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Purpose High dose rate brachytherapy applies intense and destructive radiation. A treatment plan defines radiation source dwell positions to avoid irradiating healthy tissue. The study discusses methods to quantify any positional changes of source locations along the various treatment sessions. Methods Electromagnetic tracking (EMT) localizes the radiation source during the treatment sessions. But in each session the relative position of the patient relative to the filed generator is changed. Hence, the measured dwell point sets need to be registered onto each other to render them comparable. Two point set registration techniques are compared: a probabilistic method called coherent point drift (CPD) and a multidimensional scaling (MDS) technique. Results Both enable using EMT without external registration and achieve very similar results with respect to dwell position determination of the radiation source. Still MDS achieves smaller grand average deviations (CPD-rPSR: MD = 2.55 mm, MDS-PSR: MD = 2.15 mm) between subsequent dwell position determinations, which also show less variance (CPD-rPSR: IQR = 4 mm, MDS-PSR: IQR = 3 mm). Furthermore, MDS is not based on approximations and does not need an iterative procedure to track sensor positions inside the implanted catheters. Conclusion Although both methods achieve similar results, MDS is to be preferred over rigid CPD while nonrigid CPD is unsuitable as it does not preserve topology.

A central question in neuroscience is how self-organizing dynamic interactions in the brain emerge on their relatively static structural backbone. Due to the complexity of spatial and temporal dependencies between different brain areas, fully comprehending the interplay between structure and function is still challenging and an area of intense research. In this paper we present a graph neural network (GNN) framework, to describe functional interactions based on the structural anatomical layout. A GNN allows us to process graph-structured spatio-temporal signals, providing a possibility to combine structural information derived from diffusion tensor imaging (DTI) with temporal neural activity profiles, like that observed in functional magnetic resonance imaging (fMRI). Moreover, dynamic interactions between different brain regions discovered by this data-driven approach can provide a multi-modal measure of causal connectivity strength. We assess the proposed model’s accuracy by evaluating its capabilities to replicate empirically observed neural activation profiles, and compare the performance to those of a vector auto regression (VAR), like that typically used in Granger causality. We show that GNNs are able to capture long-term dependencies in data and also computationally scale up to the analysis of large-scale networks. Finally we confirm that features learned by a GNN can generalize across MRI scanner types and acquisition protocols, by demonstrating that the performance on small datasets can be improved by pre-training the GNN on data from an earlier study. We conclude that the proposed multi-modal GNN framework can provide a novel perspective on the structure-function relationship in the brain. Accordingly this approach appears to be promising for the characterization of the information flow in brain networks.

Introduction In 2016–18, the Democratic Republic of Sao Tome and Principe suffered a necrotic skin infection epidemic. Methods A surveillance system was established after increased hospitalisations for this infection. Microbiology results were available for samples analysed in December 2016 and March 2017 using whole genome sequencing and metagenomics. Negative binomial regression was used to study the association of weather conditions with monthly case counts in a time-series analysis. Results From October 2016 to October 2018, the epidemic cumulative attack rate was 1.5%. The first peak lasted 5 months, accounting for one-third of total cases. We could not conclusively identify the aetiological agent(s) due to the country's lack of microbiology capacity. Increased relative humidity was associated with increased monthly cases (incidence rate ratio (IRR) 1.05, 95% CI 1.02–1.09), and higher precipitation in the previous month with a higher number of cases in the following month (months with 0–49 mm rainfall compared with months with 50–149 mm and ≥150 mm: IRR 1.44, 95 % CI 1.13–1.78 and 1.50, 95% CI 1.12–1.99, respectively). Discussion This epidemic was favoured by increased relative humidity and precipitation, potentially contributing to community-based transmission of ubiquitous bacterial strains superinfecting skin wounds. Funding World Health Organization Regional Office for Africa, Ministry of Health

High dose rate brachytherapy affords a frequent reassurance of the precise dwell positions of the radiation source. The current investigation proposes a multi-dimensional scaling transformation of both data sets to estimate dwell positions without any external reference. Furthermore, the related distributions of dwell positions are characterized by uni-or bi-modal heavy-tailed distributions. The latter are well represented by α-stable distributions. The newly proposed data analysis provides dwell position deviations with high accuracy, and, furthermore, offers a convenient visualization of the actual shapes of the catheters which guide the radiation source during the treatment.

