FGANet: fNIRS-Guided Attention Network for Hybrid EEG-fNIRS Brain-Computer Interfaces

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 30; pp. 329 - 339
• Main Authors: Kwak, Youngchul, Song, Woo-Jin, Kim, Seong-Eun
• Format: Journal Article
• Language: English
• Published: United States IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Attention; Brain-computer interface (BCI); Brain-Computer Interfaces; Control equipment; Deep learning; EEG; Electrodes; Electroencephalography; electroencephalography (EEG); Electroencephalography - methods; Feature extraction; fNIRS-guided attention networks; Functional near-infrared spectroscopy; functional near-infrared spectroscopy (fNIRS); Human-computer interface; Humans; hybrid BCI; Hybrid systems; Infrared spectra; Infrared spectroscopy; Interfaces; Machine learning; Mathematical analysis; Mathematics; Medical imaging; Mental task performance; Near infrared radiation; Neural coding; Performance degradation; Performance enhancement; Task analysis; Tensors; Three-dimensional displays

Non-invasive brain-computer interfaces (BCIs) have been widely used for neural decoding, linking neural signals to control devices. Hybrid BCI systems using electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have received significant attention for overcoming the limitatio...