An improved P300-based brain-computer interface

By Hilit Serby, Elad Yom-Tov, Gideon F. Inbar. IEEE transactions on neural systems and rehabilitation engineering, 2005

425 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/4a7725fde413ca215a52c089edf47c212589746b

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Citations in the corpus

A P300 event-related potential brain-computer interface (BCI): the effects of matrix size and inter stimulus interval on performance. (2006, methodology): “Several different features of scalp recorded EEG signals are being used as control signals for brain–computer interface (BCI) applications; most notably, event-related potentials (Donchin et al., 2000; Farwell and Donchin, 1988; Serby et al., 2005 ), spontaneous sensory motor rhythms (Wolpaw and Mc”
EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications (2020, neutral): “[27].”
A novel P300-based brain-computer interface stimulus presentation paradigm: moving beyond rows and columns (2010, methodology): “Others have used dynamic classifiers that stop when a classification criterion is met (e.g., Lenhardt et al., 2008; Serby et al., 2005); however, on average, the dynamic classifiers do not achieve online classification accuracy or practical bit rates as high as those reported here.”
Toward enhanced P300 speller performance. (2008, positive): “, 2006), and matched filtering (Serby et al., 2005) have been implemented, improving the performance beyond that originally reported in (Farwell and Donchin, 1988).”
Brain–computer interface systems: progress and prospects (2007, methodology): “Feature extraction may use relatively simple approaches, such as autoregressive frequency analysis [29] , or more complex techniques, such as independent component analysis (ICA) [59,85,86] .”
A New Auditory Multi-Class Brain-Computer Interface Paradigm: Spatial Hearing as an Informative Cue (2010, background): “, ITR values of 12.0 bits/minute (or 10.68 bits/minute according to equation 2) were reported. For a comparison of ITR of several BCI systems, see”
A P300-based brain-computer interface for people with amyotrophic lateral sclerosis. (2008, background): “…adults (5.5 bits/min; Donchin et al., 2000).1 Several previous P300-based BCI studies have presented bit rate with the time between characters selections removed (e.g., Donchin et al., 2000; Kaper et al., 2004; Meinicke et al., 2002; Serby et al., 2005); therefore, we have presented it both ways.”
A comparison of classification techniques for the P300 Speller (2006, methodology): “Several classification techniques have demonstrated notable performance for the P300 Speller, including stepwise linear discriminant analysis [2, 5], support vector machines [8, 10, 11], wavelets [1] and matched filtering [14].”
A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals (2007, methodology): “…component analysis (ICA) (Bayliss and Ballard 1999, 2000a, 2000b, Erfanian and Erfani 2004, Gao et al 2004, Peterson et al 2005, Wu et al 2004, Serby et al 2005, Xu et al 2004a, Wang et al 2004c, Li et al 2004a) Common spatial patterns (CSP) (Blanchard and Blankertz 2004, Dornhege et al 2003,…”
Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces (2011, neutral): “approach [37].”
Estimating workload using EEG spectral power and ERPs in the n-back task (2012, methodology): “Besides classification accuracy, we calculated bitrate (as described in Serby et al (2005)) as a measure of performance that represents the amount of information being communicated, taking into account the number of possible options, the classification accuracy and the time required to communicate…”
How many people are able to control a P300-based brain-computer interface (BCI)? (2009, neutral): “[15] achieved mean online accuracy of 79.”