Brain–computer interfaces as new brain output pathways

By Jonathan R. Wolpaw. Journal of Physiology, 2007

369 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/0a70b848f0cb7e0e796909c270c999590a06c9ec

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Single-trial analysis and classification of ERP components - A tutorial (2011, positive): “This branch of research is strongly influenced by the development of an effective communication interface connecting the human brain and a computer ([19, 33, 35, 76, 5, 52, 16, 77, 34]), which finally also attracted the machine learning community to the field [6, 69, 27, 47, 11, 50, 7, 8, 74, 49, 67”
Dynamics of Active Sensing and Perceptual Selection (2010, background): “Rhythm in motor routines used in Active Sensing Motor output is modulated by motor cortical oscillatory rhythms in the delta (1–3 Hz), theta (5–7 Hz), mu (8–12 Hz), and beta (13–30 Hz) bands [6–11], and in our view, the motor system’s imposition of these rhythms on sensory inflow is a critical facto”
Introduction to machine learning for brain imaging (2011, background): “…the ﬁ eld of real-time decoding for brain – computer interfacing (BCI) has been a technology motor (e.g. Dornhege et al., 2007; Kübler and Kotchoubey, 2007; Kübler and Müller, 2007; Wolpaw, 2007; Birbaumer, 2006; Pfurtscheller et al., 2005;CurranandStokes,2003;Wolpawetal.,2002;Kübleretal.,2001).”
Brain-Computer Interfaces: A Gentle Introduction (2009, methodology): “Inotherwords, the BCI should allow a combination of process-oriented (or low-level) control and goal-oriented (or high level) control [41, 42].”
Brain Computer Interfaces, a Review (2012, background): “Contrary to established opinion, Wolpaw [3] suggested that performance in multidimensional control may be independent of the recording method.”