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 - Causal influence of gamma oscillations on the sensorimotor rhythm. JF - Neuroimage Y1 - 2011 A1 - Grosse-Wentrup, Moritz A1 - Schölkopf, B A1 - Jeremy Jeremy Hill KW - Adult KW - Cerebral Cortex KW - Electroencephalography KW - Female KW - Humans KW - Imagination KW - Male KW - Signal Processing, Computer-Assisted KW - User-Computer Interface AB -

Gamma oscillations of the electromagnetic field of the brain are known to be involved in a variety of cognitive processes, and are believed to be fundamental for information processing within the brain. While gamma oscillations have been shown to be correlated with brain rhythms at different frequencies, to date no empirical evidence has been presented that supports a causal influence of gamma oscillations on other brain rhythms. In this work, we study the relation of gamma oscillations and the sensorimotor rhythm (SMR) in healthy human subjects using electroencephalography. We first demonstrate that modulation of the SMR, induced by motor imagery of either the left or right hand, is positively correlated with the power of frontal and occipital gamma oscillations, and negatively correlated with the power of centro-parietal gamma oscillations. We then demonstrate that the most simple causal structure, capable of explaining the observed correlation of gamma oscillations and the SMR, entails a causal influence of gamma oscillations on the SMR. This finding supports the fundamental role attributed to gamma oscillations for information processing within the brain, and is of particular importance for brain-computer interfaces (BCIs). As modulation of the SMR is typically used in BCIs to infer a subject's intention, our findings entail that gamma oscillations have a causal influence on a subject's capability to utilize a BCI for means of communication.

VL - 56 UR - http://www.ncbi.nlm.nih.gov/pubmed/20451626 IS - 2 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 - Spatiotemporal dynamics of electrocorticographic high gamma activity during overt and covert word repetition. JF - Neuroimage Y1 - 2011 A1 - Pei, Xiao-Mei A1 - Leuthardt, E C A1 - Charles M Gaona A1 - Peter Brunner A1 - Jonathan Wolpaw A1 - Gerwin Schalk KW - Adolescent KW - Adult KW - Brain KW - Brain Mapping KW - Electroencephalography KW - Female KW - Humans KW - Male KW - Middle Aged KW - Signal Processing, Computer-Assisted KW - Verbal Behavior AB -

Language is one of the defining abilities of humans. Many studies have characterized the neural correlates of different aspects of language processing. However, the imaging techniques typically used in these studies were limited in either their temporal or spatial resolution. Electrocorticographic (ECoG) recordings from the surface of the brain combine high spatial with high temporal resolution and thus could be a valuable tool for the study of neural correlates of language function. In this study, we defined the spatiotemporal dynamics of ECoG activity during a word repetition task in nine human subjects. ECoG was recorded while each subject overtly or covertly repeated words that were presented either visually or auditorily. ECoG amplitudes in the high gamma (HG) band confidently tracked neural changes associated with stimulus presentation and with the subject's verbal response. Overt word production was primarily associated with HG changes in the superior and middle parts of temporal lobe, Wernicke's area, the supramarginal gyrus, Broca's area, premotor cortex (PMC), primary motor cortex. Covert word production was primarily associated with HG changes in superior temporal lobe and the supramarginal gyrus. Acoustic processing from both auditory stimuli as well as the subject's own voice resulted in HG power changes in superior temporal lobe and Wernicke's area. In summary, this study represents a comprehensive characterization of overt and covert speech using electrophysiological imaging with high spatial and temporal resolution. It thereby complements the findings of previous neuroimaging studies of language and thus further adds to current understanding of word processing in humans.

VL - 54 UR - http://www.ncbi.nlm.nih.gov/pubmed/21029784 IS - 4 ER - TY - JOUR T1 - Transition from the locked in to the completely locked-in state: a physiological analysis. JF - Clin Neurophysiol Y1 - 2011 A1 - Murguialday, A Ramos A1 - Jeremy Jeremy Hill A1 - Bensch, M A1 - Martens, S M M A1 - S Halder A1 - Nijboer, F A1 - Schoelkopf, Bernhard A1 - Niels Birbaumer A1 - Gharabaghi, A KW - Adult KW - Amyotrophic Lateral Sclerosis KW - Area Under Curve KW - Brain KW - Communication Aids for Disabled KW - Disease Progression KW - Electroencephalography KW - Electromyography KW - Humans KW - Male KW - Signal Processing, Computer-Assisted KW - User-Computer Interface AB -

OBJECTIVE: 

To clarify the physiological and behavioral boundaries between locked-in (LIS) and the completely locked-in state (CLIS) (no voluntary eye movements, no communication possible) through electrophysiological data and to secure brain-computer-interface (BCI) communication.

