Novel multivariate methods to track frequency shifts of neural oscillations in EEG/MEG recordings.

Instantaneous and peak frequency changes in neural oscillations have been linked to many perceptual, motor, and cognitive processes. Yet, the majority of such studies have been performed in sensor space and only occasionally in source space. Furthermore, both terms have been used interchangeably in the literature, although they do not reflect the same aspect of neural oscillations. In this paper, we discuss the relation between instantaneous frequency, peak frequency, and local frequency, the latter also known as spectral centroid. Furthermore, we propose and validate three different methods to extract source signals from multichannel data whose (instantaneous, local, or peak) frequency estimate is maximally correlated to an experimental variable of interest. Results show that the local frequency might be a better estimate of frequency variability than instantaneous frequency under conditions with low signal-to-noise ratio. Additionally, the source separation methods based on local and peak frequency estimates, called LFD and PFD respectively, provide more stable estimates than the decomposition based on instantaneous frequency. In particular, LFD and PFD are able to recover the sources of interest in simulations performed with a realistic head model, providing higher correlations with an experimental variable than multiple linear regression. Finally, we also tested all decomposition methods on real EEG data from a steady-state visual evoked potential paradigm and show that the recovered sources are located in areas similar to those previously reported in other studies, thus providing further validation of the proposed methods.

Improving motor imagery classification during induced motor perturbations.

Objective.Motor imagery is the mental simulation of movements. It is a common paradigm to design brain-computer interfaces (BCIs) that elicits the modulation of brain oscillatory activity similar to real, passive and induced movements. In this study, we used peripheral stimulation to provoke movements of one limb during the performance of motor imagery tasks. Unlike other works, in which induced movements are used to support the BCI operation, our goal was to test and improve the robustness of motor imagery based BCI systems to perturbations caused by artificially generated movements.Approach.We performed a BCI session with ten participants who carried out motor imagery of three limbs. In some of the trials, one of the arms was moved by neuromuscular stimulation. We analysed 2-class motor imagery classifications with and without movement perturbations. We investigated the performance decrease produced by these disturbances and designed different computational strategies to attenuate the observed classification accuracy drop.Main results.When the movement was induced in a limb not coincident with the motor imagery classes, extracting oscillatory sources of the movement imagination tasks resulted in BCI performance being similar to the control (undisturbed) condition; when the movement was induced in a limb also involved in the motor imagery tasks, the performance drop was significantly alleviated by spatially filtering out the neural noise caused by the stimulation. We also show that the loss of BCI accuracy was accompanied by weaker power of the sensorimotor rhythm. Importantly, this residual power could be used to predict whether a BCI user will perform with sufficient accuracy under the movement disturbances.Significance.We provide methods to ameliorate and even eliminate motor related afferent disturbances during the performance of motor imagery tasks. This can help improving the reliability of current motor imagery based BCI systems.

Canonical maximization of coherence: A novel tool for investigation of neuronal interactions between two datasets.

Synchronization between oscillatory signals is considered to be one of the main mechanisms through which neuronal populations interact with each other. It is conventionally studied with mass-bivariate measures utilizing either sensor-to-sensor or voxel-to-voxel signals. However, none of these approaches aims at maximizing synchronization, especially when two multichannel datasets are present. Examples include cortico-muscular coherence (CMC), cortico-subcortical interactions or hyperscanning (where electroencephalographic EEG/magnetoencephalographic MEG activity is recorded simultaneously from two or more subjects). For all of these cases, a method which could find two spatial projections maximizing the strength of synchronization would be desirable. Here we present such method for the maximization of coherence between two sets of EEG/MEG/EMG (electromyographic)/LFP (local field potential) recordings. We refer to it as canonical Coherence (caCOH). caCOH maximizes the absolute value of the coherence between the two multivariate spaces in the frequency domain. This allows very fast optimization for many frequency bins. Apart from presenting details of the caCOH algorithm, we test its efficacy with simulations using realistic head modelling and focus on the application of caCOH to the detection of cortico-muscular coherence. For this, we used diverse multichannel EEG and EMG recordings and demonstrate the ability of caCOH to extract complex patterns of CMC distributed across spatial and frequency domains. Finally, we indicate other scenarios where caCOH can be used for the extraction of neuronal interactions.

Enhancing sensorimotor BCI performance with assistive afferent activity: An online evaluation.

