Electrical activity in the part of the brain controlling voluntary movement
One second sample of an EEG alpha oscillations . This rhythm occurs at frequencies similar to the mu rhythm, although alpha oscillations are detected over a different part of the brain.The left motor cortex, or BA4, is highlighted in green on this left lateral view of the brain. This is the area over which mu rhythms are detected bilaterally.
The sensorimotor mu rhythm, also known as mu wave, comb or wicket rhythms or arciform rhythms, are synchronized patterns of electrical activity involving large numbers of neurons, probably of the pyramidal type, in the part of the brain that controls voluntary movement.[1] These patterns as measured by electroencephalography (EEG), magnetoencephalography (MEG), or electrocorticography (ECoG), repeat at a frequency of 7.5–12.5 (and primarily 9–11) Hz, and are most prominent when the body is physically at rest.[1] Unlike the alpha wave, which occurs at a similar frequency over the resting visual cortex at the back of the scalp, the mu rhythm is found over the motor cortex, in a band approximately from ear to ear. People suppress mu rhythms when they perform motor actions or, with practice, when they visualize performing motor actions. This suppression is called desynchronization of the wave because EEG wave forms are caused by large numbers of neurons firing in synchrony. The mu rhythm is even suppressed when one observes another person performing a motor action or an abstract motion with biological characteristics. Researchers such as V. S. Ramachandran and colleagues have suggested that this is a sign that the mirror neuron system is involved in mu rhythm suppression,[2][3] although others disagree.[4]
The mu rhythm is of interest to a variety of scholars. Scientists who study neural development are interested in the details of the development of the mu rhythm in infancy and childhood and its role in learning.[5] Since a group of researchers believe that autism spectrum disorder (ASD) is strongly influenced by an altered mirror neuron system[2][6][7] and that mu rhythm suppression is a downstream indication of mirror neuron activity,[3] many of these scientists have kindled a more popular interest in investigating the mu wave in people with ASD. Assorted investigators are also in the process of using mu rhythms to develop a new technology: the brain–computer interface (BCI). With the emergence of BCI systems, clinicians hope to give the severely physically disabled population new methods of communication and a means to manipulate and navigate their environments.[8]
^ abAmzica, Florin; Fernando Lopes da Silva (2010). "Cellular Substrates of Brain Rhythms". In Schomer, Donald L.; Fernando Lopes da Silva (eds.). Niedermeyer's Electroencephalography: Basic Principles, Clinical Applications, and Related Fields (6th ed.). Philadelphia, Pa.: Lippincott Williams & Wilkins. pp. 33–63. ISBN 978-0-7817-8942-4.
^ abOberman, Lindsay M.; Edward M. Hubbarda; Eric L. Altschulera; Vilayanur S. Ramachandran; Jaime A. Pineda (July 2005). "EEG evidence for mirror neuron dysfunction in autism spectrum disorders". Cognitive Brain Research. 24 (2): 190–198. doi:10.1016/j.cogbrainres.2005.01.014. PMID 15993757.
^ abCite error: The named reference Pineda3 was invoked but never defined (see the help page).
^Churchland, Patricia (2011). Braintrust: What Neuroscience Tells Us About Morality. Princeton, NJ: Princeton University Press. p. 156. ISBN 978-0-691-13703-2.
^Nyström, Pär; Ljunghammar, Therese; Rosander, Kerstin; Von Hofsten, Claes (2011). "Using mu rhythm desynchronization to measure mirror neuron activity in infants". Developmental Science. 14 (2): 327–335. doi:10.1111/j.1467-7687.2010.00979.x. PMID 22213903.
^Bernier, R.; Dawson, G.; Webb, S.; Murias, M. (2007). "EEG mu rhythm and imitation impairments in individuals with autism spectrum disorder". Brain and Cognition. 64 (3): 228–237. doi:10.1016/j.bandc.2007.03.004. PMC 2709976. PMID 17451856.
^Cite error: The named reference Williams was invoked but never defined (see the help page).
^Pfurtscheller, Gert; Christa Neuper (2010). "EEG-Based Brain–Computer Interfaces". In Schomer, Donald L.; Fernando H. Lopes da Silva (eds.). Niedermeyer's Electroencephalography: Basic Principles, Clinical Applications, and Related Fields (6th ed.). Philadelphia, Pa.: Lippincott Williams & Wilkins. pp. 1227–1236. ISBN 978-0-7817-8942-4.
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