METHODS: 

Electromyography from facial muscles, external anal sphincter (EAS), electrooculography and electrocorticographic data during different psychophysiological tests were acquired to define electrophysiological differences in an amyotrophic lateral sclerosis (ALS) patient with an intracranially implanted grid of 112 electrodes for nine months while the patient passed from the LIS to the CLIS.

RESULTS: 

At the very end of the LIS there was no facial muscle activity, nor external anal sphincter but eye control. Eye movements were slow and lasted for short periods only. During CLIS event related brainpotentials (ERP) to passive limb movements and auditory stimuli were recorded, vibrotactile stimulation of different body parts resulted in no ERP response.

CONCLUSIONS: 

The results presented contradict the commonly accepted assumption that the EAS is the last remaining muscle under voluntary control and demonstrate complete loss of eye movements in CLIS. The eye muscle was shown to be the last muscle group under voluntary control. The findings suggest ALS as a multisystem disorder, even affecting afferent sensory pathways.

SIGNIFICANCE: 

Auditory and proprioceptive brain-computer-interface (BCI) systems are the only remaining communication channels in CLIS.

VL - 122 UR - http://www.ncbi.nlm.nih.gov/pubmed/20888292 IS - 5 ER - TY - JOUR T1 - A procedure for measuring latencies in brain-computer interfaces. JF - IEEE Trans Biomed Eng Y1 - 2010 A1 - Adam J Wilson A1 - Mellinger, Jürgen A1 - Gerwin Schalk A1 - Williams, Justin C KW - Brain KW - Computer Systems KW - Electroencephalography KW - Evoked Potentials KW - Humans KW - Models, Neurological KW - Reproducibility of Results KW - Signal Processing, Computer-Assisted KW - Time Factors KW - User-Computer Interface AB -

Brain-computer interface (BCI) systems must process neural signals with consistent timing in order to support adequate system performance. Thus, it is important to have the capability to determine whether a particular BCI configuration (i.e., hardware and software) provides adequate timing performance for a particular experiment. This report presents a method of measuring and quantifying different aspects of system timing in several typical BCI experiments across a range of settings, and presents comprehensive measures of expected overall system latency for each experimental configuration.

VL - 57 UR - http://www.ncbi.nlm.nih.gov/pubmed/20403781 IS - 7 ER - TY - Generic T1 - Effective brain-computer interfacing using BCI2000. T2 - Conf Proc IEEE Eng Med Biol Soc Y1 - 2009 A1 - Gerwin Schalk KW - Algorithms KW - Brain KW - Electrocardiography KW - Equipment Design KW - Equipment Failure Analysis KW - Rehabilitation KW - Reproducibility of Results KW - Sensitivity and Specificity KW - Signal Processing, Computer-Assisted KW - User-Computer Interface AB - To facilitate research and development in Brain-Computer Interface (BCI) research, we have been developing a general-purpose BCI system, called BCI2000, over the past nine years. This system has enjoyed a growing adoption in BCI and related areas and has been the basis for some of the most impressive studies reported to date. This paper gives an update on the status of this project by describing the principles of the BCI2000 system, its benefits, and impact on the field to date. JF - Conf Proc IEEE Eng Med Biol Soc VL - 2009 ER - TY - JOUR T1 - Evolution of brain-computer interfaces: going beyond classic motor physiology. JF - Neurosurg Focus Y1 - 2009 A1 - Leuthardt, E C A1 - Gerwin Schalk A1 - Roland, Jarod A1 - Rouse, Adam A1 - Moran, D KW - Brain KW - Cerebral Cortex KW - Humans KW - Man-Machine Systems KW - Motor Cortex KW - Movement KW - Movement Disorders KW - Neuronal Plasticity KW - Prostheses and Implants KW - Research KW - Signal Processing, Computer-Assisted KW - User-Computer Interface AB -

The notion that a computer can decode brain signals to infer the intentions of a human and then enact those intentions directly through a machine is becoming a realistic technical possibility. These types of devices are known as brain-computer interfaces (BCIs). The evolution of these neuroprosthetic technologies could have significant implications for patients with motor disabilities by enhancing their ability to interact and communicate with their environment. The cortical physiology most investigated and used for device control has been brain signals from the primary motor cortex. To date, this classic motor physiology has been an effective substrate for demonstrating the potential efficacy of BCI-based control. However, emerging research now stands to further enhance our understanding of the cortical physiology underpinning human intent and provide further signals for more complex brain-derived control. In this review, the authors report the current status of BCIs and detail the emerging research trends that stand to augment clinical applications in the future.

