@article {4366, title = {Breathable, large-area epidermal electronic systems for recording electromyographic activity during operant conditioning of H-reflex.}, journal = {Biosens Bioelectron}, volume = {165}, year = {2020}, month = {10/2020}, pages = {112404}, abstract = {

Operant conditioning of Hoffmann{\textquoteright}s reflex (H-reflex) is a non-invasive and targeted therapeutic intervention for patients with movement disorders following spinal cord injury. The reflex-conditioning protocol uses electromyography (EMG) to measure reflexes from specific muscles elicited using transcutaneous electrical stimulation. Despite recent advances in wearable electronics, existing EMG systems that measure muscle activity for operant conditioning of spinal reflexes still use rigid metal electrodes with conductive gels and aggressive adhesives, while requiring precise positioning to ensure reliability of data across experimental sessions. Here, we present the first large-area epidermal electronic system (L-EES) and demonstrate its use in every step of the reflex-conditioning protocol. The L-EES is a stretchable and breathable composite of nanomembrane electrodes (16 electrodes in a four by four array), elastomer, and fabric. The nanomembrane electrode array enables EMG recording from a large surface area on the skin and the breathable elastomer with fabric is biocompatible and comfortable for patients. We show that L-EES can record direct muscle responses (M-waves) and H-reflexes, both of which are comparable to those recorded using conventional EMG recording systems. In addition, L-EES may improve the reflex-conditioning protocol; it has potential to automatically optimize EMG electrode positioning, which may reduce setup time and error across experimental sessions.

}, keywords = {Biosensing Techniques, Conditioning, Operant, Electronics, H-Reflex, Humans, Reproducibility of Results}, issn = {1873-4235}, doi = {10.1016/j.bios.2020.112404}, author = {Kwon, Young-Tae and Norton, James J S and Cutrone, Andrew and Lim, Hyo-Ryoung and Kwon, Shinjae and Choi, Jeongmoon J and Kim, Hee Seok and Jang, Young C and Wolpaw, Jonathan R and Yeo, Woon-Hong} } @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 {3400, title = {Dichotic and dichoptic digit perception in normal adults.}, journal = {J Am Acad Audiol}, volume = {22}, year = {2011}, month = {06/2011}, pages = {332-41}, abstract = {BACKGROUND: Verbally based dichotic-listening experiments and reproduction-mediated response-selection strategies have been used for over four decades to study perceptual/cognitive aspects of auditory information processing and make inferences about hemispheric asymmetries and language lateralization in the brain. Test procedures using dichotic digits have also been used to assess for disorders of auditory processing. However, with this application, limitations exist and paradigms need to be developed to improve specificity of the diagnosis. Use of matched tasks in multiple sensory modalities is a logical approach to address this issue. Herein, we use dichotic listening and dichoptic viewing of visually presented digits for making this comparison. PURPOSE: To evaluate methodological issues involved in using matched tasks of dichotic listening and dichoptic viewing in normal adults. RESEARCH DESIGN: A multivariate assessment of the effects of modality (auditory vs. visual), digit-span length (1-3 pairs), response selection (recognition vs. reproduction), and ear/visual hemifield of presentation (left vs. right) on dichotic and dichoptic digit perception. STUDY SAMPLE: Thirty adults (12 males, 18 females) ranging in age from 18 to 30 yr with normal hearing sensitivity and normal or corrected-to-normal visual acuity. DATA COLLECTION AND ANALYSIS: A computerized, custom-designed program was used for all data collection and analysis. A four-way repeated measures analysis of variance (ANOVA) evaluated the effects of modality, digit-span length, response selection, and ear/visual field of presentation. RESULTS: The ANOVA revealed that performances on dichotic listening and dichoptic viewing tasks were dependent on complex interactions between modality, digit-span length, response selection, and ear/visual hemifield of presentation. Correlation analysis suggested a common effect on overall accuracy of performance but isolated only an auditory factor for a laterality index. CONCLUSIONS: The variables used in this experiment affected performances in the auditory modality to a greater extent than in the visual modality. The right-ear advantage observed in the dichotic-digits task was most evident when reproduction mediated response selection was used in conjunction with three-digit pairs. This effect implies that factors such as "speech related output mechanisms" and digit-span length (working memory) contribute to laterality effects in dichotic listening performance with traditional paradigms. Thus, the use of multiple-digit pairs to avoid ceiling effects and the application of verbal reproduction as a means of response selection may accentuate the role of nonperceptual factors in performance. Ideally, tests of perceptual abilities should be relatively free of such effects.}, keywords = {Adolescent, Adult, Auditory Perception, Dichotic Listening Tests, Female, Functional Laterality, Humans, Male, Recognition (Psychology), Reference Values, Reproducibility of Results, Task Performance and Analysis, Visual Perception, Young Adult}, issn = {1050-0545}, doi = {10.3766/jaaa.22.6.3}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21864471}, author = {Lawfield, Angela and Dennis J. McFarland and Cacace, Anthony T} } @article {2195, title = {A procedure for measuring latencies in brain-computer interfaces.}, journal = {IEEE Trans Biomed Eng}, volume = {57}, year = {2010}, month = {06/2010}, pages = {1785-97}, abstract = {

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.

