IFNet: An Interactive Frequency Convolutional Neural Network for Enhancing Motor Imagery Decoding From EEG

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 31; pp. 1900 - 1911
• Main Authors: Wang, Jiaheng, Yao, Lin, Wang, Yueming
• Format: Journal Article
• Language: English
• Published: United States IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Brain; Brain modeling; Brain-Computer Interfaces; Brain–computer interface; Classification; Computer applications; Convolution; Convolutional neural networks; Coupling; cross-frequency interactions; data augmentation; Datasets; Decision analysis; Decoding; EEG; Electroencephalography; Feature extraction; Frequencies; Human-computer interface; Humans; Image enhancement; Imagination - physiology; Implants; Mental task performance; motor imagery; Neural networks; Neural Networks, Computer; Sensitivity analysis; Task analysis

Objective: The key principle of motor imagery (MI) decoding for electroencephalogram (EEG)-based Brain-Computer Interface (BCI) is to extract task-discriminative features from spectral, spatial, and temporal domains jointly and efficiently, whereas limited, noisy, and non-stationary EEG samples chal...