Parallel convolutional neural network and empirical mode decomposition for high accuracy in motor imagery EEG signal classification.

ABSTRACT: In recent years, the utilization of motor imagery (MI) signals derived from electroencephalography (EEG) has shown promising applications in controlling various devices such as wheelchairs, assistive technologies, and driverless vehicles. However, decoding EEG signals poses significant challenges due to their complexity, dynamic nature, and low signal-to-noise ratio (SNR). Traditional EEG pattern recognition algorithms typically involve two key steps: feature extraction and feature classification, both crucial for accurate operation. In this work, we propose a novel method that addresses these challenges by employing empirical mode decomposition (EMD) for feature extraction and a parallel convolutional neural network (PCNN) for feature classification. This approach aims to mitigate non-sta

SUBMITTER: D J 

PROVIDER: S-EPMC11737786 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature