Mu waves, also known as mu rhythms, comb or wicket rhythms, arciform rhythms, or sensorimotor 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. 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. Unlike the alpha wave, which occurs at a similar frequency over the resting visual cortex at the back of the scalp, the mu wave is found over the motor cortex, in a band approximately from ear to ear. A person suppresses mu wave patterns when he or she performs a motor action or, with practice, when he or she visualizes performing a motor action. This suppression is called desynchronization of the wave because EEG wave forms are caused by large numbers of neurons firing in synchrony. The mu wave 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 wave suppression, although others disagree.
The mu wave is of interest to a variety of scholars. Scientists who study neural development are interested in the details of the development of the mu wave in infancy and childhood and its role in learning. Since a group of researchers believe that autism spectrum disorder (ASD) is strongly influenced by an altered mirror neuron system and that mu wave suppression is a downstream indication of mirror neuron activity, 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 waves 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.
The mirror neuron system consists of a class of neurons that was first studied in the 1990s in macaque monkeys. Studies have found sets of neurons that fire when these monkeys perform simple tasks and also when the monkeys view others performing the same simple tasks. This suggests they play a role in mapping others' movements into the brain without actually physically performing the movements. These sets of neurons are called mirror neurons and together make up the mirror neuron system. Mu waves are suppressed when these neurons fire, a phenomenon which allows researchers to study mirror neuron activity in humans. There is evidence that mirror neurons exist in humans as well as in non-human animals. The right fusiform gyrus, left inferior parietal lobule, right anterior parietal cortex, and left inferior frontal gyrus are of particular interest. Some researchers believe that mu wave suppression can be a consequence of mirror neuron activity throughout the brain, and represents a higher-level integrative processing of mirror neuron activity. Tests in both monkeys (using invasive measuring techniques) and humans (using EEG and fMRI) have found that these mirror neurons not only fire during basic motor tasks, but also have components that deal with intention. There is evidence of an important role for mirror neurons in humans, and mu waves may represent a high level coordination of those mirror neurons.
A fruitful conceptualization of mu waves in pediatric use that is independent of their frequency is that mu wave suppression is a representation of activity going on in the world, and is detectable in the frontal and parietal networks. A resting oscillation becomes suppressed during the observation of sensory information such as sounds or sights, usually within the frontoparietal (motor) cortical region. Measured in this way, the mu wave is detectable during infancy as early as four to six months, when the peak frequency the wave reaches can be as low as 5.4 Hz. There is a rapid increase in peak frequency in the first year of life, and by age two frequency typically reaches 7.5 Hz. The peak frequency of the mu wave increases with age until maturation into adulthood, when it reaches its final and stable frequency of 8–13 Hz. These varying frequencies are measured as activity around the central sulcus, within the Rolandic cortex.
Mu waves are thought to be indicative of an infant’s developing ability to imitate. This is important because the ability to imitate plays a vital role in the development of motor skills, tool use, and understanding causal information through social interaction. Mimicking is integral in the development of social skills and understanding nonverbal cues. Causal relationships can be made through social learning without requiring experience firsthand. In action execution, mu waves are present in both infants and adults before and after the execution of a motor task and its accompanying desynchronization. While executing a goal-oriented action, however, infants exhibit a higher degree of desynchronization than do adults. Just as with an action execution, during action observation infants’ mu waves not only show a desynchronization, but show a desynchronization greater in degree than the one evidenced in adults. This tendency for changes in degree of desynchronization, rather than actual changes in frequency, becomes the measure for mu wave development throughout adulthood, although the most changes take place during the first year of life. Understanding the mechanisms that are shared between action perception and execution in the earliest years of life has implications for language development. Learning and understanding through social interaction comes from imitating movements as well as vowel sounds. Sharing the experience of attending to an object or event with another person can be a powerful force in the development of language.
Autism is a disorder that is associated with social and communicative deficits. A single cause of autism has yet to be identified, but the mu wave and mirror neuron system have been studied specifically for their role in the disorder. In a typically developing individual, the mirror neuron system responds when he or she either watches someone perform a task or performs the task him- or herself. In individuals with autism, mirror neurons become active (and consequently mu waves are suppressed) only when the individual performs the task him- or herself. This finding has led some scientists, notably V. S. Ramachandran and colleagues, to view autism as disordered understanding of other individuals' intentions and goals thanks to problems with the mirror neuron system. This deficiency would explain the difficulty people with autism have in communicating with and understanding others. While most studies of the mirror neuron system and mu waves in people with autism have focused on simple motor tasks, some scientists speculate that these tests can be expanded to show that problems with the mirror neuron system underlie overarching cognitive and social deficits.