VL - 27 UR - http://www.ncbi.nlm.nih.gov/pubmed/19569892 IS - 1 ER - TY - JOUR T1 - Mapping broadband electrocorticographic recordings to two-dimensional hand trajectories in humans Motor control features. JF - Neural Netw Y1 - 2009 A1 - Gunduz, Aysegul A1 - Sanchez, Justin C A1 - Carney, Paul R A1 - Principe, Jose KW - Algorithms KW - Brain KW - Brain Mapping KW - Electrodes, Implanted KW - Electrodiagnosis KW - Epilepsy KW - Feasibility Studies KW - Hand KW - Humans KW - Linear Models KW - Motor Activity KW - Neural Networks (Computer) KW - Nonlinear Dynamics KW - Signal Processing, Computer-Assisted AB -

Brain-machine interfaces (BMIs) aim to translate the motor intent of locked-in patients into neuroprosthetic control commands. Electrocorticographical (ECoG) signals provide promising neural inputs to BMIs as shown in recent studies. In this paper, we utilize a broadband spectrum above the fast gamma ranges and systematically study the role of spectral resolution, in which the broadband is partitioned, on the reconstruction of the patients' hand trajectories. Traditionally, the power of ECoG rhythms (<200-300 Hz) has been computed in short duration bins and instantaneously and linearly mapped to cursor trajectories. Neither time embedding, nor nonlinear mappings have been previously implemented in ECoG neuroprosthesis. Herein, mapping of neural modulations to goal-oriented motor behavior is achieved via linear adaptive filters with embedded memory depths and as a novelty through echo state networks (ESNs), which provide nonlinear mappings without compromising training complexity or increasing the number of model parameters, with up to 85% correlation. Reconstructed hand trajectories are analyzed through spatial, spectral and temporal sensitivities. The superiority of nonlinear mappings in the cases of low spectral resolution and abundance of interictal activity is discussed.

VL - 22 UR - http://www.ncbi.nlm.nih.gov/pubmed/19647981 IS - 9 ER - TY - JOUR T1 - Overlap and refractory effects in a brain-computer interface speller based on the visual P300 event-related potential. JF - J Neural Eng Y1 - 2009 A1 - Martens, S M M A1 - Jeremy Jeremy Hill A1 - Farquhar, Jason A1 - Schölkopf, B KW - Algorithms KW - Brain KW - Cognition KW - Computer Simulation KW - Electroencephalography KW - Event-Related Potentials, P300 KW - Humans KW - Models, Neurological KW - Pattern Recognition, Automated KW - Photic Stimulation KW - Semantics KW - Signal Processing, Computer-Assisted KW - Task Performance and Analysis KW - User-Computer Interface KW - Writing AB -

We reveal the presence of refractory and overlap effects in the event-related potentials in visual P300 speller datasets, and we show their negative impact on the performance of the system. This finding has important implications for how to encode the letters that can be selected for communication. However, we show that such effects are dependent on stimulus parameters: an alternative stimulus type based on apparent motion suffers less from the refractory effects and leads to an improved letter prediction performance.

VL - 6 UR - http://www.ncbi.nlm.nih.gov/pubmed/19255462 IS - 2 ER - TY - JOUR T1 - Power spectrum analysis on the multiparameter electroencephalogram features of physiological mental fatigue. JF - Sheng Wu Yi Xue Gong Cheng Xue Za Zhi Y1 - 2009 A1 - Zhang, Ai-hua A1 - Zheng, Shi Dong A1 - Pei, Xiao-Mei A1 - Ouyang, Yi KW - Adult KW - Electroencephalography KW - Entropy KW - Humans KW - Male KW - Mental Fatigue KW - Signal Processing, Computer-Assisted KW - Young Adult AB -

