University, Tehran, Iran
Ali Motie Nasrabadi Department of Biomedical Engineering, Shahed University, Tehran
Nader Jafarnia Dabanloo Department of Medical Science and Technology, SR.C., Islamic Azad University, Tehran, Iran

DOI: https://doi.org/10.18502/fbt.v13i1.20759

Keywords: Vital Signal Connections; Brain Computer Interface; Regression; Convolutional Networks.

Abstract

Purpose: Understanding the functional relationships between different parts of the human body can enhance the control of Brain-Computer Interface (BCI) systems. The brain, as the decision-making organ, controls all body parts to perform activities. In this study, the main objective is to estimate the activation of hand muscles and the effect of each muscle on another using Electroencephalogram (EEG) signals.

Materials and Methods: To discover the connection of hand muscles through brain signals, brain connections are extracted as influential components, and a convolutional network is utilized to assess the impact of EEG signals on the relationships among hand muscles. Five different connectivity methods were used to analyze the connections between EEG signal channels, such as correlation, coherence, the directed transfer function, Granger causality, and the phase delay index. The relationships between electromyogram (EMG) signal channels are also calculated using Granger causality. Signals are recorded in two phases: rest and activity, and ultimately, the EMG signal activity is estimated solely using EEG signals.

Results: Simulation results estimate the correlation between the estimated and actual patterns for test data to be around 0.949, indicating a high correlation between the estimated outputs and actual values.

Conclusion: Research indicates that exploring techniques for calculating relationships can be useful in evaluating the synergy and causal connections between EMG and EEG signals. In comparison to alternative graph-based techniques, this approach, utilizing regression analysis, demonstrated notably superior performance. This study could contribute to advancements in rehabilitation techniques and brain-computer interfaces.