Classification of EEG event-related potentials based on channel attention mechanism

Event-related potentials (ERPs) represent the electroencephalographic responses to specific stimuli and are crucial for analyzing and understanding the processing of conscious activities within the human brain. Their classification is of significant importance in psychology and cognitive science. To...

Full description

Saved in:
Bibliographic Details
Published in:The Journal of supercomputing 2025, Vol.81 (1), Article 126
Main Authors: Tang, Yiou, Ma, Yan, Xiao, Chunling, Wu, Min, Zeng, Guoyuan
Format: Article
Language:English
Subjects:
Compilers
Computer Science
Electroencephalography
Interpreters
Modules
Parameter modification
Processor Architectures
Programming Languages
Signal classification
Signal to noise ratio
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Event-related potentials (ERPs) represent the electroencephalographic responses to specific stimuli and are crucial for analyzing and understanding the processing of conscious activities within the human brain. Their classification is of significant importance in psychology and cognitive science. To address the multichannel and high signal-to-noise ratio characteristics of EEG signals, we introduce a single-subject short-distance ERP superposition averaging method for preprocessing raw data and propose an ERP-Xception model that integrates an ECA module with depth-separable convolutions. The ECA module was modified to reduce potential information loss through hierarchical dimensionality reduction, effectively extracting channel weight information. The Xception architecture was optimized to minimize model parameters and inference time. Additionally, a feature panning module was incorporated in parallel, allowing for minor channel displacements to enhance model generalizability and robustness. Our model achieved the highest F1-scores of 74.7%, 84.5%, 81.2%, 50.6%, 93.5%, and 88.5% across six ERP datasets, including ERN, LRP, N2PC, N170, N400, and P3, thereby validating its effectiveness and transferability.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-024-06627-3