The aim of this experiment is to find a feasible impersonal index for analyzing the physiological mental fatigue level. Three characteristic parameters, relative power in different rhythm, barycenter frequency and power spectral entropy, are extracted from two channels' electroencephalogram (EEG) under two physiological mental fatigue states. Then relationships between such three parameters and physiological mental fatigue are analyzed to explore whether they can be of use for detecting (or monitoring) the mental fatigue level. The experiment results show that the relative power, barycenter frequency and power spectral entropy of EEG exhibit strong correlation with physiological mental fatigue level. While physiological mental fatigue level increases, the relative power in theta, alpha and beta rhythms, barycenter frequency and power spectral entropy of EEG decrease, but the relative power in delta rhythm of EEG increases. The relative power in four rhythms, barycenter frequency and power spectral entropy of EEG reflect the change of physiological mental fatigue level sensitively, and may hopefully be used as indexes for detecting physiological mental fatigue level.

VL - 26 UR - http://www.ncbi.nlm.nih.gov/pubmed/19334577 IS - 1 ER - TY - JOUR T1 - A practical procedure for real-time functional mapping of eloquent cortex using electrocorticographic signals in humans. JF - Epilepsy Behav Y1 - 2009 A1 - Peter Brunner A1 - A L Ritaccio A1 - Lynch, Timothy M A1 - Emrich, Joseph F A1 - Adam J Wilson A1 - Williams, Justin C A1 - Aarnoutse, Erik J A1 - Ramsey, Nick F A1 - Leuthardt, E C A1 - H Bischof A1 - Gerwin Schalk KW - Adult KW - Brain Mapping KW - Cerebral Cortex KW - Electric Stimulation KW - Electrodes, Implanted KW - Electroencephalography KW - Epilepsy KW - Female KW - Humans KW - Male KW - Middle Aged KW - Practice Guidelines as Topic KW - Signal Processing, Computer-Assisted KW - Young Adult AB -

Functional mapping of eloquent cortex is often necessary prior to invasive brain surgery, but current techniques that derive this mapping have important limitations. In this article, we demonstrate the first comprehensive evaluation of a rapid, robust, and practical mapping system that uses passive recordings of electrocorticographic signals. This mapping procedure is based on the BCI2000 and SIGFRIED technologies that we have been developing over the past several years. In our study, we evaluated 10 patients with epilepsy from four different institutions and compared the results of our procedure with the results derived using electrical cortical stimulation (ECS) mapping. The results show that our procedure derives a functional motor cortical map in only a few minutes. They also show a substantial concurrence with the results derived using ECS mapping. Specifically, compared with ECS maps, a next-neighbor evaluation showed no false negatives, and only 0.46 and 1.10% false positives for hand and tongue maps, respectively. In summary, we demonstrate the first comprehensive evaluation of a practical and robust mapping procedure that could become a new tool for planning of invasive brain surgeries.

VL - 15 UR - http://www.ncbi.nlm.nih.gov/pubmed/19366638 IS - 3 ER - TY - JOUR T1 - Brain-computer interfaces (BCIs): Detection Instead of Classification. JF - J Neurosci Methods Y1 - 2008 A1 - Gerwin Schalk A1 - Peter Brunner A1 - Lester A Gerhardt A1 - H Bischof A1 - Jonathan Wolpaw KW - Adult KW - Algorithms KW - Brain KW - Brain Mapping KW - Electrocardiography KW - Electroencephalography KW - Humans KW - Male KW - Man-Machine Systems KW - Normal Distribution KW - Online Systems KW - Signal Detection, Psychological KW - Signal Processing, Computer-Assisted KW - Software Validation KW - User-Computer Interface AB -

Many studies over the past two decades have shown that people can use brain signals to convey their intent to a computer through brain-computer interfaces (BCIs). These devices operate by recording signals from the brain and translating these signals into device commands. They can be used by people who are severely paralyzed to communicate without any use of muscle activity. One of the major impediments in translating this novel technology into clinical applications is the current requirement for preliminary analyses to identify the brain signal features best suited for communication. This paper introduces and validates signal detection, which does not require such analysis procedures, as a new concept in BCI signal processing. This detection concept is realized with Gaussian mixture models (GMMs) that are used to model resting brain activity so that any change in relevant brain signals can be detected. It is implemented in a package called SIGFRIED (SIGnal modeling For Real-time Identification and Event Detection). The results indicate that SIGFRIED produces results that are within the range of those achieved using a common analysis strategy that requires preliminary identification of signal features. They indicate that such laborious analysis procedures could be replaced by merely recording brain signals during rest. In summary, this paper demonstrates how SIGFRIED could be used to overcome one of the present impediments to translation of laboratory BCI demonstrations into clinically practical applications.