}, keywords = {Brain, Computer Systems, Electroencephalography, Evoked Potentials, Humans, Models, Neurological, Reproducibility of Results, Signal Processing, Computer-Assisted, Time Factors, User-Computer Interface}, issn = {1558-2531}, doi = {10.1109/TBME.2010.2047259}, url = {http://www.ncbi.nlm.nih.gov/pubmed/20403781}, author = {Adam J Wilson and Mellinger, J{\"u}rgen and Gerwin Schalk and Williams, Justin C} } @proceedings {2241, title = {Detection of spontaneous class-specific visual stimuli with high temporal accuracy in human electrocorticography.}, volume = {2009}, year = {2009}, month = {2009}, pages = {6465-8}, abstract = {Most brain-computer interface classification experiments from electrical potential recordings have been focused on the identification of classes of stimuli or behavior where the timing of experimental parameters is known or pre-designated. Real world experience, however, is spontaneous, and to this end we describe an experiment predicting the occurrence, timing, and types of visual stimuli perceived by a human subject from electrocorticographic recordings. All 300 of 300 presented stimuli were correctly detected, with a temporal precision of order 20 ms. The type of stimulus (face/house) was correctly identified in 95\% of these cases. There were approximately 20 false alarm events, corresponding to a late 2nd neuronal response to a previously identified event.}, keywords = {Algorithms, Electrocardiography, Evoked Potentials, Visual, Humans, Male, Pattern Recognition, Automated, Pattern Recognition, Visual, Photic Stimulation, Reproducibility of Results, Sensitivity and Specificity, User-Computer Interface, Visual Cortex}, issn = {1557-170X}, doi = {10.1109/IEMBS.2009.5333546}, author = {Miller, John W and Hermes, Dora and Gerwin Schalk and Ramsey, Nick F and Jagadeesh, Bharathi and den Nijs, Marcel and Ojemann, J G and Rao, Rajesh P N} } @proceedings {2243, title = {Effective brain-computer interfacing using BCI2000.}, volume = {2009}, year = {2009}, month = {2009}, pages = {5498-501}, abstract = {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.}, keywords = {Algorithms, Brain, Electrocardiography, Equipment Design, Equipment Failure Analysis, Rehabilitation, Reproducibility of Results, Sensitivity and Specificity, Signal Processing, Computer-Assisted, User-Computer Interface}, issn = {1557-170X}, doi = {10.1109/IEMBS.2009.5334558}, author = {Gerwin Schalk} } @article {2845, title = {Seizure prediction for epilepsy using a multi-stage phase synchrony based system.}, journal = {Conf Proc IEEE Eng Med Biol Soc}, volume = {2009}, year = {2009}, month = {09/2009}, pages = {25-8}, abstract = {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.}, keywords = {Algorithms, Artificial Intelligence, Diagnosis, Computer-Assisted, Electroencephalography, Epilepsy, Humans, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity}, issn = {1557-170X}, doi = {10.1109/IEMBS.2009.5334898}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19965104}, author = {Christopher J James and Disha Gupta} } @article {2107, title = {Electrocorticographic interictal spike removal via denoising source separation for improved neuroprosthesis control.}, journal = {Conf Proc IEEE Eng Med Biol Soc}, volume = {2008}, year = {2008}, month = {08/2008}, pages = {5224-7}, abstract = {

Electrocorticographic (ECoG) neuroprosthesis is a promising area of research that could provide channels of communication and control for patients who have lost their motor functions due to damage to the nervous system. However, implantation of subdural electrodes are clinically restricted to diagnostics of pre-surgical epileptic patients. Hence, interictal activity is present in the recordings across various areas of the sensorimotor cortex and suppresses the amplitude modulated features extracted to model hand trajectories. Denoising source separation is a recently introduced framework which extracts hidden structures of interest within the\ data\ through denoising the source estimates with filters designed around prior knowledge on the observations. Herein, we exploit the high amplitude quasiperiodic nature of the observed interictal spikes and show that removal of the interictal activity improves linear prediction of hand trajectories.