An important goal in Brain-Computer Interfacing (BCI) is to find and enhance procedural strategies for users for whom BCI control is not sufficiently accurate. To address this challenge, we conducted offline analyses and online experiments to test whether the classification of different types of motor imagery could be improved when the training of the classifier was performed on the data obtained with the assistive muscular stimulation below the motor threshold. 10 healthy participants underwent three different types of experimental conditions: a) Motor imagery (MI) of hands and feet b) sensory threshold neuromuscular electrical stimulation (STM) of hands and feet while resting and c) sensory threshold neuromuscular electrical stimulation during performance of motor imagery (BOTH). Also, another group of 10 participants underwent conditions a) and c). Then, online experiments with 15 users were performed. These subjects received neurofeedback during MI using classifiers calibrated either on MI or BOTH data recorded in the same experiment. Offline analyses showed that decoding MI alone using a classifier based on BOTH resulted in a better BCI accuracy compared to using a classifier based on MI alone. Online experiments confirmed accuracy improvement of MI alone being decoded with the classifier trained on BOTH data. In addition, we observed that the performance in MI condition could be predicted on the basis of a more pronounced connectivity within sensorimotor areas in the frequency bands providing the best performance in BOTH. These finding might offer a new avenue for training SMR-based BCI systems particularly for users having difficulties to achieve efficient BCI control. It might also be an alternative strategy for users who cannot perform real movements but still have remaining afferent pathways (e.g., ALS and stroke patients).

Neuromuscular electrical stimulation induced brain patterns to decode motor imagery.

OBJECTIVE: Regardless of the paradigm used to implement a brain-computer interface (BCI), all systems suffer from BCI-inefficiency. In the case of patients the inefficiency can be high. Some solutions have been proposed to overcome this problem, however they have not been completely successful yet. METHODS: EEG from 10 healthy users was recorded during neuromuscular electrical stimulation (NMES) of hands and feet and during motor imagery (MI) of the same limbs. Features and classifiers were computed using part of these data to decode MI. RESULTS: Offline analyses showed that it was possible to decode MI using a classifier based on afferent patterns induced by NMES and even infer a better model than with MI data. CONCLUSION: Afferent NMES motor patterns can support the calibration of BCI systems and be used to decode MI. SIGNIFICANCE: This finding might be a new way to train sensorimotor rhythm (SMR) based BCI systems for healthy users having difficulties to attain BCI control. It might also be an alternative to train MI-based BCIs for users who cannot perform real movements but have remaining afferents (ALS, stroke patients).

Toward unsupervised adaptation of LDA for brain-computer interfaces.

There is a step of significant difficulty experienced by brain-computer interface (BCI) users when going from the calibration recording to the feedback application. This effect has been previously studied and a supervised adaptation solution has been proposed. In this paper, we suggest a simple unsupervised adaptation method of the linear discriminant analysis (LDA) classifier that effectively solves this problem by counteracting the harmful effect of nonclass-related nonstationarities in electroencephalography (EEG) during BCI sessions performed with motor imagery tasks. For this, we first introduce three types of adaptation procedures and investigate them in an offline study with 19 datasets. Then, we select one of the proposed methods and analyze it further. The chosen classifier is offline tested in data from 80 healthy users and four high spinal cord injury patients. Finally, for the first time in BCI literature, we apply this unsupervised classifier in online experiments. Additionally, we show that its performance is significantly better than the state-of-the-art supervised approach.

Combining Brain-Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges.

In recent years, new research has brought the field of electroencephalogram (EEG)-based brain-computer interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely, "Communication and Control", "Motor Substitution", "Entertainment", and "Motor Recovery". We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users' mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices.

Incidence of cancer in the general population and in patients with or without atopic dermatitis in the U.K.

BACKGROUND: Atopic dermatitis (AD) affects approximately 20% of children and 1-3% of adults in developed countries. OBJECTIVE: To study the incidence of cancer in patients with AD in the U.K. general population. METHODS: We conducted a follow-up study in the U.K. using The Health Improvement Network (THIN) database. We calculated the incidence rate (IR) of the first occurrence of overall cancer, lymphoma, melanoma and nonmelanoma skin cancer (NMSC) in the general population, in patients with AD and in individuals without AD. In addition we calculated the IR ratio (IRR) of overall cancer and subtypes of cancer in patients with AD vs. those without. RESULTS: The study population included 4,518,131 patients [2,336,230 (51·7%) female]. There were 129,972 subjects [68,688 (52·8%) female] with a diagnosis of cancer (excluding NMSC). The IR (per 10,000 person-years) of cancer (excluding NMSC) was 42·41 [95% confidence interval (CI) 42·18-42·64]; of lymphoma 1·70 (95% CI 1·65-1·74); of skin melanoma 1·71 (95% CI 1·67-1·76) and of NMSC 11·76 (95% CI 11·64-11·88). The age- and sex-adjusted IRR for cancer (excluding NMSC) was 1·49 (95% CI 1·39-1·61); for lymphoma 2·21 (95% CI 1·65-2·98); for melanoma 1·74 (95% CI 1·25-2·41); and for NMSC 1·46 (95% CI 1·27-1·69). CONCLUSIONS: Our results indicate an increased incidence of cancer overall as well as of specific cancer subtypes, including lymphoma, in patients with AD. Further studies are needed to disentangle the effects of treatment for AD from AD itself.

