Brain-Computer Interfacing for Intelligent Systems

By Anton Nijholt, Desney S. Tan, Gert Pfurtscheller, Clemens Brunner, José del R. Millán, Brendan Z. Allison, Bernhard Graimann, F. Popescu, Benjamin Blankertz, K. Müller. IEEE Intelligent Systems, 2008

321 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/97fc5996f67fc09a114315adbf3829ac5676ef2a

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

The Hybrid BCI (2010, background): “In stressful situations, the baroreflex vagal component is suppressed and the HR increases (Nosaka et al., 1991).”
Could Anyone Use a BCI? (2010, background): “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).”
Turning Shortcomings into Challenges: Brain-Computer Interfaces for Games (2009, background): “Experiments that investigate the consequences of using less electrodes and using a ‘dry-cap’ are reported in [2].”
BCI Demographics: How Many (and What Kinds of) People Can Use an SSVEP BCI? (2010, background): “Intersubject variability often leads to the well-documented “BCI illiteracy” phenomenon; across different BCI approaches (SSVEP, P300, SMR1), about 10%–25% of users are unable to attain effective control [8], [12]–[18].”
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].”
Silent speech interfaces (2010, background): “brain activity, which is then transformed into a control signal for the device (Nijholt et al., 2008).”
An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method (2009, methodology): “The most commonly used signals in EEG-based BCI systems are event-related synchronization of mu and beta bands, event-related potentials and steady-state visual evoked potential (SSVEP) [1–3].”
Pay attention!: designing adaptive agents that monitor and improve user engagement (2012, background): “Traditional BCI systems utilize machine learning algorithms, which must be presented with large amounts of data in order to learn individual EEG patterns to create statistical models of user behavior [35].”
Toward a hybrid brain–computer interface based on imagined movement and visual attention (2010, methodology): “A system that combines a BCI with another input mechanism could be called a ‘mixed’ hybrid BCI [22, 52].”