Modern radiotherapy of female breast cancers often employs high dose rate brachytherapy, where a radioactive source is moved inside catheters, implanted in the female breast, according to a prescribed treatment plan. Source localization relative to the patient's anatomy is determined with solenoid sensors whose spatial positions are measured with an electromagnetic tracking system. Precise sensor dwell position determination is of utmost importance to assure irradiation of the cancerous tissue according to the treatment plan. We present a hybrid data analysis system which combines multi-dimensional scaling with particle filters to precisely determine sensor dwell positions in the catheters during subsequent radiation treatment sessions. Both techniques are complemented with empirical mode decomposition for the removal of superimposed breathing artifacts. We show that the hybrid model robustly and reliably determines the spatial positions of all catheters used during the treatment and precisely determines any deviations of actual sensor dwell positions from the treatment plan. The hybrid system only relies on sensor positions measured with an EMT system and relates them to the spatial positions of the implanted catheters as initially determined with a computed x-ray tomography.

This work presents an unsupervised mining strategy, applied to an independent component analysis (ICA) of segments of data collected while participants are answering to the items of the Halstead Category Test (HCT). This new methodology was developed to achieve signal components at trial level and therefore to study signal dynamics which are not available within participants’ ensemble average signals. The study will be focused on the signal component that can be elicited by the binary visual feedback which is part of the HCT protocol. The experimental study is conducted using a cohort of 58 participants.

The thrombus causing a stroke can be seen on the susceptibility weighted angiography (SWAN) magnetic resonance imaging (MRI) sequence. But it is very small and hard to detect by humans. Up to date the thrombus is identified by trained human experts. But as stroke needs quick treatment, an automatic detection of the thrombus would be useful to speed up the diagnosis of acute stroke. We propose a method for automatic thrombus detection from SWAN using three separate U-Nets which work on the axial, coronal and sagittal planes.

Independent component analysis (ICA), as a data driven method, has shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is, that it is naturally not convenient for analysis of group studies. Therefore various techniques have been proposed in order to overcome this limitation of ICA. In this paper a novel ICA based work-flow for extracting resting state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used to generate reference signals in a data driven manner, which can be incorporated into a constrained version of ICA (cICA), what helps to overcome the inherent ambiguities. The results of the proposed workflow are then compared to those obtained by a widely used group ICA approach. It is demonstrated that intrinsic modes, extracted by EMD, are suitable to serve as references for cICA to obtain typical resting state patterns, which are consistent over subjects. This novel processing pipeline makes it transparent for the user, how comparable activity patterns across subjects emerge, and also the trade-off between similarity across subjects and preserving individual features can be well adjusted and adapted for different requirements in the new work-flow.

The Halstead Category Test (HCT) is a neuropsychological test that measures a person's ability to formulate and apply abstract principles. Performance must be adjusted based on feedback after each trial and errors are common until the underlying rules are discovered. Event-related potential (ERP) studies associated with the HCT are lacking. This paper demonstrates the use of a methodology inspired on Singular Spectrum Analysis (SSA) applied to EEG signals, to remove high amplitude ocular and movement artifacts during performance on the test. This filtering technique introduces no phase or latency distortions, with minimum loss of relevant EEG information. Importantly, the test was applied in its original clinical format, without introducing adaptations to ERP recordings. After signal treatment, the feedback-related negativity (FRN) wave, which is related to error-processing, was identified. This component peaked around 250ms, after feedback, in fronto-central electrodes. As expected, errors elicited more negative amplitudes than correct responses. Results are discussed in terms of the increased clinical potential that coupling ERP information with behavioral performance data can bring to the specificity of the HCT in diagnosing different types of impairment in frontal brain function.