Omalizumab in children with inadequately controlled severe allergic (IgE-mediated) asthma.

BACKGROUND: Many children with severe persistent allergic (IgE-mediated) asthma remain inadequately controlled despite treatment with high-dose inhaled corticosteroids (ICS) plus a long-acting beta(2)-agonist (LABA). RESEARCH AND DESIGN METHODS: This pre-specified analysis of a randomized, double-blind, placebo-controlled trial evaluated the efficacy and safety of omalizumab in children (6-<12 years) with perennial allergen sensitivity, and history of asthma exacerbations and symptoms despite treatment with ICS (fluticasone >or=500 microg x day(-1) or equivalent) plus a LABA. Patients received omalizumab (75-375 mg once or twice a month by subcutaneous injection, as determined from dosing tables) or placebo over 52 weeks (24-week fixed-steroid then 28-week adjustable-steroid phases). RESULTS: Out of 246 randomized patients (omalizumab, n = 166; placebo, n = 80), efficacy was analysed in 235 (omalizumab, n = 159; placebo, n = 76). Over the 24-week fixed-steroid phase, omalizumab reduced the rate of clinically significant asthma exacerbations (worsening symptoms requiring doubling of baseline ICS dose and/or systemic steroids) by 34% versus placebo (0.42 vs 0.63, rate ratio 0.662; P = 0.047). Over 52 weeks, the exacerbation rate was reduced by 50% (P < 0.001). Omalizumab had an acceptable safety profile, with no statistically significant (P < 0.05) differences in adverse events observed between omalizumab and placebo. CONCLUSION: Add-on omalizumab is well-tolerated and reduces exacerbations in children (6-<12 years) with severe persistent allergic asthma, inadequately controlled despite high-dose ICS plus a LABA. It should be noted that the sample size was not based on providing statistical power in the severe subgroup, and no corrections were made for multiple comparisons; however, outcomes consistently favoured omalizumab.

Frequency-specific coupling in the cortico-cerebellar auditory system.

Induced oscillatory activity in the auditory cortex peaks at around 40 Hz in humans. Using regional cerebral blood flow and positron emission tomography we previously confirmed frequency-selective cortical responses to 40-Hz tones in auditory primary cortices and concomitant bilateral activation of the cerebellar hemispheres. In this study, using functional magnetic resonance imaging (fMRI) we estimated the influence of 40-Hz auditory stimulation on the coupling between auditory cortex and superior temporal sulcus (STS) and Crus II, using a dynamic causal model of the interactions between medial geniculate nuclei, auditory superior temporal gyrus (STG)/STS, and the cerebellar Crus II auditory region. Specifically, we tested the hypothesis that 40-Hz-selective responses in the cerebellar Crus II auditory region could be explained by frequency-specific enabling of interactions in the auditory cortico-cerebellar-thalamic loop. Our model comparison results suggest that input from auditory STG/STS to cerebellum is enhanced selectively at gamma-band frequencies around 40 Hz.

Study of on-line adaptive discriminant analysis for EEG-based brain computer interfaces.

A study of different on-line adaptive classifiers, using various feature types is presented. Motor imagery brain computer interface (BCI) experiments were carried out with 18 naive able-bodied subjects. Experiments were done with three two-class, cue-based, electroencephalogram (EEG)-based systems. Two continuously adaptive classifiers were tested: adaptive quadratic and linear discriminant analysis. Three feature types were analyzed, adaptive autoregressive parameters, logarithmic band power estimates and the concatenation of both. Results show that all systems are stable and that the concatenation of features with continuously adaptive linear discriminant analysis classifier is the best choice of all. Also, a comparison of the latter with a discontinuously updated linear discriminant analysis, carried out in on-line experiments with six subjects, showed that on-line adaptation performed significantly better than a discontinuous update. Finally a static subject-specific baseline was also provided and used to compare performance measurements of both types of adaptation.

15 years of BCI research at Graz University of Technology: current projects.

Over the last 15 years, the Graz Brain-Computer Interface (BCI) has been developed and all components such as feature extraction and classification, mode of operation, mental strategy, and type of feedback have been investigated. Recent projects deal with the development of asynchronous BCIs, the presentation of feedback and applications for communication and control.

A fully on-line adaptive BCI.

A viable fully on-line adaptive brain computer interface (BCI) is introduced. On-line experiments with nine naive and able-bodied subjects were carried out using a continuously adaptive BCI system. The data were analyzed and the viability of the system was studied. The BCI was based on motor imagery, the feature extraction was performed with an adaptive autoregressive model and the classifier used was an adaptive quadratic discriminant analysis. The classifier was on-line updated by an adaptive estimation of the information matrix (ADIM). The system was also able to provide continuous feedback to the subject. The success of the feedback was studied analyzing the error rate and mutual information of each session and this analysis showed a clear improvement of the subject's control of the BCI from session to session.

