An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate

By Reinhold Scherer, Gernot R. Müller-Putz, Christa Neuper, Bernhard Graimann, Gert Pfurtscheller. IEEE Transactions on Biomedical Engineering, 2004

360 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/0ae2546e9a8e124885f58aca7413d5e63f069ffd

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In Cellular and Molecular Neuroscience

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In Human-Computer Interaction

Citations in the corpus

Toward a hybrid brain–computer interface based on imagined movement and visual attention (2010, background): “For example, BCIs based on imagined movement have increased the dimensionality of control by incorporating additional signal features and / or imagination of additional body parts [49, 53, 54].”
Control of an Electrical Prosthesis With an SSVEP-Based BCI (2008, background): “However, to control various single movements, more classes should be provided by the BCI resulting in longer training periods and/or a decrease of classification accuracy [6], [10].”
Event-related (De)synchronization (ERD/ERS) during motor imagery tasks: Implications for brain–computer interfaces (2011, background): “…who have lost all voluntary muscle control, but are cognitively intact, by allowing them to write sentences (Birbaumer, 1997; Donchin et al., 2000; Scherer et al., 2004; Nijboer et al., 2008; Kübler and Birbaumer, 2008), move a cursor on the computer screen (Wolpaw et al., 2002), play an…”
Motor imagery and action observation: modulation of sensorimotor brain rhythms during mental control of a brain-computer interface. (2009, background): “All rights reserved. characters or icons for communication purposes (Birbaumer et al., 1999; Kübler et al., 2001; Neuper et al., 2006; Scherer et al., 2004), or to acquire control of a neuroprosthesis to restore grasping (Müller-Putz et al., 2005; Neuper et al., 2006).”
Could Anyone Use a BCI? (2010, background): “For example, foot movement might seem more natural if the goal is to walk forward (Leeb et al. 2007; Scherer et al. 2008 )o r control vertically scrolling letters ( Scherer et al. 2004 ).”
Brain Computer Interfaces, a Review (2012, methodology): “LDA provides relatively acceptable accuracy and has been used successfully in numerous BCI systems, such as P300 speller [179], multiclass [230], or synchronous [231] BCIs.”
Toward Brain-Computer Interfacing (2009, methodology): “of two dynamic processes (Taylor et al. (2002); Wolpaw et al.”
A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals (2007, methodology): “…reduction method Reference ID Genetic algorithm (GA) (Flotzinger et al 1994, Garrett et al 2003, Graimann et al 2003a, 2004, Peterson et al 2005, Scherer et al 2004, Schroder et al 2003, Tavakolian et al 2004, Yom-Tov and Inbar 2001, 2002) Principal component analysis (PCA) (Anderson et al…”
Recognizing the Degree of Human Attention Using EEG Signals from Mobile Sensors (2013, methodology): “As published in Neuroscience Letters , Scherer et al. developed a system [5] for classifying EEG signals using Fisher’s linear discriminant analysis (FLDA) and a virtual keyboard for spelling.”
Brain–computer interface systems: progress and prospects (2007, background): “For example, some BCI systems allow users to voluntarily imagine or perform mental tasks or movements at their own pace [18,77,92,135] .”
Self-Paced (Asynchronous) BCI Control of a Wheelchair in Virtual Environments: A Case Study with a Tetraplegic (2007, background): “Asynchronous BCIs are much more complicate than synchronous ones, nevertheless, the community is more and more addressing these problems [17–21].”