Based on findings correlating mirror neuron activity and mu wave suppression in individuals with autism as in typically developing individuals, studies have examined both the development of mirror neurons and therapeutic means for stimulating the system. A recent study has found that fMRI activation magnitudes in the inferior frontal gyrus increase with age in people with autism. This finding was not apparent in typically developing individuals. Furthermore, greater activation was associated with greater amounts of eye contact and better social functioning. Scientists believe the inferior frontal gyrus is one of the main neural correlates with the mirror neuron system in humans and is often related to deficits associated with autism. These findings suggest that the mirror neuron system may not be non-functional in individuals with autism, but simply abnormal in its development. This information is significant to the present discussion because mu waves may be integrating different areas of mirror neuron activity in the brain. Other studies have assessed attempts to consciously stimulate the mirror neuron system and suppress mu waves using neurofeedback (a type of biofeedback given through computers that analyze real time recordings of brain activity, in this case EEGs of mu waves). This type of therapy is still in its early phases of implementation for individuals with autism, and has conflicting forecasts for success.
Brain-computer interfaces (BCIs) are a developing technology that clinicians hope will one day bring more independence and agency to the severely physically disabled. Those the technology has the potential to help include people with near-total or total paralysis, such as those with tetraplegia (quadriplegia) or advanced amyotrophic lateral sclerosis (ALS); BCIs are intended to help them to communicate or even move objects such as motorized wheelchairs, neuroprostheses, or robotic grasping tools. Few of these technologies are currently in regular use by people with disabilities, but a diverse array are in development at an experimental level. One type of BCI uses event-related desynchronization (ERD) of the mu wave in order to control the computer. This method of monitoring brain activity takes advantage of the fact that when a group of neurons is at rest they tend to fire in synchrony with each other. When a participant is cued to imagine movement (an "event"), the resulting desynchronization (the group of neurons that was firing in synchronous waves now firing in complex and individualized patterns) can be reliably detected and analyzed by a computer. Users of such an interface are trained in visualizing movements, typically of the foot, hand, and/or tongue, which are each in different locations on the cortical homunculus and thus distinguishable by an electroencephalograph (EEG) or electrocorticograph (ECoG) recording of electrical activity over the motor cortex. In this method, computers monitor for a typical pattern of mu wave ERD contralateral to the visualized movement combined with event-related synchronization (ERS) in the surrounding tissue. This paired pattern intensifies with training, and the training increasingly takes the form of games, some of which utilize virtual reality. Some researchers have found that the feedback from virtual reality games is particularly effective in giving the user tools to improve control of his or her mu wave patterns. The ERD method can be combined with one or more other methods of monitoring the brain's electrical activity to create hybrid BCIs, which often offer more flexibility than a BCI that uses any single monitoring method.
Mu waves have been studied since the 1930s, and are referred to as the wicket rhythm because the rounded EEG waves resemble croquet wickets. In 1950, Henri Gastaut and his coworkers reported desynchronization of these waves not only during active movements of their subjects, but also while the subjects observed actions executed by someone else. These results were later confirmed by additional research groups, including a study using subdural electrode grids in epileptic patients. The latter study showed mu suppression while the patients observed moving body parts in somatic areas of the cortex that corresponded to the body part moved by the actor. Further studies have shown that the mu waves can also be desynchronized by imagining actions and by passively viewing point-light biological motion.
- Delta wave – (0.1 – 3 Hz)
- Theta wave – (4 – 7 Hz)
- Alpha wave – (8 – 15 Hz)
- Mu wave – (7.5 – 12.5 Hz)
- SMR wave – (12.5 – 15.5 Hz)
- Beta wave – (16 – 31 Hz)
- Gamma wave – (32 – 100 Hz)
- Amzica, Florin; Fernando Lopes da Silva (2010). "Celluluar Substrates of Brain Rhythms". In Schomer, Donald L.; Fernando Lopes da Silva. 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.
- Oberman, Lindsay M.; Edward M. Hubbarda; Eric L. Altschulera; Vilayanur S. Ramachandran; Jaime A. Pineda (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. Cite error: Invalid
<ref>tag; name "Oberman" defined multiple times with different content (see the help page).