}, keywords = {Algorithms, Artifacts, Diagnosis, Computer-Assisted, Electroencephalography, Epilepsy, Evoked Potentials, Motor, Motor Cortex, Reproducibility of Results, Sensitivity and Specificity, User-Computer Interface}, issn = {1557-170X}, doi = {10.1109/IEMBS.2008.4650392}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19163895}, author = {Gunduz, Aysegul and Sanchez, Justin C and Principe, Jose} } @article {2187, title = {Real-time detection of event-related brain activity.}, journal = {Neuroimage}, volume = {43}, year = {2008}, month = {11/2008}, pages = {245-9}, abstract = {

The complexity and inter-individual variation of\ brain\ signals impedes real-time detection of events in raw signals. To convert these complex signals into results that can be readily understood, current approaches usually apply statistical methods to data from known conditions after all data have been collected. The capability to provide meaningful visualization of complex\ brain\ signals without the requirement to initially collect data from all conditions would provide a new tool, essentially a new imaging technique, that would open up new avenues for the study of\ brain\ function. Here we show that a new analysis approach, called SIGFRIED, can overcome this serious limitation of current methods. SIGFRIED can visualize\ brain\ signal changes without requiring prior data collection from all conditions. This capacity is particularly well suited to applications in which comprehensive prior data collection is impossible or impractical, such as intraoperative localization of cortical function or detection of epileptic seizures.

}, keywords = {Adult, Algorithms, Brain Mapping, Computer Systems, Diagnosis, Computer-Assisted, Electroencephalography, Epilepsy, Evoked Potentials, Female, Humans, Male, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2008.07.037}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18718544}, author = {Gerwin Schalk and Leuthardt, E C and Peter Brunner and Ojemann, J G and Lester A Gerhardt and Jonathan Wolpaw} } @article {2846, title = {Space-time ICA versus Ensemble ICA for ictal EEG analysis with component differentiation via Lempel-Ziv complexity.}, journal = {Conf Proc IEEE Eng Med Biol Soc}, volume = {08/2007}, year = {2007}, month = {2007}, pages = {5473-6}, abstract = {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.}, keywords = {Algorithms, Artificial Intelligence, Diagnosis, Computer-Assisted, Electroencephalography, Epilepsy, Humans, Pattern Recognition, Automated, Principal Component Analysis, Reproducibility of Results, Sensitivity and Specificity}, issn = {1557-170X}, doi = {10.1109/IEMBS.2007.4353584}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18003250}, author = {Christopher J James and Ab{\'a}solo, Daniel and Disha Gupta} } @article {2153, title = {Multi-channel linear descriptors for event-related EEG collected in brain computer interface.}, journal = {J Neural Eng}, volume = {3}, year = {2006}, month = {03/2006}, pages = {52-8}, abstract = {

By three multi-channel linear descriptors, i.e. spatial\ complexity\ (omega), field power (sigma) and frequency of field changes (phi),\ event-relatedEEGdata\ within 8-30 Hz were investigated during imagination of left or right hand movement. Studies on the\ event-relatedEEGdata\ indicate that a two-channel version of omega, sigma and phi could reflect the antagonistic ERD/ERS patterns over contralateral and ipsilateral areas and also characterize different phases of the changing brain states in the\ event-related\ paradigm. Based on the selective two-channel linear descriptors, the left and right hand motor imagery tasks are classified to obtain satisfactory results, which testify the validity of the three linear descriptors omega, sigma and phi for characterizing\ event-relatedEEG. The preliminary results show that omega, sigma together with phi have good separability for left and right hand motor imagery tasks, which could be considered for classification of two classes of\ EEG\ patterns in the application of brain computer interfaces.

}, keywords = {Algorithms, Electroencephalography, Evoked Potentials, Motor, Humans, Imagination, Motor Cortex, Movement, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, User-Computer Interface}, issn = {1741-2560}, doi = {10.1088/1741-2560/3/1/006}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16510942}, author = {Pei, Xiao-Mei and Zheng, Shi Dong and Xu, Jin and Bin, Guang-yu and Zuoguan Wang} } @article {2167, title = {The BCI Competition 2003: Progress and perspectives in detection and discrimination of EEG single trials.}, journal = {IEEE Trans Biomed Eng}, volume = {51}, year = {2004}, month = {06/2004}, pages = {1044-51}, abstract = {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.}, keywords = {Adult, Algorithms, Amyotrophic Lateral Sclerosis, Artificial Intelligence, Brain, Cognition, Databases, Factual, Electroencephalography, Evoked Potentials, Humans, Reproducibility of Results, Sensitivity and Specificity, User-Computer Interface}, issn = {0018-9294}, doi = {10.1109/TBME.2004.826692}, author = {Benjamin Blankertz and M{\"u}ller, Klaus-Robert and Curio, Gabriel and Theresa M Vaughan and Gerwin Schalk and Jonathan Wolpaw and Schl{\"o}gl, Alois and Neuper, Christa and Pfurtscheller, Gert and Hinterberger, T. and Schr{\"o}der, Michael and Niels Birbaumer} } @article {3148, title = {Factor analysis in CAPD and the "unimodal" test battery: do we have a model that will satisfy?.}, journal = {American journal of audiology}, volume = {11}, year = {2002}, month = {06/2002}, pages = {7{\textendash}9; author reply 9-12}, keywords = {Reproducibility of Results}, issn = {1059-0889}, doi = {10.1044/1059-0889(2002/ltr01)}, url = {http://www.ncbi.nlm.nih.gov/pubmed/12227358}, author = {Dennis J. McFarland and Anthony T. Cacace} }