Abstract
In this paper we present the evolution of a quantitative movement analysis methodology for epileptic seizures. With this improved method we analyzed 20 seizure video sequences, 10 classified as automotor and 10 as hypermotor, from 17 different patients. The results obtained show we could classify all (100%) of the hypermotor seizures solely based on a quantified movement parameter - called movement extent extracted with our method. Other quantitative parameters were also studied. This striking result paves the way to the contribution of quantitative movement methods in automated epileptic seizure detection systems.

Abstract
This work assesses the ability of Self Organizing Maps (SOMs) to find nonlinear association and/or connectivity among biosignals. The proposed method can find numerous applications where nonlinear biosignals are measured in spatiotemporal manner. Experiments are performed on tens of thousands of biosignals that are obtained from real biosignals by implementing a nonlinear transform, delays, additive and multiplicative random noise. Results showed that resolving association among biosignals under strong nonlinear transformation, noise, and delay is effective using SOMs.

Abstract
EpiGauss is a method that combines single dipole model with dipole clustering to characterize active brain generators in space and time related to EEG events. EpiGauss was applied to study epileptogenic activity in 4 patients suffering of hypothalamic hamartoma related epilepsy, a rare syndrome with a unique epileptogenic source - the hamartoma lesion - and natural propagation hypothesis - from hamartoma to the surface EEG focus. The results are compared to Rap-MUSIC and Single Moving Dipole methods over the same patients.

Abstract
This paper studies the suitability of brain activity, namely electroencephalogram signals, as raw material for conducting biometric authentication of individuals. Brain responses were extracted in particular scenarios, namely with visual stimulation leading to biological brain responses known as visual evoked potentials. In our study, we evaluated a novel method, using only 8 occipital electrodes and the energy of differential EEG signals, to extract information about the subjects for further use as their biometric features. To classify the features obtained from each individual we used a one-class classifier per subject. These classifiers are trained only with target class features, which is the correct procedure to apply in biometric authentication scenarios. Two types of one-class classifiers were tested, K-Nearest Neighbor and Support Vector Data Description. Two other classifier architectures were also studied, both resulting from the combination of the two previously mentioned classifiers. After testing these classifiers with the features extracted from 70 subjects, the results showed that brain responses to visual stimuli are suitable for an accurate biometric authentication.

Abstract
We used genetic algorithms to detect active voxels in the human brain imaged using functional magnetic resonance images. The method that we called EVOX deploys multivoxel pattern analysis to find the fitness of most active voxels. The fitness function is a classifier that works in a leave-one-run-out cross-validation. In each generation, the fitness value is calculated as the average performance over all cross-validation folds. Experimental results using functional magnetic resonance images collected while humans (subjects) were responding to attention visual stimuli showed certain situations that EVOX has could be useful compared to univariate ANOVA (analysis of variance) and searchlight methods. EVOX is an effective multivoxel evolutionary tool that can be used to tell where in the brain patterns responding to stimuli are.

Abstract
Permutation tests have extensively been used to estimate the significance of classification. Permutation tests usually use the test error as a dataset statistic to measure the difference between two or more populations. Then, to estimate the p-value(s), the test error is compared to a set of permuted test-error(s), which is usually obtained after permuting the labels of the populations. In this study, we investigate how several dataset factors, e.g., the number of samples, the number of classes, and the dimensionality size, may affect the p-value obtained via permutation tests. We performed the analysis using the standard permutation test procedure that uses the overall all test error dataset statistic and compared it to the permutation test procedure that uses per-class test error as a dataset statistic that we recently have proposed (doi:10.1016/j.neucom.2011.11.007). We found that permutation tests that use a per-class test error as a dataset statistic are not only more reliable in addressing the null hypothesis but also are highly sensitive to changes in the dataset factors that we investigated in this work. An important finding of this study is that when the dimensionality is low and the number of classes is up to several, say ten, highly above chance accuracy would be required to state the significance. For the same low dimensionality, however, slightly above chance accuracy would be adequate to state significance in a two-class problem.