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 - A graphical model framework for decoding in the visual ERP-based BCI speller. JF - Neural Comput Y1 - 2011 A1 - Martens, S M M A1 - Mooij, J M A1 - Jeremy Jeremy Hill A1 - Farquhar, Jason A1 - Schölkopf, B KW - Artificial Intelligence KW - Computer User Training KW - Discrimination Learning KW - Electroencephalography KW - Evoked Potentials KW - Evoked Potentials, Visual KW - Humans KW - Language KW - Models, Neurological KW - Models, Theoretical KW - Reading KW - Signal Processing, Computer-Assisted KW - User-Computer Interface KW - Visual Cortex KW - Visual Perception AB -

We present a graphical model framework for decoding in the visual ERP-based speller system. The proposed framework allows researchers to build generative models from which the decoding rules are obtained in a straightforward manner. We suggest two models for generating brain signals conditioned on the stimulus events. Both models incorporate letter frequency information but assume different dependencies between brain signals and stimulus events. For both models, we derive decoding rules and perform a discriminative training. We show on real visual speller data how decoding performance improves by incorporating letter frequency information and using a more realistic graphical model for the dependencies between the brain signals and the stimulus events. Furthermore, we discuss how the standard approach to decoding can be seen as a special case of the graphical model framework. The letter also gives more insight into the discriminative approach for decoding in the visual speller system.

VL - 23 UR - http://www.ncbi.nlm.nih.gov/pubmed/20964540 IS - 1 ER - TY - JOUR T1 - Seizure prediction for epilepsy using a multi-stage phase synchrony based system. JF - Conf Proc IEEE Eng Med Biol Soc Y1 - 2009 A1 - Christopher J James A1 - Disha Gupta KW - Algorithms KW - Artificial Intelligence KW - Diagnosis, Computer-Assisted KW - Electroencephalography KW - Epilepsy KW - Humans KW - Pattern Recognition, Automated KW - Reproducibility of Results KW - Sensitivity and Specificity AB - Seizure onset prediction in epilepsy is a challenge which is under investigation using many and varied signal processing techniques. Here we present a multi-stage phase synchrony based system that brings to bear the advantages of many techniques in each substage. The 1(st) stage of the system unmixes continuous long-term (2-4 days) multichannel scalp EEG using spatially constrained Independent Component Analysis and estimates the long term significant phase synchrony dynamics of narrowband (2-8 Hz and 8-14 Hz) seizure components. It then projects multidimensional features onto a 2-D map using Neuroscale and evaluates the probability of predictive events using Gaussian Mixture Models. We show the possibility of seizure onset prediction within a prediction window of 35-65 minutes with a sensitivity of 65-100% and specificity of 65-80% across epileptic patients. VL - 2009 UR - http://www.ncbi.nlm.nih.gov/pubmed/19965104 ER - TY - JOUR T1 - Particle-verification for single-particle, reference-based reconstruction using multivariate data analysis and classification. JF - J Struct Biol Y1 - 2008 A1 - Shaikh, Tanvir R A1 - Trujillo, Ramon A1 - LeBarron, Jamie A1 - Baxter, Bill A1 - Frank, Joachim KW - Algorithms KW - Artificial Intelligence KW - Classification KW - Image Enhancement KW - Image Processing, Computer-Assisted KW - Microscopy, Electron KW - Multivariate Analysis KW - Ribosomes AB -

As collection of electron microscopy data for single-particle reconstruction becomes more efficient, due to electronic image capture, one of the principal limiting steps in a reconstruction remains particle-verification, which is especially costly in terms of user input. Recently, some algorithms have been developed to window particles automatically, but the resulting particle sets typically need to be verified manually. Here we describe a procedure to speed up verification of windowed particles using multivariate data analysis and classification. In this procedure, the particle set is subjected to multi-reference alignment before the verification. The aligned particles are first binned according to orientation and are binned further by K-means classification. Rather than selection of particles individually, an entire class of particles can be selected, with an option to remove outliers. Since particles in the same class present the same view, distinction between good and bad images becomes more straightforward. We have also developed a graphical interface, written in Python/Tkinter, to facilitate this implementation of particle-verification. For the demonstration of the particle-verification scheme presented here, electron micrographs of ribosomes are used.

