Alpha event-related desynchronization (ERD) has been widely applied to understand the psychophysiological role of this band in cognition. In particular, a considerable number of publications have described spectral alterations in several pathologies using this time-frequency approach. However, ERD is not capable of specifically showing nonphase (induced) activity related to the presentation of stimuli. Recent studies have described an evoked and induced activity in the early phases (first 200 ms) of stimulus processing. However, scarce studies have analyzed induced and evoked modulations in longer latencies (>200 ms) and their potential roles in cognitive processing. The main goal of the present study was to analyze diverse evoked and induced modulations in response to visual stimuli. Thus, 58-channel electroencephalogram (EEG) was recorded in 21 healthy subjects during the performance of a visual attention task, and analyses were performed for both target and standard stimuli. The initial result showed that phase-locked and nonphase locked activities coexist in the early processing of target and standard stimuli as has been reported by previous studies. However, more modulations were evident in longer latencies in both evoked and induced activities. Correlation analyses suggest that similar maps were present for evoked and induced activities at different timepoints. In the discussion section, diverse proposals will be stated to define the potential roles of these modulations in the information processing for this cognitive task. As a general conclusion, induced activity enables the observation of cognitive mechanisms that are not visible by ERD or ERP modulations.

Some of the anatomical and functional basis of cognitive impairment in multiple sclerosis (MS) currently remains unknown. In particular, there is scarce knowledge about modulations in induced EEG (nonphase activity) for diverse frequency bands related to attentional deficits in this pathology. The present study analyzes phase and nonphase alpha and gamma modulations in 26 remitting-relapsing multiple sclerosis patients during their participation in the attention network test compared with twenty-six healthy controls (HCs) matched in sociodemographic variables. Behavioral results showed that the MS group exhibited general slowing, suggesting impairment in alerting and orienting networks, as has been previously described in other studies. Time–frequency analysis of EEG revealed that the gamma band was related to the spatial translation of the attentional focus, and the alpha band seemed to be related to the expectancy mechanisms and cognitive processing of the target. Moreover, phase and nonphase modulations differed in their psychophysiological roles and were affected differently in the MS and HC groups. In summary, nonphase modulations can unveil hidden cognitive mechanisms for phase analysis and complete our knowledge of the neural basis of cognitive impairment in multiple sclerosis pathology.

This letter describes a fast and very simple algorithm for estimating the fetal electrocardiogram (FECG). It is based on independent component analysis, but we substitute its computationally demanding calculations for a much simpler procedure. The resulting method consists of two steps: 1) a dimensionality reduction step and 2) a computationally light postprocessing stage used to enhance the FECG signal.

Principal component analysis (PCA) based on L1-norm maximization is an emerging technique that has drawn growing interest in the signal processing and machine learning research communities, especially due to its robustness to outliers. The present work proves that L1-norm PCA can perform independent component analysis (ICA) under the whitening assumption. However, when the source probability distributions fulfil certain conditions, the L1-norm criterion needs to be minimized rather than maximized, which can be accomplished by simple modifications on existing optimal algorithms for L1-PCA. If the sources have symmetric distributions, we show in addition that L1-PCA is linked to kurtosis optimization. A number of numerical experiments illustrate the theoretical results and analyze the comparative performance of different algorithms for ICA via L1-PCA. Although our analysis is asymptotic in the sample size, this equivalence opens interesting new perspectives for performing ICA using optimal algorithms for L1-PCA with guaranteed global convergence while inheriting the increased robustness to outliers of the L1-norm criterion.

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Brain computer interfaces (BCIs) have been attracting a great interest in recent years. The common spatial patterns (CSP) technique is a well-established approach to the spatial filtering of the electroencephalogram (EEG) data in BCI applications. Even though CSP was originally proposed from a heuristic viewpoint, it can be also built on very strong foundations using information theory. This paper reviews the relationship between CSP and several information-theoretic approaches, including the Kullback-Leibler divergence, the Beta divergence and the Alpha-Beta log-det (AB-LD)divergence. We also revise other approaches based on the idea of selecting those features that are maximally informative about the class labels. The performance of all the methods will be also compared via experiments.

In brain-computer interfaces (BCIs), the typical models of the EEG observations usually lead to a poor estimation of the trial covariance matrices, given the high non-stationarity of the EEG sources. We propose the application of two techniques that significantly improve the accuracy of these estimations and can be combined with a wide range of motor imagery BCI (MI-BCI) methods. The first one scales the observations in such a way that implicitly normalizes the common temporal strength of the source activities. When the scaling applies independently to the trials of the observations, the procedure justifies and improves the classical preprocessing for the EEG data. In addition, when the scaling is instantaneous and independent for each sample, the procedure particularizes to Tyler's method in statistics for obtaining a distributionfree estimate of scattering. In this case, the proposal provides an original interpretation of this existing method as a technique that pursuits an implicit instantaneous power-normalization of the underlying source processes. The second technique applies to the classifier and improves its performance through a convenient regularization of the features covariance matrix. Experimental tests reveal that a combination of the proposed techniques with the state-of-the-art algorithms for motor-imagery classification provides a significant improvement in the classification results. Index Terms-Common spatial pattern, brain-computer interfaces, motor-imagery classification, covariance matrix estimation. I. INTRODUCTION B RAIN-COMPUTER interfaces (BCI) have a great potential for enabling the communication between machine and humans by means of the analysis of the electroencephalographic activity. Nowadays, almost all the Motor Imagery BCI (MI-BCI) systems summarize most of the relevant information about the measurements in two kinds of covariance matrices: the covariance matrices of the filtered observations (employed for dimensionality reduction) and the covariance matrices of the features (which are required for classification). In the dimensionality reduction Manuscript