Positron emission tomography (PET) provides a functional imaging modality to detect signs of dementias in human brains. Two-dimensional empirical mode decomposition (2D-EMD) provides means to analyze such images. It decomposes the latter into characteristic modes which represent textures on different spatial scales. These textures provide informative features for subsequent classification purposes. The study proposes a new EMD variant which relies on a Green's function based estimation method including a tension parameter to fast and reliably estimate the envelope hypersurfaces interpolating extremal points of the two-dimensional intensity distrubution of the images. The new method represents a fast and stable bi-dimensional EMD which speeds up computations roughly 100-fold. In combination with proper classifiers these exploratory feature extraction techniques can form a computer aided diagnosis (CAD) system to assist clinicians in identifying various diseases from functional images alone. PET images of subjects suffering from Alzheimer's disease are taken to illustrate this ability.

In this research the performance of individuals with normal hearing and impaired hearing while playing a computer game was evaluated. The aim was to study and understand if impaired hearing gamers are at disadvantage when playing games without being able to hear the music. Three levels (attention, concentration and blinking) were measured to compare and understand how sound can influence players’ attention and concentration performance. The data was recorded using Mindwave equipment during the game Outlast considering two scenarios: game with sound and game without sound. The results show that hearing impaired individuals have the same standard of attention and level of concentration as individuals with normal hearing when there is sound in the game. In the case of the blinking level, this is quite different between the scenarios and the analyzed groups. For this particular study the results suggest that sound is not an important level in the attention and concentration performance of impaired hearing players. Although much work still needs to be done, there is evidence of a relation between the attention and the concentration levels between normal hearing and impaired hearing individuals in the presence and absence of sound.

The human brain is one of the most complex physiological systems, involving billions of interacting physiological and chemical processes giving rise to the experimentally observed neuroelectric activity, called electroencephalographic (EEG) signals. A biomedical signal, electroencephalography (EEG), reflects the state of mind and is often used to verify the influence of music on human brain activity. In this paper we present a brief study of various effects of music on Attention and Concentration state of the brain using the Neurosky Mindwave Mobile Headset and EEG Analyser application. The aim of the study is to identify which type of music can help the brain activities to be more attentive while performing a cognitive task (playing a game). In contrast the effects of music in the Concentration state is also studied in order to understand how the music can help the brain activities to be more focused. Although the number of participants was reduced, the results were interesting. The music acts in an individualized way in the performance of each participant. It was found that for certain tasks there is a strong correlation between Attention and Concentration levels. In addition, a distinction between genders of participants can be made according to the results.

Background: Zika virus has been responsible for recent outbreaks in the western hemisphere with known neurological complications such as microcephaly. This complication has not been previously documented in continental Africa. Methods: Neurological evaluation of the newborn was performed after birth, at one and two months of age. The mother and the newborn sera samples were tested by immunofluorescent assay (IFA; immunoglobulin G [IgG] and IgM) for Zika virus and the presence of Zika virus ribonucleic acid (RNA) was checked by qualitative real-time reverse transcription polymerase chain reaction (RT-PCR) in placenta, blood and urine samples. Results: We report on a newborn, born in Portugal, with microcephaly with confirmed congenital Zika virus infection (Asian lineage) imported from Angola with typical clinical and imaging findings. Conclusions: To our knowledge this is the first report that shows the circulation of the Asian lineage in Angola and the first report of a congenital Zika syndrome in continental Africa. info:eu-repo/semantics/publishedVersion

Background Empirical mode decomposition (EMD) is an empirical data decomposition technique. Recently there is growing interest in applying EMD in the biomedical field. New method EMDLAB is an extensible plug-in for the EEGLAB toolbox, which is an open software environment for electrophysiological data analysis. Results EMDLAB can be used to perform, easily and effectively, four common types of EMD: plain EMD, ensemble EMD (EEMD), weighted sliding EMD (wSEMD) and multivariate EMD (MEMD) on EEG data. In addition, EMDLAB is a user-friendly toolbox and closely implemented in the EEGLAB toolbox. Comparison with existing methods EMDLAB gains an advantage over other open-source toolboxes by exploiting the advantageous visualization capabilities of EEGLAB for extracted intrinsic mode functions (IMFs) and Event-Related Modes (ERMs) of the signal. Conclusions EMDLAB is a reliable, efficient, and automated solution for extracting and visualizing the extracted IMFs and ERMs by EMD algorithms in EEG study.

