Brainloop
 
4. BCI - Brain–computer interfaces 
    4.2. Present-day BCIs
  4.2.4. Mu and beta rhythms

In awake people, primary sensory or motor cortical areas often display 8–12 Hz EEG activity when they are not engaged in processing sensory input or producing motor output.
This idling activity is called mu rhythm when focused over somatosensory or motor cortex and visual alpha rhythm when focused over visual cortex.

mu-rhythm activity comprises a variety of different 8–12 Hz rhythms, distinguished from each other by location, frequency, and/or relationship to concurrent sensory input or motor output. These mu rhythms are usually associated with 18–26 Hz beta rhythms. While some beta rhythms are harmonics of mu rhythms, some are separable from them by topography and/or timing, and thus are independent EEG features.

mu and/or beta rhythms could be good signal features for EEG-based communication. They are associated with those cortical areas most directly connected to the brain’s normal motor output channels.

Movement or preparation for movement is typically accompanied by a decrease in mu and beta rhythms.
This decrease has been labeled 'event-related desynchronization' or ERD.

Rhythm increase after movement and with relaxation.
This increase has been labeled 'event-related synchronization' (ERS).

ERD and ERS do not require actual movement, they occur also with motor imagery (i.e. imagined movement).

 
4. BCI - Intro
  4.1. Definition and features of a BCI
  4.1.1. Dependent and independent BCIs
4.1.2. BCI use is a skill
4.1.3. The parts of a BCI
  4.1.3.1. Signal acquisition
4.1.3.2. Signal processing: feature extraction
4.1.3.3. Signal processing: the translation algorithm
4.1.3.4. The output device
4.1.3.5. The operating protocol
4.2. Present-day BCIs
  4.2.1. Visual evoked potentials
4.2.2. Slow cortical potentials
4.2.3. P300 evoked potentials
4.2.4. Mu and beta rhythms
  4.2.4.1. The Wadsworth BCI
4.2.4.2. The Graz BCI
4.2.5. Cortical neuronal action potentials
4.3. The future of BCI-based communication
   

Source: Brain–computer interfaces for communication and control, Clinical Neurophysiology 113 (2002) 767–791, Jonathan R. Wolpaw, Niels Birbaumer, Dennis J. McFarland, Gert Pfurtscheller, Theresa M. Vaughan
 
 
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