The mental prosthesis: assessing the speed of a P300-based brain-computer interface.

By Emanuel Donchin, K. Spencer, R. Wijesinghe. IEEE transactions on rehabilitation engineering, 2000

1,298 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f679635e7b270338c405f0be9b3165932b846abe

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In Cognitive Neuroscience

Citations in the corpus

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.”
EEG-Based Brain-Controlled Mobile Robots: A Survey (2013, background): “…ms after the random occurrence of a desired target stimulus from nontarget stimuli (the stimuli can be in visual, auditory, or tactile modality) [7], [60], [129]; 2) SSVEP, which are visually evoked by a stimulus modulated at a ﬁxed frequency and occur as an increase in EEG activity at the…”
Brain-computer interface technology: a review of the Second International Meeting. (2003, methodology): “The case used by Donchin as an illustration is the P300-based speller, [30], [31], which relies on the fact that events that force “context updating” in the so-called “oddball paradigm” [32], [33] elicit the P300 component of the ERP.”
BCI competition 2003-data sets Ib and IIb: feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram (2004, methodology): “The -CWT method was applied in a P300 speller paradigm [7], [18].”
The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials (2004, methodology): “, the 2 out of 12 stimuli that did contain the desired character) are different from those evoked by the stimuli that did not contain the desired character and they are similar to the P300 responses previously reported [21], [22].”
Event-related potentials in clinical research: guidelines for eliciting, recording, and quantifying mismatch negativity, P300, and N400. (2009, background): “P300 may also serve as a mediating response, allowing brain control of prosthetic devices (Donchin et al., 2000; Sellers and Donchin, 2006; Nijboer et al., 2008).”
Toward enhanced P300 speller performance. (2008, neutral): “(Donchin et al., 2000; Sellers and Donchin, 2006), wavelets (Bostanov, 2004), support vector machines (Kaper et al.”
Combining Brain–Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges (2010, background): “The most prominent is the approach that elicits a P300 component [41].”
An improved P300-based brain-computer interface (2005, methodology): “The offline results in this study show a slight improvement compared to the offline system described in [6].”
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].”
Flashing characters with famous faces improves ERP-based brain–computer interface performance (2011, background): “Speed is basically increased by decreasing ISIs (Sellers et al 2006a, 2006b) and decreasing the number of stimulus sequences used for averaging (Donchin et al 2000, Halder et al 2010, Kleih et al 2010).”
A comprehensive review of EEG-based brain–computer interface paradigms (2019, neutral): “Additionally, P300 shares very similar inter-subject characteristics which help to diminish the subjects ’ training time [94].”
Brain-computer interface technology: a review of the first international meeting. (2000, background): “With the exception of systems that depend on muscle control [2], [10], [25], and the possible exception of the P300 BCI [4], current BCI’s depend on EEG or single-unit control that is acquired through operant conditioning procedures.”
A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals (2007, methodology): “…2003, Schroder et al 2003) P300 Cross-correlation (Bayliss and Ballard 1999, 2000a, 2000b, Farwell and Donchin 1988) Stepwise discriminant analysis (Donchin et al 2000, Farwell and Donchin 1988) Matched filtering (Serby et al 2005) PPM (Jansen et al 2004) TFR method (Bostanov 2004, Donchin et al…”
BCI competition 2003-data set IIb: enhancing P300 wave detection using ICA-based subspace projections for BCI applications (2004, methodology): “Farwell and Donchin introduced some P300 detection methods for BCI such as stepwise discriminant analysis (SWDA), peak picking, area, and covariance in 1988 [2], and Donchin added discrete wavelet transform (DWT) to the SWDA [4].”