Objective In this study, individual differences in brain electrophysiology during positive and negative affective valence processing in women with different neuroticism scores are quantified. Methods Twenty-six women scoring high and low on neuroticism participated on this experiment. A support vector machine (SVM)-based classifier was applied on the EEG single trials elicited by high arousal pictures with negative and positive valence scores. Based on the accuracy values obtained from subject identification tasks, the most distinguishing EEG channels among participants were detected, pointing which scalp regions show more distinct patterns. Results Significant differences were obtained, in the EEG heterogeneity between positive and negative valence stimuli, yielding higher accuracy in subject identification using negative pictures. Regarding the topographical analysis, significantly higher accuracy values were reached in occipital areas and in the right hemisphere ( p 0.001 ). Conclusions Mainly, individual differences in EEG can be located in parietooccipital regions. These differences are likely to be due to the different reactivity and coping strategies to unpleasant stimuli in individuals with high neuroticism. In addition, the right hemisphere shows a greater individual specificity. Significance An SVM-based classifier asserts the individual specificity and its topographical differences in electrophysiological activity for women with high neuroticism compared to low neuroticism.

The synthesis and characterization of the new 2-phenylindole derivative: C 8 H 3 N-2-C 6 H 5 -3NOMe-5OMe ( 3c ) and the trans- and cis- isomers of [Pt( 3c )Cl 2 (DMSO)] complexes ( 4c and 5c , respectively) are described. The crystal structures of 4c ·CH 2 Cl 2 and 5c confirm: (a) the existence of a Pt-N indole bond, (b) the relative arrangement of the Cl − ligands [ trans- (in 4c ) or cis- (in 5c )] and (c) the anti- ( E ) configuration of the oxime. The cytotoxic assessment of C 8 H 3 N-2-(C 6 H 4 -4′R 1 )-3NOMe-5R 2 [with R 1 = R 2 = H ( 3a ); R 1 = Cl, R 2 = H ( 3b ) and R 1 = H, R 2 = OMe ( 3c )] and the geometrical isomers of [Pt(L)Cl 2 (DMSO)] with L = 3a – 3c [ trans- ( 4a – 4c ) and cis- ( 5a – 5c ), respectively] against human breast adenocarcinoma cell lines (MDA-MB231 and MCF-7) is also reported and reveals that all the platinum(II) complexes (except 4a ) are more cytotoxic than cisplatin in front of the MCF7 cell line. Electrophoretic DNA migration studies of the synthesized compounds in the absence and in the presence of topoisomerase-I have been performed, in order to get further insights into their mechanism of action.

International audience; The analysis of positron emission tomography (PET) scan image is challenging due to a high level of noise and a low resolution and also because differences between healthy and demented are very subtle. High dimensional classification methods based on PET have been proposed to automatically discriminate between normal control group (NC) patients and patients with Alzheimer’s disease (AD), with mild cognitive impairment (MCI), and mild cognitive impairment converting to Alzheimer’s disease (MCIAD) (a group of patients that clearly degrades to AD). We developed a voxelbased method for volumetric image analysis. We performed 3 classification experiments AD vs CG, AD vs MCI, MCIAD vs MCI. We will also give a small demonstration of the presented method on a set of face images. This method is capable to extract information about the location of metabolic changes induced by Alzheimer’s disease that directly relies statistical features and brain regions of interest (ROIs). We produce “maps” to visualize the most informative regions of the brain and compare them with voxel-wise statistics. Using the mean intensity of about 2000 6 × 6 × 6mm patches, selected by the extracted map, as input for a classifier we obtain a classification rate of 95.5%.