VL - 164 UR - http://www.ncbi.nlm.nih.gov/pubmed/18619547 IS - 1 ER - TY - JOUR T1 - Space-time ICA versus Ensemble ICA for ictal EEG analysis with component differentiation via Lempel-Ziv complexity. JF - Conf Proc IEEE Eng Med Biol Soc Y1 - 2007 A1 - Christopher J James A1 - Abásolo, Daniel A1 - Disha Gupta KW - Algorithms KW - Artificial Intelligence KW - Diagnosis, Computer-Assisted KW - Electroencephalography KW - Epilepsy KW - Humans KW - Pattern Recognition, Automated KW - Principal Component Analysis KW - Reproducibility of Results KW - Sensitivity and Specificity AB - In this proof-of-principle study we analyzed intracranial electroencephalogram recordings in patients with intractable focal epilepsy. We contrast two implementations of Independent Component Analysis (ICA) - Ensemble (or spatial) ICA (E-ICA) and Space-Time ICA (ST-ICA) in separating out the ictal components underlying the measurements. In each case we assess the outputs of the ICA algorithms by means of a non-linear method known as the Lempel-Ziv (LZ) complexity. LZ complexity quantifies the complexity of a time series and is well suited to the analysis of non-stationary biomedical signals of short length. Our results show that for small numbers of intracranial recordings, standard E-ICA results in marginal improvements in the separation as measured by the LZ complexity changes. ST-ICA using just 2 recording channels both near and far from the epileptic focus result in more distinct ictal components--although at this stage there is a subjective element to the separation process for ST-ICA. Our results are promising showing that it is possible to extract meaningful information from just 2 recording electrodes through ST-ICA, even if they are not directly over the seizure focus. This work is being further expanded for seizure onset analysis. VL - 08/2007 UR - http://www.ncbi.nlm.nih.gov/pubmed/18003250 ER - TY - JOUR T1 - Classifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects. JF - IEEE Trans Neural Syst Rehabil Eng Y1 - 2006 A1 - Jeremy Jeremy Hill A1 - Lal, T.N A1 - Schröder, Michael A1 - Hinterberger, T. A1 - Wilhelm, Barbara A1 - Nijboer, F A1 - Mochty, Ursula A1 - Widman, Guido A1 - Elger, Christian A1 - Schölkopf, B A1 - Kübler, A. A1 - Niels Birbaumer KW - Algorithms KW - Artificial Intelligence KW - Cluster Analysis KW - Computer User Training KW - Electroencephalography KW - Evoked Potentials KW - Female KW - Humans KW - Imagination KW - Male KW - Middle Aged KW - Paralysis KW - Pattern Recognition, Automated KW - User-Computer Interface AB -

We summarize results from a series of related studies that aim to develop a motor-imagery-based brain-computer interface using a single recording session of electroencephalogram (EEG) or electrocorticogram (ECoG) signals for each subject. We apply the same experimental and analytical methods to 11 nonparalysed subjects (eight EEG, three ECoG), and to five paralyzed subjects (four EEG, one ECoG) who had been unable to communicate for some time. While it was relatively easy to obtain classifiable signals quickly from most of the nonparalyzed subjects, it proved impossible to classify the signals obtained from the paralyzed patients by the same methods. This highlights the fact that though certain BCI paradigms may work well with healthy subjects, this does not necessarily indicate success with the target user group. We outline possible reasons for this failure to transfer.

VL - 14 UR - http://www.ncbi.nlm.nih.gov/pubmed/16792289 IS - 2 ER - TY - JOUR T1 - The BCI Competition 2003: Progress and perspectives in detection and discrimination of EEG single trials. JF - IEEE Trans Biomed Eng Y1 - 2004 A1 - Benjamin Blankertz A1 - Müller, Klaus-Robert A1 - Curio, Gabriel A1 - Theresa M Vaughan A1 - Gerwin Schalk A1 - Jonathan Wolpaw A1 - Schlögl, Alois A1 - Neuper, Christa A1 - Pfurtscheller, Gert A1 - Hinterberger, T. A1 - Schröder, Michael A1 - Niels Birbaumer KW - Adult KW - Algorithms KW - Amyotrophic Lateral Sclerosis KW - Artificial Intelligence KW - Brain KW - Cognition KW - Databases, Factual KW - Electroencephalography KW - Evoked Potentials KW - Humans KW - Reproducibility of Results KW - Sensitivity and Specificity KW - User-Computer Interface AB - Interest in developing a new method of man-to-machine communication--a brain-computer interface (BCI)--has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms. VL - 51 IS - 6 ER -