Decoding two-dimensional movement trajectories using electrocorticographic signals in humans

By Gerwin Schalk, J. Kubanek, Kai J. Miller, Nicholas R. Anderson, Eric C. Leuthardt, Jeffrey G. Ojemann, D. Limbrick, Daniel W. Moran, L. Gerhardt, Jonathan R. Wolpaw. 2007

569 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

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Citations in the corpus

Filtering with State-Observation Examples via Kernel Monte Carlo Filter (2013, methodology): “We begin by introducing positive-definite kernels and reproducing kernel Hilbert spaces, details of which can be found in Schölkopf and Smola (2002), Berlinet and ThomasAgnan (2004), and Steinwart and Christmann (2008).”
Brain Computer Interfaces, a Review (2012, methodology): “[70] presented a more advanced ECoG-based BCI which allowed the user to control a two-dimensional cursor.”
Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects (2009, background): “array of ECoG electrodes to localize critical cortical functions [16], [20], [34], [35].”
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, methodology): “For example, as illustrated in FIGURE 5A, large variety of brain signals, ranging from single units (114, 124, 466, 583, 852), to local field potentials (LFPs) (264, 670, 750), electrocorticography (ECoG) (483, 484, 697, 759, 837, 839, 856), electroencephalography (EEG) (7, 71, 141, 169, 358, 412, 6”
Two-dimensional movement control using electrocorticographic signals in humans (2008, result): “However, evidence in recent and ongoing studies [45–50] strongly suggests that this is not the case, that, in fact, ECoG supports decoding of multidimensional movement parameters with a fidelity that is comparable to that achieved previously only with microelectrodes implanted within cortex.”
Brain-Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives (2014, unknown): “Interestingly, similar counterparts of very low frequency oscillations in ECoG, referred to as local motor potentials, have also been found to show close correlation with hand and finger kinematics [49].”
Brain-machine interfaces in neurorehabilitation of stroke. (2015, unknown): “While low-frequency rhythms, such as SMR, showed limited correlation and contingency with intended movements (Schalk et al., 2007; Mehring et al., 2004; Stark and Abeles, 2007; Flint et al., 2012a), decoding of high gamma band and action potentials (single- or multi-unit) (Mehring et al.”
Signal quality of simultaneously recorded invasive and non-invasive EEG (2009, background): “emphasized is that it is less susceptible to artifact contamination (Ball et al., 2004; Canolty et al., 2007; Engel et al., 2005; Lachaux et al., 2003; Leuthardt et al., 2004; Schalk et al., 2007).”
Multiple Operator-valued Kernel Learning (2012, unknown): “More precisely, we are interested in finger movement prediction from electrocorticographic signals [27].”
Prior Knowledge Improves Decoding of Finger Flexion from Electrocorticographic Signals (2011, methodology): “E respectively) for subsequent analyses.”