Quinolones are a class of antibacterial agents for the treatment of several infectious diseases (e.g. urinary and respiratory tract infections). They are used worldwide due to their broad spectrum of activity, high bioavailability and good safety profile. The safety profile varies from quinolone to quinolone. The aim of this article was to review the neurological and psychiatric adverse drug reaction (ADR) profile of quinolones, using a literature search strategy designed to identify case reports and case series. A literature search using PubMed/MEDLINE (from inception to 31 October 2010) was performed to identify case reports and case series related to quinolone-associated neurological and psychiatric ADRs. The search was conducted in two phases: the first phase was the literature search and in the second phase relevant articles were identified through review of the references of the selected articles. Relevant articles were defined as articles referring to adverse events/reactions associated with the use of any quinolone. Abstracts referring to animal studies, clinical trials and observational studies were excluded. Identified case reports were analysed by age group, sex, active substances, dosage, concomitant medication, ambulatory or hospital-based event and seriousness, after Medical Dictionary for Regulatory Activities (MedDRA®) coding. From a total of 828 articles, 83 were identified as referring to nervous system and/or psychiatric disorders induced by quinolones. 145 individual case reports were extracted from the 83 articles. 40.7% of the individual case reports belonged to psychiatric disorders only, whereas 46.9% related to neurological disorders only. Eight (5.5%) individual case reports presented both neurological and psychiatric ADRs. Ciprofloxacin, ofloxacin and pefloxacin were the quinolones with more neurological and psychiatric ADRs reported in the literature. Ciprofloxacin has been extensively used worldwide, which may explain the higher number of reports, while for ofloxacin and pefloxacin, the number of reports may be over-representative. A total of 232 ADRs were identified from the selected articles, with 206 of these related to psychiatric and/or neurological ADRs. The other 26 were related to other body systems but were reported together with the reactions of interest. Mania, insomnia, acute psychosis and delirium were the most frequently reported psychiatric adverse events; grand mal convulsion, confusional state, convulsions and myoclonus were the most frequently reported neurological adverse events. Several aspects should be taken into account in the development of CNS adverse effects, such as the pharmacokinetics of quinolones, chemical structure and quinolone uptake in the brain. These events may affect not only susceptible patients but also 'healthy' patients.

International audience; Predictive models arouse increasing interest in clinical practice, not only to improve successful intervention rates but also to extract information of diverse physiological disorders. This is the case of persistent atrial fibrillation (AF), the most common cardiac arrhythmia in adults. Currently, catheter ablation (CA) is one of the preferred therapies to face this disease. However, selecting the best responders to CA by standard noninvasive techniques such as the electrocardiogram (ECG) remains a challenge. This work presents different predictive models for determining long-term CA outcome based on the dominant frequency (DF) of atrial activity measured in the ECG. The ensemble empirical mode decomposition (EEMD) is employed to obtain the intrinsic mode functions (IMFs) composing the ECG signal in each lead. The IMF DFs computed in multiple leads are then combined into a logistic regression (LR) model. The IMF DF features are discriminant enough to reach 79% accuracy for long-term CA outcome prediction, outperforming other methods based on DF computation. Our study shows EEMD as a valuable alternative to extract clinically relevant spectral information from AF ECGs and confirms the advantage of LR to build multivariate predictive models as compared with univariate analysis.

International audience; The dominant frequency (DF) of the atrial activity signal is arguably one of the most relevant features characterizing atrial fibrillation (AF), the most common cardiac ar-rhythmia. Its accurate estimation from noninvasive acquisition modalities such as the electrocardiogram (ECG) can avoid risks of potential complications to patients in a cost-effective manner. However, the approximation of the underlying intracardiac atrial activity by noninvasive techniques such as average beat subtraction or blind source separation has not always been satisfactory. In the present work, a new approach based on the ensemble empirical mode decomposition (EEMD) is proposed for AF DF estimation. Our results suggest that EEMD provides more accurate estimates of intracardiac AF DF than alternative noninvasive methods. In addition, the empirical nature of EEMD overcomes important drawbacks of other techniques, simplifying its implementation in automatic tools for diagnosis aid.