VL - 167 UR - http://www.ncbi.nlm.nih.gov/pubmed/17920134 IS - 1 ER - TY - JOUR T1 - Extraction and localization of mesoscopic motor control signals for human ECoG neuroprosthetics. JF - J Neurosci Methods Y1 - 2008 A1 - Sanchez, Justin C A1 - Gunduz, Aysegul A1 - Carney, Paul R A1 - Principe, Jose KW - Adolescent KW - Biofeedback, Psychology KW - Brain Mapping KW - Cerebral Cortex KW - Electroencephalography KW - Epilepsies, Partial KW - Female KW - Hand KW - Humans KW - Magnetic Resonance Imaging KW - Physical Therapy Modalities KW - Psychomotor Performance KW - Signal Processing, Computer-Assisted KW - Spectrum Analysis KW - User-Computer Interface AB -

Electrocorticogram (ECoG) recordings for neuroprosthetics provide a mesoscopic level of abstraction of brain function between microwire single neuron recordings and the electroencephalogram (EEG). Single-trial ECoG neural interfaces require appropriate feature extraction and signal processing methods to identify and model in real-time signatures of motor events in spontaneous brain activity. Here, we develop the clinical experimental paradigm and analysis tools to record broadband (1Hz to 6kHz) ECoG from patients participating in a reaching and pointing task. Motivated by the significant role of amplitude modulated rate coding in extracellular spike based brain-machine interfaces (BMIs), we develop methods to quantify spatio-temporal intermittent increased ECoG voltages to determine if they provide viable control inputs for ECoG neural interfaces. This study seeks to explore preprocessing modalities that emphasize amplitude modulation across frequencies and channels in the ECoG above the level of noisy background fluctuations in order to derive the commands for complex, continuous control tasks. Preliminary experiments show that it is possible to derive online predictive models and spatially localize the generation of commands in the cortex for motor tasks using amplitude modulated ECoG.

VL - 167 UR - http://www.ncbi.nlm.nih.gov/pubmed/17582507 IS - 1 ER - TY - JOUR T1 - Voluntary brain regulation and communication with electrocorticogram signals. JF - Epilepsy Behav Y1 - 2008 A1 - Hinterberger, T. A1 - Widman, Guido A1 - Lal, T.N A1 - Jeremy Jeremy Hill A1 - Tangermann, Michael A1 - Rosenstiel, W. A1 - Schölkopf, B A1 - Elger, Christian A1 - Niels Birbaumer KW - Adult KW - Biofeedback, Psychology KW - Cerebral Cortex KW - Communication Aids for Disabled KW - Dominance, Cerebral KW - Electroencephalography KW - Epilepsies, Partial KW - Female KW - Humans KW - Imagination KW - Male KW - Middle Aged KW - Motor Activity KW - Motor Cortex KW - Signal Processing, Computer-Assisted KW - Software KW - Somatosensory Cortex KW - Theta Rhythm KW - User-Computer Interface KW - Writing AB -

Brain-computer interfaces (BCIs) can be used for communication in writing without muscular activity or for learning to control seizures by voluntary regulation of brain signals such as the electroencephalogram (EEG). Three of five patients with epilepsy were able to spell their names with electrocorticogram (ECoG) signals derived from motor-related areas within only one or two training sessions. Imagery of finger or tongue movements was classified with support-vector classification of autoregressive coefficients derived from the ECoG signals. After training of the classifier, binary classification responses were used to select letters from a computer-generated menu. Offline analysis showed increased theta activity in the unsuccessful patients, whereas the successful patients exhibited dominant sensorimotor rhythms that they could control. The high spatial resolution and increased signal-to-noise ratio in ECoG signals, combined with short training periods, may offer an alternative for communication in complete paralysis, locked-in syndrome, and motor restoration.

