01613nas a2200181   4500008004100000022001400041245007000055210006600125260001200191300001100203490000600214520106600220653003201286100002601318700001901344700002101363856004701384       1998                        eng d  a1063-652800aEEG-based communication and control: short-term role of feedback.0 aEEGbased communication and control shortterm role of feedback  c03/1998  a7–110 v63 aWhen people learn to control the amplitudes of certain electroencephalogram (EEG) components (e.g., the 8-12 Hz mu-rhythm over sensorimotor cortex) and use them to move a cursor to a target on a video screen, feedback about performance is normally provided by cursor movement and by trial outcome (i.e., success or failure). We assessed the short-term effects of this feedback on EEG control. After subjects received initial training with feedback present, feedback was removed intermittently for periods of several minutes. Subjects still displayed EEG control when feedback was removed. Removal of cursor movement alone appeared to have effects comparable to removal of both cursor movement and trial outcome. These results show that, in the short-term at least, mu-rhythm control is not dependent on the sensory input provided by cursor movement. They also suggest that feedback can have inhibitory as well as facilitory effects on EEG control, and that these effects vary across subjects. This finding has implications for the design of training procedures.10aSensitivity and Specificity1 aMcFarland, Dennis, J.1 aMcCane, L., M.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/953551802193nas a2200265   4500008004100000022001400041245005800055210005600113260001200169300001400181490000800195520144100203653002801644653002701672653001401699653002501713653001501738653001901753653002401772100002601796700001901822700001801841700002101859856004701880       1997                        eng d  a0013-469400aSpatial filter selection for EEG-based communication.0 aSpatial filter selection for EEGbased communication  c09/1997  a386–3940 v1033 aIndividuals can learn to control the amplitude of mu-rhythm activity in the EEG recorded over sensorimotor cortex and use it to move a cursor to a target on a video screen. The speed and accuracy of cursor movement depend on the consistency of the control signal and on the signal-to-noise ratio achieved by the spatial and temporal filtering methods that extract the activity prior to its translation into cursor movement. The present study compared alternative spatial filtering methods. Sixty-four channel EEG data collected while well-trained subjects were moving the cursor to targets at the top or bottom edge of a video screen were analyzed offline by four different spatial filters, namely a standard ear-reference, a common average reference (CAR), a small Laplacian (3 cm to set of surrounding electrodes) and a large Laplacian (6 cm to set of surrounding electrodes). The CAR and large Laplacian methods proved best able to distinguish between top and bottom targets. They were significantly superior to the ear-reference method. The difference in performance between the large Laplacian and small Laplacian methods presumably indicated that the former was better matched to the topographical extent of the EEG control signal. The results as a whole demonstrate the importance of proper spatial filter selection for maximizing the signal-to-noise ratio and thereby improving the speed and accuracy of EEG-based communication.10aassistive communication10aElectroencephalography10amu rhythm10aoperant conditioning10aprosthesis10aRehabilitation10asensorimotor cortex1 aMcFarland, Dennis, J.1 aMcCane, L., M.1 aDavid, S., V.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/9305287