In this work, we present a method to extract high-amplitude artefacts from single channel electroencephalogram (EEG) signals. The method is called local singular spectrum analysis (local SSA). It is based on a principal component analysis (PCA) applied to clusters of the multidimensional signals obtained after embedding the signals in their time-delayed coordinates. The decomposition of the multidimensional signals in each cluster is achieved by relating the largest eigenvalues with the large amplitude artefact component of the embedded signal. Then by reverting the clustering and embedding processes, the high-amplitude artefact can be extracted. Subtracting it from the original signal a corrected EEG signal results. The algorithm is applied to segments of real EEG recordings containing paroxysmal epileptiform activity contaminated by large EOG artefacts. We will show that the method can be applied also in parallel to correct all channels that present high-amplitude artefacts like ocular movement interferences or high-amplitude low frequency baseline drifts. The extracted artefacts as well as the corrected EEG will be presented.

In biomedical and psychological applications dealing with EEG, a suitable selection of the most relevant electrodes is useful for lightening the data acquisition and facilitating the signal processing. Therefore, an efficient method for extracting and selecting features from EEG channels is desirable. Classification methods are more and more applied for obtaining important conclusions from diverse psychological processes, and specifically for emotional processing. In this work, an original and straightforward method, inspired by the spectral turbulence (ST) measure from electrocardiogram and the support vector machine-recursive feature elimination (SVM-RFE) algorithm, is proposed for classifying EEG signals. The goal of this study is to introduce the ST concept in applications of artificial intelligence related to cognitive processes and to determine the best EEG channels for distinguishing between two different experimental conditions. By means of this method, the left temporal region of the brain has revealed to be greatly involved in the affective valence processing elicited by visual stimuli.

Highlights? A wrapper method is suggested to get discriminant features in neuroscience. ? SVM-RFE is proposed for feature selection related to affective valence. ? Topography-time-frequency analysis is used to get relevant scalp regions. ? Morlet wavelet filter is used for feature extraction from event related potentials. In this work, event related potentials (ERPs) induced by visual stimuli categorized with different value of affective valence are studied. EEG signals are recorded during visualization of selected pictures belonging to International Affective Picture System (IAPS). A Morlet wavelet filter is used to transform the EEG input space to a topography-time-frequency feature space. Support vector machine-recursive feature elimination (SVM-RFE) is applied for detecting scalp spectral dynamics of interest (SSDOIs) in this feature space, allowing to identify the most relevant time intervals, frequency bands and EEG channels. This feature selection method has proven to outperform the classical t-test in the discrimination of brain cortex regions involved in affective valence processing. Furthermore, the presented combination of feature extraction and selection techniques can be applied as an alternative in other different clinical applications.

EEG signals have been widely explored in emotional processing analyses, both in time and frequency domains. However, in such studies, habituation phenomenon is barely considered in the discrimination of different emotional responses. In this work, spectral features of the event-related potentials (ERPs) are studied by means of event-related desynchronization/synchronization computation. In order to determine the most relevant ERP features for distinguishing how positive and negative affective valences are processed within the brain, support vector machine-recursive feature elimination is employed. The proposed approach was applied for investigating in which way the familiarity of stimuli affects the affective valence processing as well as which frequency bands and scalp regions are more involved in this process. In a group composed of young adult women, results prove that parietooccipital region and theta band are especially involved in the processing of novelty in emotional stimuli. Furthermore, the proposed method has shown to perform successfully using a moderated number of trials.

Objective. We propose a combination of a constrained independent component analysis (cICA) with an ensemble empirical mode decomposition (EEMD) to analyze electroencephalographic recordings from depressed or schizophrenic subjects during olfactory stimulation. Approach. EEMD serves to extract intrinsic modes (IMFs) underlying the recorded EEG time. The latter then serve as reference signals to extract the most similar underlying independent component within a constrained ICA. The extracted modes are further analyzed considering their power spectra. Main results. The analysis of the extracted modes reveals clear differences in the related power spectra between the disease characteristics of depressed and schizophrenic patients. Such differences appear in the high frequency gamma-band in the intrinsic modes, but also in much more detail in the low frequency range in the alpha-,theta- and delta-bands. Significance. The proposed method provides various means to discriminate both disease pictures in a clinical environment.

