A Transformer-Based Approach Combining Deep Learning Network and Spatial-Temporal Information for Raw EEG Classification

Xie, Jin; Zhang, J. X.; Sun, Jiayao; Ma, Zheng; Qin, Liuni; Li, Guanglin; Zhou, Huihui; Zhan, Yang

doi:10.1109/tnsre.2022.3194600

articleIEEE Transactions on Neural Systems and Rehabilitation EngineeringJan 1, 2022DIAMOND OA

A Transformer-Based Approach Combining Deep Learning Network and Spatial-Temporal Information for Raw EEG Classification

JXJin Xie JXJ. X. Zhang JSJiayao Sun ZMZheng Ma LQLiuni Qin

HKUST Shenzhen Research Institute · Shenzhen Institutes of Advanced Technology · +2 more institutions

PubMed

Indexed incrossrefdoajpubmed

Abstract

The attention mechanism of the Transformer has the advantage of extracting feature correlation in the long-sequence data and visualizing the model. As time-series data, the spatial and temporal dependencies of the EEG signals between the time points and the different channels contain important information for accurate classification. So far, Transformer-based approaches have not been widely explored in motor-imagery EEG classification and visualization, especially lacking general models based on cross-individual validation. Taking advantage of the Transformer model and the spatial-temporal characteristics of the EEG signals, we designed Transformer-based models for classifications of motor imagery EEG based on…

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Topics

Keywords

Computer science
Artificial intelligence
Transformer
Deep learning
Electroencephalography
Pattern recognition (psychology)
Machine learning
Speech recognition

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