- Pineda, Jaime A. (1 December 2005). "The functional significance of mu rhythms: Translating "seeing" and "hearing" into "doing"". Brain Research Reviews 50 (1): 57–68. doi:10.1016/j.brainresrev.2005.04.005. PMID 15925412.
- 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.
- Williams, Justin H.G.; Waiter, Gordon D.; Gilchrist, Anne; Perrett, David I.; Murray, Alison D.; Whiten, Andrew (1 January 2006). "Neural mechanisms of imitation and 'mirror neuron' functioning in autistic spectrum disorder" (PDF). Neuropsychologia 44 (4): 610–621. doi:10.1016/j.neuropsychologia.2005.06.010. PMID 16140346.
- Pfurtscheller, Gert; Christa Neuper (2010). "EEG-Based Brain-Computer Interfaces". In Schomer, Donald L.; Fernando H. Lopes da Silva. 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.
- di Pellegrino, G.; Fadiga, L.; Fogassi, L.; Gallese, F.; Rizzolatti, G. (1992). "Understanding motor events: A neurophysiological study". Experimental Brain Research 91 (1): 176–180. doi:10.1007/bf00230027. PMID 1301372.
- Rizzolatti, G; Fogassi, L; Gallese, V (September 2001). "Neurophysiological mechanisms underlying the understanding and imitation of action". Nature reviews. Neuroscience 2 (9): 661–70. doi:10.1038/35090060. PMID 11533734.
- Marshall, Peter J.; Meltzoff, Andrew N. (2011). "Neural mirroring systems: Exploring the EEG mu rhythm in human infancy". Developmental Cognitive Neuroscience 1 (2): 110–123. doi:10.1016/j.dcn.2010.09.001. PMC 3081582. PMID 21528008.
- Keuken, M. C.; Hardie, A.; Dorn, B. T.; Dev, S.; Paulus, M. P.; Jonas, K. J.; Den Wildenberg, W. P.; Pineda, J. A. (6). "The role of the left inferior frontal gyrus in social perception: an rTMS study". Brain Research 1383: 196–205. doi:10.1016/j.brainres.2011.01.073. PMID 21281612. Check date values in:
- Sinigaglia, C; Rizzolatti, G (March 2011). "Through the looking glass: self and others". Consciousness and cognition 20 (1): 64–74. doi:10.1016/j.concog.2010.11.012. PMID 21220203.
- Berchicci, M.; Zhang, T.; Romero, L.; Peters, A.; Annett, R.; Teuscher, U.; Bertollo, M.; Okada, Y.; Stephen, J.; Comani, S. (21 July 2011). "Development of Mu Rhythm in Infants and Preschool Children". Developmental Neuroscience 33 (2): 130–143. doi:10.1159/000329095. PMC 3221274. PMID 21778699.
- Meltzoff, A. N.; Kuhl, P. K.; Movellan, J.; Sejnowski, T. J. (16). "Foundations for a New Science of Learning". Science 325 (5938): 284–288. doi:10.1126/science.1175626. PMC 2776823. PMID 19608908. Check date values in:
- Pineda, J.A.; Juavinett, A.; Datko, M. (1). "Self-regulation of brain oscillations as a treatment for aberrant brain connections in children with autism". Medical Hypotheses 79 (6): 790–798. doi:10.1016/j.mehy.2012.08.031. PMID 22999736. Check date values in:
- Bastiaansen, JA; Thioux, M; Nanetti, L; van der Gaag, C; Ketelaars, C; Minderaa, R; Keysers, C (1). "Age-related increase in inferior frontal gyrus activity and social functioning in autism spectrum disorder". Biological Psychiatry 69 (9): 832–838. doi:10.1016/j.biopsych.2010.11.007. PMID 21310395. Check date values in:
- Holtmann, Martin; Steiner, Sabina; Hohmann, Sarah; Poustka, Luise; Banaschewski, Tobias; Bölte, Sven (1). "Neurofeedback in autism spectrum disorders". Developmental Medicine & Child Neurology 53 (11): 986–993. doi:10.1111/j.1469-8749.2011.04043.x. PMID 21752020. Check date values in:
- Coben, Robert; Linden, Michael; Myers, Thomas E. (24). "Neurofeedback for Autistic Spectrum Disorder: A Review of the Literature". Applied Psychophysiology and Biofeedback 35 (1): 83–105. doi:10.1007/s10484-009-9117-y. PMID 19856096. Check date values in:
- Machado, S; Araújo, F; Paes, F; Velasques, B; Cunha, M; Budde, H; Basile, LF; Anghinah, R; Arias-Carrión, O; Cagy, M; Piedade, R; de Graaf, TA; Sack, AT; Ribeiro, P (2010). "EEG-based brain-computer interfaces: an overview of basic concepts and clinical applications in neurorehabilitation". Reviews in the neurosciences 21 (6): 451–68. doi:10.1515/REVNEURO.2010.21.6.451. PMID 21438193.