Depression and fibromyalgia syndrome are debilitating chronic conditions that impose a significant burden on individuals, families and society. Both depression and fibromyalgia have many overlapping symptoms, and antidepressants of several classes are among recommended treatment options for patients with fibromyalgia syndrome. Pirlindole is a selective and reversible inhibitor of monoamine oxidase (MAO) subtype A (MAO-A) that is approved in some European and non-European countries for the treatment of depression. The antidepressant efficacy and safety of pirlindole have been demonstrated in a number of placebo- and active comparator-controlled studies and are supported by many years of clinical experience in the treatment of depression. The drug's efficacy and safety have also been demonstrated, more recently, in the treatment of fibromyalgia syndrome. Pirlindole has a favourable tolerability profile, with no deleterious effect on cardiovascular dynamics. The effect of pirlindole on sensorimotor performance relevant to driving a motor vehicle is similar to that of placebo, as pirlindole appears to have an activating rather than a sedating antidepressant profile. Because of its specific and reversible inhibition of MAO-A and relatively short elimination half-life, no tyramine or 'cheese' effect is likely after short- or long-term administration. The available evidence supports pirlindole as a safe and effective treatment option for the management of depression and fibromyalgia syndrome.

During the last decade many advances in a number of fields have supported the idea that a direct interface between the human brain and an artificial system, called Brain Computer Interface (BCI), is a viable concept, although a significant research and development effort has to be conducted before these technologies enter routine use. The conceptual approach is to model the brain activity variations and map them into some kind of actuation over a target output through the use of signal processing and machine learning methods. In the meantime, several working BCI systems have been described in the literature using a variety of signal acquisition methods, experimental paradigms, pattern recognition approaches and output interfaces, and requiring different types of cognitive activity (Allison et al., 2008; Bashashati et al., 2007; Berger et al., 2008; Leeb et al.‚ 2007; Millan, 2008; Muller-Putz & Pfurtscheller‚ 2008). Nowadays, the principal reason for the BCI research is the potential benefits to those with severe motor disabilities, such as brainstem stroke, amyotrophic lateral sclerosis or severe cerebral palsy (Bensch et al.‚ 2007; Birbaumer et al.‚ 2007; Nijboer et al.‚ 2008; Pfurtscheller et al.‚ 2008). However, the most recent advances in acquisition technology and signal processing assert that controlling certain functions by neural interfaces may have a significant impact in the way people will operate computers, wheelchairs, prostheses, robotic systems and other devices. A very effective way to analyze the brain physiological activity is the electroencephalogram (EEG) measurements from the cortex whose sources are the action potentials of the nerve cells in the brain. The theoretical and the application studies are based on the knowledge that the EEG signals are composed of waves inside the 0-60 Hz frequency band and on the fact that different brain activities can be identified based on the recorded oscillations (Niedermayer & Lopes da Silva, 1999). Over the last years, the interest in extracting knowledge hidden in the EEG signals is rapidly growing, as well as their applications. EEGbased BCIs for motor control and biometry are among the most recent applications in the computational neuro-engineering field. Despite the proof of concept and many encouraging results achieved by some research groups (Marcel & Millan, 2007; Millan et al.‚ 2004; Palaniappan & Mandic, 2007; Pineda, 2005; Pfurtscheller et al., 2006; Vidaurre et al., 2006), additional efforts are required in order to design and implement efficient BCIs. For example, reliable signal processing and pattern recognition techniques able to continuously extract meaningful information from the very noisy EEG is still a high challenge.

We use two spatiotemporal Independent Component Analysis algorithms, stJADE and stSOBI, to analyse data from a retinotopic functional magnetic resonance imaging experiment and compare their performance to the analysis of the same data with the spatial ICA done with JADE. This kind of experimental setting has the advantage that the activation in the brain can be estimated fairly easily and therefore can be used as well defined benchmark. We show that stSOBI can outperform sJADE and exhibits quite stable behaviour while stJADE critically depends on the quality of the chosen parameter settings for each subject.

