Neurophysiological Analysis in Motor and Sensory Cortices for Improving Motor Imagination

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...

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Vydáno v:The ... International Winter Conference on Brain-Computer Interface s. 1 - 4
Hlavní autoři: Kim, Si-Hyun, Kim, Sung-Jin, Lee, Dae-Hyeok
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 24.02.2025
Témata:
Accuracy
Brain modeling
brain-computer interface
Brain-computer interfaces
Decoding
electroencephalogram
Electroencephalography
Focusing
motor execution
motor imagery
Motors
Robustness
Scalp
Surfaces
ISSN:2572-7672
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Shrnutí: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.
ISSN:2572-7672
DOI:10.1109/BCI65088.2025.10931728