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

• Published in: PloS one Vol. 20; no. 1; p. e0311942
• Main Authors: D, Jaipriya, K C, Sriharipriya
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 16.01.2025; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Analysis; Artificial neural networks; Biology and Life Sciences; Brain research; Brain-Computer Interfaces; Classification; Computer and Information Sciences; Convolutional Neural Networks; Datasets; Decoding; Decomposition; Decomposition method; Deep learning; Discriminant analysis; EEG; Electrodes; Electroencephalography; Electroencephalography - methods; Engineering and Technology; Feature extraction; Humans; Imagery; Imagination - physiology; Keyboards; Machine learning; Medicine and Health Sciences; Mental task performance; Motor skill learning; Motor task performance; Neural networks; Pattern recognition; Performance enhancement; Performance evaluation; Performance measurement; Qualitative analysis; Research and Analysis Methods; Sensitivity analysis; Signal classification; Signal Processing, Computer-Assisted; Signal to noise ratio; Transfer learning; Wavelet transforms; Wheelchairs; India
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0311942

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 du...