Mu and Beta Rhythm Topographies During Motor Imagery and Actual Movements

By Dennis J. McFarland, Laurie A. Miner, Theresa M. Vaughan, Jonathan R. Wolpaw. Brain Topography, 2004

903 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/c3bdebe54f2a78c5f1e98b65525d8f45623cad48

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In Biomedical Engineering

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

The functional significance of mu rhythms: translating "seeing" and "hearing" into "doing". (2005, background): “Such learning has been mapped to the control of a variety of applications, from moving a cursor, spelling words, answering questions, selecting among choices on a computer screen, to activating a device [87,88,113,148–150].”
Graph analysis of functional brain networks: practical issues in translational neuroscience (2014, background): “Finally, brain nodes are typically assigned to sensors or sources, but they can also be assigned to ICA components [51].”
Two-dimensional movement control using electrocorticographic signals in humans (2008, result): “This is consistent with our previous ECoG studies [18, 33] and also with corresponding EEG studies [34].”
An MEG-based brain-computer interface (BCI) (2007, background): “μ rhythm amplitude is decreased during planning, execution or mere imagination of limb movements (desynchronization of the μ rhythm) (Gastaut, 1952; Pfurtscheller, 1999; McFarland et al., 2000).”
The ups and downs of β oscillations in sensorimotor cortex. (2013, positive): “It can be elicited, although with reduced amplitude, during motor imagery (McFarland et al., 2000; Nakagawa et al., 2011), movement observation (Babiloni et al., 2002; Koelewijn et al., 2008), passive movement and kinesthetic illusion (Keinrath et al., 2006).”
Turning Shortcomings into Challenges: Brain-Computer Interfaces for Games (2009, result): “As reported in other literature [35], actual movement elicited stronger signals and is better recognizable than imagined movement.”
Towards adaptive classification for BCI (2006, methodology): “This is done by a statistical analysis of a calibration measurement in which the subject performs well-defined mental acts such as imagined movements [19, 27].”
EEG feature extraction based on wavelet packet decomposition for brain computer interface (2008, background): “(2) Autoregressive (AR) model: From the AR spectrum, band power is calculated in several frequency bands and the power sum is used as independent variables [16].”
Brain-computer interfaces for communication and control. (2002, methodology): “When a single signal feature is used to select among more than two choices, the slope also affects the relative accessibility of the choices (e.g. McFarland et al., 1999, 2000b ).”
BCI2000: a general-purpose brain-computer interface (BCI) system (2004, methodology): “To date, we have implemented seven different user applications: four cursor movement applications (see [33]–[36]), an application for evaluating prospective users [37], an application that can present user-selectable auditory and visual stimuli, and a spelling application based on Fig.”
Review of the BCI Competition IV (2012, background): “Asymmetry in the amplitude of the rhythms Typically, one can observe some asymmetry in the strength of the desynchronization in each of the hemispheres (McFarland et al., 2000; Mazaheri and Jensen, 2008; Nikulin et al., 2010).”
Neural signatures of competition between voluntary and involuntary influences over the focus of attention in visual working memory (2023, background): “The attenuation of beta activity contralateral to the response hand is a well-established neural marker of manual action planning (e.g., van Wijk, Daffertshofer, Roach, & Praamstra, 2009; Baker, 2007; Neuper et al., 2006; Mcfarland, Miner, Vaughan, & Wolpaw, 2000; Salmelin & Hari, 1994).”