02668nas a2200241   4500008004100000022001400041245009000055210006900145260001200214300001100226490000700237520188700244653002802131653003502159653003402194653003102228653004102259653001902300100001202319700002602331700002102357856004802378       2013                        eng d  a1741-255200aAdaptive Laplacian filtering for sensorimotor rhythm-based brain-computer interfaces.0 aAdaptive Laplacian filtering for sensorimotor rhythmbased brainc  c02/2013  a0160020 v103 aOBJECTIVE:
Sensorimotor rhythms (SMRs) are 8-30 Hz oscillations in the electroencephalogram (EEG) recorded from the scalp over sensorimotor cortex that change with movement and/or movement imagery. Many brain-computer interface (BCI) studies have shown that people can learn to control SMR amplitudes and can use that control to move cursors and other objects in one, two or three dimensions. At the same time, if SMR-based BCIs are to be useful for people with neuromuscular disabilities, their accuracy and reliability must be improved substantially. These BCIs often use spatial filtering methods such as common average reference (CAR), Laplacian (LAP) filter or common spatial pattern (CSP) filter to enhance the signal-to-noise ratio of EEG. Here, we test the hypothesis that a new filter design, called an 'adaptive Laplacian (ALAP) filter', can provide better performance for SMR-based BCIs.
APPROACH:
An ALAP filter employs a Gaussian kernel to construct a smooth spatial gradient of channel weights and then simultaneously seeks the optimal kernel radius of this spatial filter and the regularization parameter of linear ridge regression. This optimization is based on minimizing the leave-one-out cross-validation error through a gradient descent method and is computationally feasible.
MAIN RESULTS:
Using a variety of kinds of BCI data from a total of 22 individuals, we compare the performances of ALAP filter to CAR, small LAP, large LAP and CSP filters. With a large number of channels and limited data, ALAP performs significantly better than CSP, CAR, small LAP and large LAP both in classification accuracy and in mean-squared error. Using fewer channels restricted to motor areas, ALAP is still superior to CAR, small LAP and large LAP, but equally matched to CSP.
SIGNIFICANCE:
Thus, ALAP may help to improve the accuracy and robustness of SMR-based BCIs.10aassistive communication10abrain computer interface (BCI)10abrain-machine interface (BMI)10aelectroencephalogram (EEG)10aleave-one-out (LOO) cross-validation10aspatial filter1 aLu, Jun1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/23220879