@article {3395,
	title = {Adaptive spatio-temporal filtering for movement related potentials in EEG-based brain-computer interfaces.},
	journal = {IEEE Trans Neural Syst Rehabil Eng},
	volume = {22},
	year = {2014},
	month = {07/2014},
	pages = {847-57},
	abstract = {Movement related potentials (MRPs) are used as features in many brain-computer interfaces (BCIs) based on electroencephalogram (EEG). MRP feature extraction is challenging since EEG is noisy and varies between subjects. Previous studies used spatial and spatio-temporal filtering methods to deal with these problems. However, they did not optimize temporal information or may have been susceptible to overfitting when training data are limited and the feature space is of high dimension. Furthermore, most of these studies manually select data windows and low-pass frequencies. We propose an adaptive spatio-temporal (AST) filtering method to model MRPs more accurately in lower dimensional space. AST automatically optimizes all parameters by employing a Gaussian kernel to construct a low-pass time-frequency filter and a linear ridge regression (LRR) algorithm to compute a spatial filter. Optimal parameters are simultaneously sought by minimizing leave-one-out cross-validation error through gradient descent. Using four BCI datasets from 12 individuals, we compare the performances of AST filter to two popular methods: the discriminant spatial pattern filter and regularized spatio-temporal filter. The results demonstrate that our AST filter can make more accurate predictions and is computationally feasible.},
	keywords = {Algorithms, Artificial Intelligence, brain-computer interfaces, Data Interpretation, Statistical, Electroencephalography, Evoked Potentials, Motor, Humans, Imagination, Motor Cortex, Movement, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Signal Processing, Computer-Assisted, Spatio-Temporal Analysis},
	issn = {1558-0210},
	doi = {10.1109/TNSRE.2014.2315717},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/24723632},
	author = {Lu, Jun and Xie, Kan and Dennis J. McFarland}
}
@article {3244,
	title = {EEG-based communication and control: short-term role of feedback.},
	journal = {IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
	volume = {6},
	year = {1998},
	month = {03/1998},
	pages = {7{\textendash}11},
	abstract = {When 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.},
	keywords = {Sensitivity and Specificity},
	issn = {1063-6528},
	doi = {10.1109/86.662615},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/9535518},
	author = {Dennis J. McFarland and McCane, L. M. and Jonathan Wolpaw}
}