This paper is focused on proving the concept that the EEG signals collected during a perception or mental task can be used for discrimination of individuals. The viability of the EEG-based person identification was successfully tested for a data base of 13 persons. Among various classifiers tested, Support Vector Machine (SVM) with Radial Basis Function (RBF) exhibits the best performance. The problem of static classification that does not take into account the temporal nature of the EEG sequence was considered by an empirical post classifier procedure. The algorithm proposed has an effect of introducing a memory into the classifier without increasing its complexity. Control of a classified access into restricted areas security systems, health disorder identification in medicine, gaining more understanding of the cognitive human brain processes in neuroscience are some of the potential applications of EEG-based biometry.

Exploratory matrix factorization methods like PCA, ICA and sparseNMF are applied to identify marker genes and classify gene expression data sets into different categories for diagnostic purposes or group genes into functional categories for further investigation of related regulatory pathways. Gene expression levels of either human breast cancer (HBC) cell lines [6] or the famous leucemia data set [10] are considered.

Support vector machines are applied to extract marker genes from various microarray data sets: breast cancer, leukemia and monocyte - macrophage differentiation to ease classification of related pathologies or characterize related gene regulation pathways.

Exploratory matrix factorization methods like ICA and LNMF are applied to identify marker genes and classify gene expression data sets into different categories for diagnostic purposes or group genes into functional categories for further investigation of related regulatory pathways. Gene expression levels of either human breast cancer (HBC) cell lines [5] mediating bone metastasis or cell lines from Niemann Pick C patients monitoring monocyte - macrophage differentiation are considered.

Positron emission tomography (PET) provides a functional imaging modality to detect signs of dementias in human brains. Two-dimensional empirical mode decomposition (2D EMD) provides means to analyze such images. It extracts characteristic textures from these images which may be fed into powerful classifiers trained to group these textures into several classes depending on the problem at hand. The study investigates the potential use of 2D EEMD in combination with proper classifiers to form a computer aided diagnosis (CAD) system to assist clinicians in identifying various diseases from functional images alone. PET images of subjects suffering from a dementia are taken to illustrate this ability.

Exploratory data analysis techniques such as independent component analysis (ICA) do not depend on a priori hypotheses and are able to detect unknown, yet structured spatiotemporal processes in neuroimaging data. We present fMRI data of two different subject-groups (young and old), which performed a modified Wisconsin Card Sorting Test (WCST). Spatiotemporal ICA and SPM-generated brain maps of the subject data are compared. For the group analysis a singular value decomposition approach was used. Spatiotemporal ICA reveals a frontoparietal network being activated while subjects performed different variants of the WCST. Contrary to the SPM analysis, ICA analysis revealed significant differences between young and old subjects as well as significant within-group differences.While young subjects showed with increasing task demands (A>B>C) increasing activation of the right lateral prefrontal cortex and of the medial orbito-frontal cortex, old subjects showed no such gradient in activation pattern and appeared to be more distributed.

Glaucoma represent one of the most frequent causes of partial loss of the visual field. It comes along with an ongoing destruction of the optic nerve caused by an increased pressure of the eye liquid. The disease becomes obvious from investigations of the retina with scanning laser microscopes. In this report an image analysis and classification system based on independent component analysis and k-nearest-neighbor classification is proposed. The method is tested with 120 selected retina images collected with the Heidelberg Retina Tomograph and achieves a classification rate of 91%.

This work proposes a clustering technique to analyze evoked potential signals. The proposed method uses an orthogonal subspace model to enhance the single-trial signals of a session and simultaneously a subspace measure to group the trials into clusters. The ensemble averages of the signals of the different clusters are compared with ensemble averages of visually selected trials which are free of any artifact. Preliminary results consider recordings from an occipital channel where evoked response P100 wave is most pronounced.