Adaptive on-line classification for EEG-based brain computer interfaces with AAR parameters and band power estimates.

We present the result of on-line feedback Brain Computer Interface experiments using adaptive and non-adaptive feature extraction methods with an on-line adaptive classifier based on Quadratic Discriminant Analysis. Experiments were performed with 12 naïve subjects, feedback was provided from the first moment and no training sessions were needed. Experiments run in three different days with each subject. Six of them received feedback with Adaptive Autoregressive parameters and the rest with logarithmic Band Power estimates. The study was done using single trial analysis of each of the sessions and the value of the Error Rate and the Mutual Information of the classification were used to discuss the results. Finally, it was shown that even subjects starting with a low performance were able to control the system in a few hours: and contrary to previous results no differences between AAR and BP estimates were found.

AMAN o síndrome axonal difuso / AMAN or diffused axonal syndrome

Presentamos un caso clínico de neuropatía axonal motora aguda en un varón adulto, la orientación diagnóstica fue corroborada por electromiografía y detección de anticuerpos específicos GM1. El rasgo semiológico predominante fue la plejÍa generalizada, en la que participaba la musculatura respiratoria por lo que precisó ventilación mecánica prolongada. El paciente presentó varias infecciones intercurrentes, motivo finalmente de su éxitus (AU)

Comparative in vitro activity of a new quinolone, fleroxacin, against respiratory pathogens from patients with cystic fibrosis.

We evaluated fleroxacin, a newer fluoroquinolone, against isolates from sputum from patients with cystic fibrosis. These isolates included rough and mucoid Pseudomonas aeruginosa, Pseudomonas cepacia, Staphylococcus aureus, Haemophilus influenzae, and Escherichia coli. Selected isolates were tested by the broth microdilution method to examine the influence of various pHs, inoculum sizes, and biological fluids (serum or sputum from patients with cystic fibrosis). Fleroxacin MICs for 50 and 90% of isolates of P. aeruginosa were 2.0 and 4 micrograms/ml, those for P. cepacia were 2 and 16 micrograms/ml, those for S. aureus were 0.5 and 1 microgram/ml, those for H. influenzae were 0.06 and 0.06 micrograms/ml, and those for E. coli were 0.01 and 0.03 micrograms/ml, respectively. Fleroxacin activity against mucoid P. aeruginosa was similar to the activities of enoxacin and ofloxacin but eightfold lower than that of ciprofloxacin. It was twofold more active than norfloxacin and enoxacin but was twofold less active than ciprofloxacin, ofloxacin, and nafcillin against S. aureus. Fleroxacin inhibitory activity against P. cepacia was two- to fourfold lower than that of ciprofloxacin but eightfold greater than those of the other quinolones tested. Alterations in pH, diluent, and inoculum size did not significantly affect fleroxacin activity. These results, combined with available pharmacokinetic and tissue distribution data, support the need for clinical evaluation of fleroxacin in pulmonary infections in patients with cystic fibrosis.

In vitro activity of E-1040, a 3-substituted cephalosporin, against pathogens from cystic fibrosis sputum.

On the basis of preliminary in vitro data, we evaluated E-1040, a new cephalosporin, against 188 cystic fibrosis (CF) sputum isolates obtained from 26 CF centers in the United States. These isolates included mucoid and nonmucoid Pseudomonas aeruginosa, Pseudomonas cepacia, Staphylococcus aureus, Haemophilus influenzae, and Escherichia coli. In addition to MICs measured under standard conditions, selected isolates were tested at various pH values, inoculum sizes, and diluent (CF serum and sputum) conditions. E-1040 activities (MICs for 50 and 90% of the strains) against the isolates were as follows: P. aeruginosa (mucoid and nonmucoid), 1 and 4 micrograms/ml; P. cepacia, 4 and 16 micrograms/ml; S. aureus, 8 and 8 micrograms/ml; H. influenzae, 1 and 4 micrograms/ml; and E. coli, less than or equal to 0.12 and less than or equal to 0.12 microgram/ml. E-1040 activity against mucoid P. aeruginosa was 4-fold greater than that of aztreonam, 16-fold greater than that of ceftazidime, and 32-fold greater than that of piperacillin. E-1040 was similar to other broad-spectrum cephalosporins against S. aureus, H. influenzae, and E. coli. Bactericidal activity was less than or equal to 1 dilution of MIC for 88% of the strains, although kinetic studies with mucoid strains of P. aeruginosa demonstrated effective killing only at eight times the MIC. Variations in pH from 5 to 8, in inoculum size from 10(3) to 10(7) CFU/ml, and in diluent (CF serum or CF sputum) did not affect E-1040 activity.