- Pfurtscheller, Gert; McFarland, Dennis J. (2012). "BCIs that use sensorimotor rhythms". In Wolpaw, Jonathan R.; Wolpaw, Elizabeth Winter. Brain-Computer Interfaces: Principles and Practice. Oxford: Oxford University Press. pp. 227–240. ISBN 9780195388855.
- Leuthardt, Eric C.; Schalk, Gerwin; Roland, Jarod; Rouse, Adam; Moran, Daniel W. (2009). "Evolution of brain-computer interfaces: going beyond classic motor physiology". Neurosurgical Focus 27 (1): E4. doi:10.3171/2009.4.FOCUS0979. PMC 2920041. PMID 19569892.
- Allison, B Z; Leeb, R; Brunner, C; Müller-Putz, G R; Bauernfeind, G; Kelly, J W; Neuper, C (1). "Toward smarter BCIs: extending BCIs through hybridization and intelligent control". Journal of Neural Engineering 9 (1): 013001. doi:10.1088/1741-2560/9/1/013001. PMID 22156029. Check date values in:
- Cohen-Seat, G., Gastaut, H., Faure, J., & Heuyer, G. (1954). "Etudes experimentales de l'activite nerveuse pendant la projection cinematographique". Rev. Int. Filmologie 5: 7–64.
- Gastaut, H. J., & Bert, J. (1954). "EEG changes during cinematographic presentation". Electroencephalogr. Clin. Neurophysiol. 6 (3): 433–444. doi:10.1016/0013-4694(54)90058-9. PMID 13200415.
- Cochin, S., Barthelemy, C., Lejeune, B., Roux, S., & Martineau, J. (1998). "Perception of motion and qEEG activity in human adults". Electroencephalogr Clin Neurophysiol 107 (4): 287–295. doi:10.1016/S0013-4694(98)00071-6. PMID 9872446.
- Cochin, S., Barthelemy, C., Roux, S., & Martineau, J. (1999). "Observation and execution of movement: similarities demonstrated by quantified electroencephalography". Eur J Neurosci 11 (5): 1839–1842. doi:10.1046/j.1460-9568.1999.00598.x. PMID 10215938.
- Muthukumaraswamy, S. D., Johnson, B. W., & McNair, N. A. (2004). "Mu rhythm modulation during observation of an object-directed grasp". Brain Res Cogn Brain Res 19 (2): 195–201. doi:10.1016/j.cogbrainres.2003.12.001. PMID 15019715.
- Arroyo, S., Lesser, R. P., Gordon, B., Uematsu, S., Jackson, D., & Webber, R. (1993). "Functional significance of the mu rhythm of human cortex: an electrophysiologic study with subdural electrodes". Electroencephalography and Clinical Neurophysiology 87 (3): 76–87. doi:10.1016/0013-4694(93)90114-B. PMID 7691544.
- Pfurtscheller, G., Brunner, C., Schlogl, A., & Lopes da Silva, F. H. (2006). "Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks". Neuroimage 31 (1): 153–159. doi:10.1016/j.neuroimage.2005.12.003. PMID 16443377.
- Pineda, J. A., Allison, B. Z., & Vankov, A. (2000). "The effects of self-movement, observation, and imagination on mu rhythms and readiness potentials (RP's): toward a brain–computer interface (BCI)". IEEE Trans Rehabil Eng 8 (2): 219–222. doi:10.1109/86.847822. PMID 10896193.
- Ulloa, E. R., & Pineda, J. A. (2007). "Recognition of point-light biological motion: mu rhythms and mirror neuron activity". Behav Brain Res 183 (2): 188–194. doi:10.1016/j.bbr.2007.06.007. PMID 17658625.