Visual and Auditory Brain–Computer Interfaces

By Shangkai Gao, Xiaorong Gao, Bo Hong. IEEE Transactions on Biomedical Engineering, 2014

403 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/b23992a1ca82789e684839775129b0fcead97c21

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Brain computer interfacing: Applications and challenges (2015, background): “Endogenous components vary in amplitude, latency, and scalp distribution with strategies, expectancies, and other mental activities triggered by the event [126].”
A comprehensive review of EEG-based brain–computer interface paradigms (2019, positive): “External stimulation can be visual [82, 83], auditory [83, 84], or somatosensory [85].”
Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis (2017, background): “BRAIN-COMPUTER interfaces (BCIs) based on steadystate visual evoked potentials (SSVEPs) have been attracting much attention due to high communication rates [1]–[3].”
High-speed spelling with a noninvasive brain–computer interface (2015, background): “Although Although communication speed of the EEG-based spellers has been significantly improved in the past decade (4), it still remains a key obstacle to real-life applications in humans.”
A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials (2015, methodology): “Various target identification methods have been developed for detecting SSVEPs in BCIs [2 – 4].”
Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain–computer interface (2015, methodology): “Fourth, in addition to the signal-processing methods, performance of the SSVEP BCI could also be improved by advanced stimulus coding methods (Hwang et al 2013, Gao et al 2014) and system paradigms such as hybrid BCIs (Wang et al 2015, Allison et al 2014, Pan et al 2014).”
A review of channel selection algorithms for EEG signal processing (2015, background): “Another important issue is to investigate channel selection techniques for emerging applications based on visual and auditory-evoked potential [77].”
A Benchmark Dataset for SSVEP-Based Brain–Computer Interfaces (2017, background): “BRAIN–computer interfaces (BCIs) provide humans a new communication channel based on information coding and decoding with brain activities [1]–[3].”