High-speed spelling with a noninvasive brain–computer interface

By Xiaogang Chen, Masaki Nakanishi, Xiaorong Gao, Tzyy-Ping Jung, Shangkai Gao. Proceedings of the National Academy of Sciences of the United States of America, 2015

879 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/e8dac4e0bd6a6b765319c96b9f2fc5b991f83e4a

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

High-performance brain-to-text communication via handwriting (2021, methodology): “Commonly used BCIs for restoring communication are either flashing electroencephalogram (EEG) spellers 18,28–32 or intracortical point-and-click BCIs 6,7,33 .”
Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis (2017, methodology): “This study used the same design of visual stimulator as the one used in our previous study for a direct comparison of target identification methods [2], however, the performance could be further improved by optimizing the parameter of stimuli for the TRCA-based method.”
Learning with Humans in the Loop (2020, background): “These waves are distinct and strong enough to enable users to spell out words purely by focusing on di↵erent areas of the screen [14].”
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, neutral): “These include spelling devices (10, 71, 101, 117, 132, 438, 559, 608, 619, 672, 876, 882), speech generators (190, 326, 549), BMIs for control of a humanoid robot (55, 103, 120, 138, 301), telepresence systems (130, 223, 804), BMI-controlled wheelchairs (121, LEBEDEV AND NICOLELIS”
EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications (2020, methodology): “For EEG-based BCIs, before applying features learned by the conventional approaches to different subjects, a training period of pilot data on each new subject is required due to inter-subject variability [65].”
Noninvasive neuroimaging enhances continuous neural tracking for robotic device control (2019, methodology): “Although achievable task complexity varies between invasive and noninvasive systems, BCIs in both domains have restored once-lost bodily functions that include independent ambulation (8), functional manipulations of the hands (3, 4), and linguistic communication (9, 10).”
Brain–Computer Interface Spellers: A Review (2018, methodology): “As a result of reﬁned classiﬁcation methods and user-speciﬁc calibration data, Chen et al. achieved average ITRs of 267 bits/min and accuracy of 89.76%, employing 40 SSVEP targets [34].”
EEG-Based Brain-Computer Interfaces (2017, background): “SSVEP-based BCIs can support high rates of information transfer [22].”
Quo vadis artificial intelligence? (2022, background): “In the laboratory environment, researchers have managed to achieve non-invasive implementation for writing and spelling [91, 92], prosthetic/exoskeleton control [93], and for general-purpose communication [94].”
Exploring the impact of artificial intelligence on teaching and learning in higher education (2017, background): “Providing affordable solutions to use brain computer interface (BCI) devices capable to measure when a student is fully focused on the content and learning tasks (Chen et al. 2015; González et al. 2015) is already possible, and super-computers, such as IBM’s Watson, can provide an auto-”
Progress in Brain Computer Interface: Challenges and Opportunities (2019, background): “Employing high density EEG mapping to increase spatial resolution results in high computational cost and efforts to maintain a reasonable signal-to-noise ratio across all channels [73].”
Transfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016 (2020, positive): “This paradigm is frequently used in BCI spellers [33], as it can achieve a very high information transfer rate.”
A comprehensive review of EEG-based brain–computer interface paradigms (2019, positive): “Chen et al [131] examined the correlation coefficients between stimulus frequency and subject ’ s EEG frequency using canonical correlation analysis (CCA).”