Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks

By Gert Pfurtscheller, Clemens Brunner, Alois Schlögl, Fernando Henrique Lopes da Silva. NeuroImage, 2006

1,629 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9af5711c0645997d1884602190a142ef183961e6

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In Cognitive Neuroscience

Citations in the corpus

The Berlin Brain-Computer Interface: Accurate performance from first-session in BCI-naive subjects (2008, background): ““surround inhibition” enhancing focal cortical activation [39], [40].”
Optimizing Spatial filters for Robust EEG Single-Trial Analysis (2008, background): “It can either originate from an ERD during right hand imagery, or from an ERS during foot imagery (hand areas are more relaxed if concentration focuses on the foot, therefore the idle rhythm may increase; lateral inhibition [36], [43]).”
Brain–Computer Communication: Motivation, Aim, and Impact of Exploring a Virtual Apartment (2007, background): “As expected from literature [22] oscillatio ns in the alpha and beta range of the EEG over the sensorimotor areas contribute substantially to the discrimination betw e n”
ERD/ERS patterns reflecting sensorimotor activation and deactivation. (2006, background): “…movement preparation and execution, desynchronization of alpha band (mu) activity at a specific cortical location may be accompanied by an increase of synchronization (ERS) in the 10-Hz band over areas not engaged in the task (Pfurtscheller and Neuper, 1994; Pfurtscheller et al., 1996b, 2006).”
Brain-Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives (2014, neutral): “that classifies the mental states of various types of motor imageries [41].”
Brain Computer Interfaces, a Review (2012, background): “In contrast to alpha rhythms, mu rhythms are strongly connected to motor activities and, in some cases, appear to correlate with beta rhythms [31,35].”
Multiclass Brain–Computer Interface Classification by Riemannian Geometry (2012, background): “This phenomenon is due to physiological reasons, as it was discussed in [19].”
Spectral Changes in Cortical Surface Potentials during Motor Movement (2007, background): “The classic, although still debated (Nunez et al., 2001), inter pretation of changes in spectral amplitude, developed by Pfurtscheller and others (Pfurtscheller, 1981, 2000, 2001; Pfurtscheller et al., 2003, 2006), is that they reflect phase coherence of cortical circuits.”
Brain-machine interfaces in neurorehabilitation of stroke. (2015, neutral): “…been tested: 1. sensori-motor rhythms (SMR, 8–15 Hz, also termed rolandic alpha or mu-rhythm depending on the context) (McFarland et al., 1993, 2006; Pfurtscheller et al., 2006; Soekadar et al., 2011a, in press-a), 2. slow cortical potentials (SCP) (Birbaumer et al., 1999), 3. event-related…”
EEG Source Imaging Enhances the Decoding of Complex Right Hand Motor Imagery Tasks (2015, background): “The alpha/mu band ERD has long been shown to be indicative of gross limb activity [17], [40], [41].”
Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface (2007, methodology): “Pfurtscheller et al. (1998, 2000, 2006) reported a direct EEG-based BCI using motor imagery.”
The non-invasive Berlin Brain-Computer Interface: Fast acquisition of effective performance in untrained subjects (2007, methodology): “Therefore it is likely that the BCI failure in this subject was not related to an unfavorable mental strategy (e.g., visual instead of kinesthetic; see Neuper et al., 2005), but rather that the idiosyncratic properties of the sensorimotor brain rhythms (or what is discernible of them in scalp EEG) i”
Brain-machine interfaces: past, present and future. (2006, background): “24 Pfurtscheller, G. et al. (2006) Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks.”
Seperability of four-class motor imagery data using independent components analysis (2006, background): “The components chosen to depict tongue imagery contained dominant ERS activity, whereas for foot imagery both ERD and ERS patterns were more significant (Pfurtscheller et al 2006).”