TY - JOUR T1 - Adaptive spatio-temporal filtering for movement related potentials in EEG-based brain-computer interfaces. JF - IEEE Trans Neural Syst Rehabil Eng Y1 - 2014 A1 - Lu, Jun A1 - Xie, Kan A1 - Dennis J. McFarland KW - Algorithms KW - Artificial Intelligence KW - brain-computer interfaces KW - Data Interpretation, Statistical KW - Electroencephalography KW - Evoked Potentials, Motor KW - Humans KW - Imagination KW - Motor Cortex KW - Movement KW - Pattern Recognition, Automated KW - Reproducibility of Results KW - Sensitivity and Specificity KW - Signal Processing, Computer-Assisted KW - Spatio-Temporal Analysis AB - 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. VL - 22 UR - http://www.ncbi.nlm.nih.gov/pubmed/24723632 IS - 4 ER - TY - JOUR T1 - Value of amplitude, phase, and coherence features for a sensorimotor rhythm-based brain-computer interface. JF - Brain Res Bull Y1 - 2012 A1 - Krusienski, Dean J A1 - Dennis J. McFarland A1 - Jonathan Wolpaw KW - Algorithms KW - Brain KW - Electroencephalography KW - Humans KW - Motor Cortex KW - User-Computer Interface AB - Measures that quantify the relationship between two or more brain signals are drawing attention as neuroscientists explore the mechanisms of large-scale integration that enable coherent behavior and cognition. Traditional Fourier-based measures of coherence have been used to quantify frequency-dependent relationships between two signals. More recently, several off-line studies examined phase-locking value (PLV) as a possible feature for use in brain-computer interface (BCI) systems. However, only a few individuals have been studied and full statistical comparisons among the different classes of features and their combinations have not been conducted. The present study examines the relative BCI performance of spectral power, coherence, and PLV, alone and in combination. The results indicate that spectral power produced classification at least as good as PLV, coherence, or any possible combination of these measures. This may be due to the fact that all three measures reflect mainly the activity of a single signal source (i.e., an area of sensorimotor cortex). This possibility is supported by the finding that EEG signals from different channels generally had near-zero phase differences. Coherence, PLV, and other measures of inter-channel relationships may be more valuable for BCIs that use signals from more than one distinct cortical source. VL - 87 UR - http://www.ncbi.nlm.nih.gov/pubmed/21985984 IS - 1 ER - TY - JOUR T1 - Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. JF - Neurology Y1 - 2005 A1 - Kübler, A. A1 - Nijboer, F A1 - Mellinger, Jürgen A1 - Theresa M Vaughan A1 - Pawelzik, H A1 - Gerwin Schalk A1 - Dennis J. McFarland A1 - Niels Birbaumer A1 - Jonathan Wolpaw KW - Aged KW - Amyotrophic Lateral Sclerosis KW - Electroencephalography KW - Evoked Potentials, Motor KW - Evoked Potentials, Somatosensory KW - Female KW - Humans KW - Imagination KW - Male KW - Middle Aged KW - Motor Cortex KW - Movement KW - Paralysis KW - Photic Stimulation KW - Prostheses and Implants KW - Somatosensory Cortex KW - Treatment Outcome KW - User-Computer Interface AB -

People with severe motor disabilities can maintain an acceptable quality of life if they can communicate. Brain-computer interfaces (BCIs), which do not depend on muscle control, can provide communication. Four people severely disabled by ALS learned to operate a BCI with EEG rhythms recorded over sensorimotor cortex. These results suggest that a sensorimotor rhythm-based BCI could help maintain quality of life for people with ALS.

VL - 64 UR - http://www.ncbi.nlm.nih.gov/pubmed/15911809 IS - 10 ER -