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

People with or without motor disabilities learn to control mu- or beta-rhythm amplitude and use that control to move a cursor in one or two dimensions to targets on a computer screen.

The picture shows the basic phenomenon.

Picture: sensorimotor rhythm BCI. Scalp EEG is recorded over sensorimotor cortex. Users control the amplitude of a 8–12 Hz mu rhythm (or a 18–26 Hz beta rhythm) to move a cursor to a target at the top of the screen or to a target at the bottom (or to additional targets at intermediate locations). Frequency spectra (top) for top and bottom targets show that control is clearly focused in the mu-rhythm frequency band. Sample EEG traces (bottom) also indicate that the mu rhythm is prominent when the target is at the top and minimal when it is at the bottom.

Users learn over a series of 40 min sessions to control cursor movement. They participate in 2–3 sessions per week, and most acquire significant control within 2–3 weeks. In the initial sessions, most employ motor imagery (e.g. imagination of hand movements, whole body activities, relaxation, etc.) to control the cursor.
As training proceeds, imagery usually becomes less important, and users move the cursor like they perform conventional motor acts, that is, without thinking about the details of performance.


 
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