VL - 13 UR - http://www.ncbi.nlm.nih.gov/pubmed/18495541 IS - 2 ER - TY - JOUR T1 - Electrocorticographic Frequency Alteration Mapping: A Clinical Technique for Mapping the Motor Cortex. JF - Neurosurgery Y1 - 2007 A1 - Leuthardt, E C A1 - Miller, John W A1 - Nicholas R Anderson A1 - Gerwin Schalk A1 - Dowling, Joshua A1 - Miller, John W A1 - Moran, D A1 - Ojemann, J G KW - Adult KW - Biological Clocks KW - Brain Mapping KW - Electric Stimulation KW - Electrodes, Implanted KW - Electroencephalography KW - Female KW - Hand KW - Humans KW - Male KW - Middle Aged KW - Motor Cortex KW - Oscillometry KW - Signal Processing, Computer-Assisted KW - Tongue AB -

OBJECTIVE: 

Electrocortical stimulation (ECS) has been well established for delineating the eloquent cortex. However, ECS is still coarse and inefficient in delineating regions of the functional cortex and can be hampered by after-discharges. Given these constraints, an adjunct approach to defining the motor cortex is the use of electrocorticographic signal changes associated with active regions of the cortex. The broad range of frequency oscillations are categorized into two main groups with respect to the sensorimotor cortex: low and high frequency bands. The low frequency bands tend to show a power reduction with cortical activation, whereas the high frequency bands show power increases. These power changes associated with the activated cortex could potentially provide a powerful tool in delineating areas of the motor cortex. We explore electrocorticographic signal alterations as they occur with activated regions of the motor cortex, as well as its potential in clinical brain mapping applications.

METHODS: 

We evaluated seven patients who underwent invasive monitoring for seizure localization. Each patient had extraoperative ECS mapping to identify the motor cortex. All patients also performed overt hand and tongue motor tasks to identify associated frequency power changes in regard to location and degree of concordance with ECS results that localized either hand or tongue motor function.

RESULTS: 

The low frequency bands had a high sensitivity (88.9-100%) and a lower specificity (79.0-82.6%) for identifying electrodes with either hand or tongue ECS motor responses. The high frequency bands had a lower sensitivity (72.7-88.9%) and a higher specificity (92.4-94.9%) in correlation with the same respective ECS positive electrodes.

CONCLUSION: 

The concordance between stimulation and spectral power changes demonstrate the possible utility of electrocorticographic frequency alteration mapping as an adjunct method to improve the efficiency and resolution of identifying the motor cortex.

VL - 60 UR - http://www.ncbi.nlm.nih.gov/pubmed/17415162 IS - 4 Suppl 2 ER - TY - JOUR T1 - An MEG-based brain-computer interface (BCI). JF - Neuroimage Y1 - 2007 A1 - Mellinger, Jürgen A1 - Gerwin Schalk A1 - Christoph Braun A1 - Preissl, Hubert A1 - Rosenstiel, W. A1 - Niels Birbaumer A1 - Kübler, A. KW - Adult KW - Algorithms KW - Artifacts KW - Brain KW - Electroencephalography KW - Electromagnetic Fields KW - Electromyography KW - Feedback KW - Female KW - Foot KW - Hand KW - Head Movements KW - Humans KW - Magnetic Resonance Imaging KW - Magnetoencephalography KW - Male KW - Movement KW - Principal Component Analysis KW - Signal Processing, Computer-Assisted KW - User-Computer Interface AB -

Brain-computer interfaces (BCIs) allow for communicating intentions by mere brain activity, not involving muscles. Thus, BCIs may offer patients who have lost all voluntary muscle control the only possible way to communicate. Many recent studies have demonstrated that BCIs based on electroencephalography(EEG) can allow healthy and severely paralyzed individuals to communicate. While this approach is safe and inexpensive, communication is slow. Magnetoencephalography (MEG) provides signals with higher spatiotemporal resolution than EEG and could thus be used to explore whether these improved signal properties translate into increased BCI communication speed. In this study, we investigated the utility of an MEG-based BCI that uses voluntary amplitude modulation of sensorimotor mu and beta rhythms. To increase the signal-to-noise ratio, we present a simple spatial filtering method that takes the geometric properties of signal propagation in MEG into account, and we present methods that can process artifacts specifically encountered in an MEG-based BCI. Exemplarily, six participants were successfully trained to communicate binary decisions by imagery of limb movements using a feedback paradigm. Participants achieved significant mu rhythm self control within 32 min of feedback training. For a subgroup of three participants, we localized the origin of the amplitude modulated signal to the motor cortex. Our results suggest that an MEG-based BCI is feasible and efficient in terms of user training.

