Toward a hybrid brain–computer interface based on imagined movement and visual attention

By Brendan Z. Allison, Clemens Brunner, Vera Kaiser, Gernot R. Müller-Putz, Christa Neuper, Gert Pfurtscheller. Journal of Neural Engineering, 2010

302 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4ec3e345a04c0d75a19cc67e00dc543c677b7bfb

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Citations in the corpus

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 ].”
P300 brain computer interface: current challenges and emerging trends (2012, methodology): “Combining a P300 BCI with an SSVEP BCI was ﬁrst described two years ago (Allison et al., 2010).”
Combining Brain–Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges (2010, background): “A second example is the combination of MI with steady state visual evoked potentials (SSVEPs) explored in some offline studies (Allison et al., 2010; Brunner et al., 2010). Other hBCIs combine brain and other biosignals. For instance, Scherer et al. (2007b) combined a standard SSVEP BCI with an on/o”
Progress in Brain Computer Interface: Challenges and Opportunities (2019, background): “Invasive signal acquisitions are yet to be tested for long-term reliability for assessing whether the placed sensors could maintain the signal quality over a long period inside the brain [70].”
EEG-Based Brain-Controlled Mobile Robots: A Survey (2013, methodology): “Moreover, all the subjects who could not generate the ERD or SSVEP patterns could likely use a “hybrid” BCI that combines the two approaches to improve accuracy [39], [114].”
Brain-Computer Interfaces: A Gentle Introduction (2009, background): “Second, we are optimistic about a new technology called a “hybrid” system, which is composed of 2 BCIs or at least one BCI and another system [48–50].”
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, methodology): “Such BMIs combine several decoding principles, for example, motor imagery combined with SSVEPs (13, 100) or steady-state somatosensory evoked potentials (7), functional near-infrared spectroscopy (fNIRS) with asynchronous sensorimotor rhythms, P300 with SSVEP (154), and eye position with EEG decodin”
A Hybrid BCI System Combining P300 and SSVEP and Its Application to Wheelchair Control (2013, background): “demonstrated that by detecting MI and SSVEP simultaneously, classification accuracy can be improved, especially for BCI-blind subjects [19].”
Could Anyone Use a BCI? (2010, methodology): “We used a threshold of 70% in two recent articles involving tasks that simulated a two choice BCI ( Allison et al. 2010b; Brunner et al. 2010).”
Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior (2019, background): “Such pervasive andmarked individual differences present a challenge for the use of BCIs clinically [14].”