Brain–computer interface systems: progress and prospects

By Brendan Z. Allison, E. Wolpaw, Jonathan R. Wolpaw. Expert Review of Medical Devices, 2007

428 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/e659e72b7962c9ad586a2d35a7099550102fb054

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Could Anyone Use a BCI? (2010, methodology): “This progress and enthusiasm is reflected in the dramatic increase in peer reviewed publications, conference presentations and symposia, and media attention (Pfurtscheller et al. 2006, 2008; Allison et al. 2007; Nijholt et al. 2008).”
EEG-Based Brain-Controlled Mobile Robots: A Survey (2013, background): “More details of classiﬁers, applications, and issues regarding BCI systems can be seen in several reviews of BCI [3], [4], [66], [90]–[94], [137].”
Towards an independent brain-computer interface using steady state visual evoked potentials. (2008, methodology): “…of BCI systems have been described in the literature and typically are categorized according to the cognitive and neural activity needed for control (for review, see Kü bler et al., 2001; Wolpaw et al., 2002; Allison, 2003; Kübler and Neumann, 2005; Jackson et al., 2006; Allison et al., 2007).”
BCI Demographics: How Many (and What Kinds of) People Can Use an SSVEP BCI? (2010, background): “About 70% of subjects 1BCIs based on sensorimotor rhythms (SMRs) have also been called mu BCIs and ERD/ERS BCIs [2], [5].”
Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces (2011, background): “Such systems allow people to communicate through direct measurements of brain activity, without requiring any movement [1], [2], [3].”
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, background): “clinical practice by Niels Birbaumer (71), have undergone a considerable expansion (14, 74, 246, 358, 815, 863).”
The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology (2010, background): “…(BCI), i.e., the ability to transfer and use information from distinct brain states for communicating with a machine has in the past years received considerable attention (Wolpaw et al., 2002; Birbaumer, 2006; Allison et al., 2007; Dornhege et al., 2007a; Schalk, 2008; Krusienski and Wolpaw, 2009).”
Toward a hybrid brain–computer interface based on imagined movement and visual attention (2010, methodology): “A hybrid BCI that combines two different BCI approaches, such as the system suggested in [10, 49] and validated in this work, could be called a ‘pure’ hybrid BCI.”
Combining Brain–Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges (2010, background): “Known as Brain-Computer Interfaces (BCI), the ﬁeld has already seen several early prototypes [136, 115, 207, 202, 42, 2].”
Time Domain Parameters as a feature for EEG-based Brain-Computer Interfaces (2009, neutral): “(2007); Allison et al. (2007); Birbaumer et al. (2006); Pfurtscheller et al. (2005); Wolpaw et al. (2002); Kübler et al. (2001) for an overview, but only SMR-based BCIs are considered in this paper.”
The Hybrid BCI (2010, background): “, 2008a), but BCIs are quickly becoming useful to healthy people too (Allison et al., 2007; Nijholt et al., 2008).”
Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data (2016, positive): “These applications use classifiers to predict brain states to operate computers or robots (Müller et al., 2008; Allison, Wolpaw, & Wolpaw, 2007; Hill et al., 2006; Müller, Anderson, & Birch, 2003; Vidal, 1973, p. 2008).”