VL - 36 UR - http://www.ncbi.nlm.nih.gov/pubmed/17475511 IS - 3 ER - TY - JOUR T1 - Narrowband vs. broadband phase synchronization analysis applied to independent components of ictal and interictal EEG. JF - Conf Proc IEEE Eng Med Biol Soc Y1 - 2007 A1 - Disha Gupta A1 - Christopher J James KW - Algorithms KW - Electroencephalography KW - Humans KW - Predictive Value of Tests KW - Seizures KW - Signal Processing, Computer-Assisted AB - This paper presents a comparison of the use of broadband and narrow band signals for phase synchronization analysis as applied to Independent Components of ictal and interictal scalp EEG in the context of seizure onset detection and prediction. Narrow band analysis for phase synchronization is found to be better performed in the present context than the broad band signal analysis. It has been observed that the phase synchronization of Independent Components in a narrow band (particularly the Gamma band) shows a prominent trend of increasing and decreasing synchronization at seizure onset near the epileptogenic area (spatially). This information is not always found to be consistent in analysis with the raw EEG signals, which may show spurious synchronization happening due to volume conduction effects. These observations lead us to believe that tracking changes in phase synchronization of narrow band activity, on continuous data records will be of great value in the context of seizure prediction. VL - 2007 UR - http://www.ncbi.nlm.nih.gov/pubmed/18002842 ER - TY - JOUR T1 - Analysis of the correlation between local field potentials and neuronal firing rate in the motor cortex. JF - Conf Proc IEEE Eng Med Biol Soc Y1 - 2006 A1 - Wang, Yiwen A1 - Sanchez, Justin C A1 - Principe, Jose A1 - Mitzelfelt, Jeremiah D A1 - Gunduz, Aysegul KW - Action Potentials KW - Animals KW - Brain KW - Brain Mapping KW - Electric Stimulation KW - Electrodes KW - Evoked Potentials, Motor KW - Male KW - Models, Statistical KW - Motor Cortex KW - Neurons KW - Rats KW - Rats, Sprague-Dawley KW - Signal Processing, Computer-Assisted KW - Synaptic Transmission AB -

Neuronal firing rate has been the signal of choice for invasive motor brain machine interfaces (BMI). The use of local field potentials (LFP) in BMI experiments may provide additional dendritic information about movement intent and may improve performance. Here we study the time-varying amplitude modulated relationship between local field potentials (LFP) and single unit activity (SUA) in the motor cortex. We record LFP and SUA in the primary motor cortex of rats trained to perform a lever pressing task, and evaluate the correlation between pairs of peri-event time histograms (PETH) and movement evoked local field potentials (mEP) at the same electrode. Three different correlation coefficients were calculated and compared between the neuronal PETH and the magnitude and power of the mEP. Correlation as high as 0.7 for some neurons occurred between the PETH and the mEP magnitude. As expected, the correlations between the single trial LFP and SUV are much lower due to the inherent variability of both signals.

VL - 1 UR - http://www.ncbi.nlm.nih.gov/pubmed/17946745 ER - TY - JOUR T1 - Temporal transformation of multiunit activity improves identification of single motor units. JF - J Neurosci Methods Y1 - 2002 A1 - Gerwin Schalk A1 - Jonathan S. Carp A1 - Jonathan Wolpaw KW - Action Potentials KW - Animals KW - Electromyography KW - H-Reflex KW - Motor Neurons KW - Muscle, Skeletal KW - Rats KW - Signal Processing, Computer-Assisted AB -

This report describes a temporally based method for identifying repetitive firing of motor units. This approach is ideally suited to spike trains with negative serially correlated inter-spike intervals (ISIs). It can also be applied to spike trains in which ISIs exhibit little serial correlation if their coefficient of variation (COV) is sufficiently low. Using a novel application of the Hough transform, this method (i.e. the modified Hough transform (MHT)) maps motor unit action potential (MUAP) firing times into a feature space with ISI and offset (defined as the latency from an arbitrary starting time to the first MUAP in the train) as dimensions. Each MUAP firing time corresponds to a pattern in the feature space that represents all possible MUAP trains with a firing at that time. Trains with stable ISIs produce clusters in the feature space, whereas randomly firing trains do not. The MHT provides a direct estimate of mean firing rate and its variability for the entire data segment, even if several individual MUAPs are obscured by firings from other motor units. Addition of this method to a shape-based classification approach markedly improved rejection of false positives using simulated data and identified spike trains in whole muscle electromyographic recordings from rats. The relative independence of the MHT from the need to correctly classify individual firings permits a global description of stable repetitive firing behavior that is complementary to shape-based approaches to MUAP classification.

VL - 114 UR - http://www.ncbi.nlm.nih.gov/pubmed/11850043 IS - 1 ER - TY - JOUR T1 - Brain-computer interface technology: a review of the first international meeting. JF - IEEE Trans Rehabil Eng Y1 - 2000 A1 - Jonathan Wolpaw A1 - Niels Birbaumer A1 - Heetderks, W J A1 - Dennis J. McFarland A1 - Peckham, P H A1 - Gerwin Schalk A1 - Emanuel Donchin A1 - Quatrano, L A A1 - Robinson, C J A1 - Theresa M Vaughan KW - Algorithms KW - Cerebral Cortex KW - Communication Aids for Disabled KW - Disabled Persons KW - Electroencephalography KW - Evoked Potentials KW - Humans KW - Neuromuscular Diseases KW - Signal Processing, Computer-Assisted KW - User-Computer Interface AB -

Over the past decade, many laboratories have begun to explore brain-computer interface (BCI) technology as a radically new communication option for those with neuromuscular impairments that prevent them from using conventional augmentative communication methods. BCI's provide these users with communication channels that do not depend on peripheral nerves and muscles. This article summarizes the first international meeting devoted to BCI research and development. Current BCI's use electroencephalographic (EEG) activity recorded at the scalp or single-unit activity recorded from within cortex to control cursor movement, select letters or icons, or operate a neuroprosthesis. The central element in each BCI is a translation algorithm that converts electrophysiological input from the user into output that controls external devices. BCI operation depends on effective interaction between two adaptive controllers, the user who encodes his or her commands in the electrophysiological input provided to the BCI, and the BCI which recognizes the commands contained in the input and expresses them in device control. Current BCI's have maximum information transfer rates of 5-25 b/min. Achievement of greater speed and accuracy depends on improvements in signal processing, translation algorithms, and user training. These improvements depend on increased interdisciplinary cooperation between neuroscientists, engineers, computer programmers, psychologists, and rehabilitation specialists, and on adoption and widespread application of objective methods for evaluating alternative methods. The practical use of BCI technology depends on the development of appropriate applications, identification of appropriate user groups, and careful attention to the needs and desires of individual users. BCI research and development will also benefit from greater emphasis on peer-reviewed publications, and from adoption of standard venues for presentations and discussion.

VL - 8 UR - http://www.ncbi.nlm.nih.gov/pubmed/10896178 IS - 2 ER - TY - JOUR T1 - Spatiotemporal irregularities of spiral wave activity in isolated ventricular muscle. JF - J Electrocardiol Y1 - 1992 A1 - Davidenko, J M A1 - Pertsov, A V A1 - Salomonsz, R A1 - Baxter, Bill A1 - Jalife, J KW - Animals KW - Cardiac Pacing, Artificial KW - Fluorescent Dyes KW - Heart Conduction System KW - Membrane Potentials KW - Optics and Photonics KW - Pericardium KW - Signal Processing, Computer-Assisted KW - Tachycardia KW - Ventricular Function AB -

Voltage-sensitive dyes and high resolution optical mapping were used to analyze the characteristics of spiral waves of excitation in isolated ventricular myocardium. In addition, analytical techniques, which have been previously used in the study of the characteristics of spiral waves in chemical reactions, were applied to determine the voltage structure of the center of the rotating activity (ie, the core). During stable spiral wave activity local activation occurs in a periodic fashion (ie, 1:1 stimulus: response activation ratio) throughout the preparation, except at the core, which is a small elongated area where the activity is of low voltage and the activation ratio is 1:0. The voltage amplitude increases gradually from the center of the core to the periphery. In some cases, however, regular activation patterns at the periphery may coexist with irregular local activation patterns near the core. Such a spatiotemporal irregularity is attended by variations in the core size and shape and results from changes in the core position. The authors conclude that functionally determined reentrant activity in the heart may be the result of spiral waves of propagation and that local spatiotemporal irregularities in the activation pattern are the result of changes in the core position.

VL - 24 Suppl UR - http://www.ncbi.nlm.nih.gov/pubmed/1552240 ER -