Abstract-The use of EEG biometrics, for the purpose of automatic people recognition, has received increasing attention in the recent years. Most of current analysis rely on the extraction of features characterizing the activity of single brain regions, like power-spectrum estimates, thus neglecting possible temporal dependencies between the generated EEG signals. However, important physiological information can be extracted from the way different brain regions are functionally coupled. In this study, we propose a novel approach that fuses spectral coherencebased connectivity between different brain regions as a possibly viable biometric feature. The proposed approach is tested on a large dataset of subjects (N=108) during eyes-closed (EC) and eyes-open (EO) resting state conditions. The obtained recognition performances show that using brain connectivity leads to higher distinctiveness with respect to power-spectrum measurements, in both the experimental conditions. Notably, a 100% recognition accuracy is obtained in EC and EO when integrating functional connectivity between regions in the frontal lobe, while a lower 97.41% is obtained in EC (96.26% in EO) when fusing power spectrum information from centro-parietal regions. Taken together, these results suggest that functional connectivity patterns represent effective features for improving EEG-based biometric systems.

Synchronous brain activity in motor cortex in perception or in complex cognitive processing has been the subject of several studies. The advanced analysis of cerebral electro-physiological activity during the course of planning (PRE) or execution of movement (EXE) in a high temporal resolution could reveal interesting information about the brain functional organization in patients following stroke damage. High-power (128 channels) electroencephalography registration was carried out on 8 healthy subjects and on a patient with stroke with capsular lacuna in the right hemisphere. For activation of motor cortex, the finger tapping paradigm was used. In this preliminary study, we tested a theoretical graph approach to characterize the task-related spectral coherence. All of the obtained brain functional networks were analyzed by the connectivity degree, the degree distribution, and efficiency parameters in the Theta, Alpha, Beta, and Gamma bands during the PRE and EXE intervals. All the brain networks were found to hold a regular and ordered topology. However, significant differences (P < 0.01) emerged between the patient with stroke and the control subjects, independently of the neural processes related to the PRE or EXE periods. In the Beta (13-29 Hz) and Gamma (30-40 Hz) bands, the significant (P < 0.01) decrease in globaland local-efficiency in the patient's networks, reflected a lower capacity to integrate communication between distant brain regions and a lower tendency to be modular. This weak organization is principally due to the significant (P < 0.01 Bonferroni corrected) increase in disconnected nodes together with the significant increase in the links in some other crucial vertices.

Abstract-The classification, monitoring, and compression of electrocardiogram (ECG) signals recorded of a single patient over a relatively long period of time is considered. The particular application we have in mind is high-resolution ECG analysis, such as late potential analysis, morphology changes in QRS during arrythmias, -wave alternants, or the study of drug effects on ventricular activation. We propose to apply a modification of a classical method of cluster analysis or vector quantization. The novelty of our approach is that we use a new distortion measure to quantify the distance of two ECG cycles, and the class-distortion measure is defined using a min-max criterion. The new class-distortion-measure is much more sensitive to outliers than the usual distortion measures using average-distance. The price of this practical advantage is that computational complexity is significantly increased. The resulting nonsmooth optimization problem is solved by an adapted version of the simultaneous perturbation stochastic approximation (SPSA) method of [1]. The main idea is to generate a smooth approximation by a randomization procedure. The viability of the method is demonstrated on both simulated and real data. An experimental comparison with the widely used correlation method is given on real data.

Within-group variability of body surface potential maps was assessed on data from 685 carefully validated normal subjects (348 men and 337 women). Sources of within-group variability were evaluated by subgrouping maps by patient sex, age, height, and weight. Contribution of reproducibility error to total variance was assessed in a separate group of 52 normal subjects in whom multiple maps were recorded. Total variance was significantly lower in women than in men. Total variance tended to decrease with age, and the greatest decrease occurred in men during the 3rd decade. The ratio of total variance to mean signal energy showed a slow decrease with age for each group. Results suggest that the dominant source of within-group variability arises from variability of cardiac electric sources while the influence of volume conductor variability is significantly less. Variability due to measurement reproducibility was approximately half of the total variance. (Circulation 1989;79:1077-1083 T he performance of statistical methods for classifying body surface potential maps ultimately depends on the extent of overlap of the probability distributions representing the classes to be separated. Even if the class distributions overlap, there is a chance of improving diagnostic performance by using additional information, such as geometry, to form tighter probability distribution subgroups within a clinically homogeneous population. In this case, reduction of overlap should result in improved classification accuracy. To find a reasonable, small number of efficient additional parameters, a better understanding of the sources of within-group scattering is needed. If it is assumed that the sources of within-group variability are similar in all clinically homogeneous classes, then the conclusions drawn from the study of one class can be cautiously extended to others.Considerable experimental and theoretical information has been published about the main sources of within-group variability in electrocardiographic data.'-5 Parameters of the heart (geometrical and electrical) and the body (geometrical and conductive) can be considered random variables, which contribute to the resultant scatter of measured potential field parameters. Also, errors in electrode placement (reproducibility errors) contribute to total

A multi-scale modeling approach is proposed in this paper that assists the user in constructing musculoskeletal system models from sub-models describing various mechanisms on different levels on the length scale. In addition, dynamic timescale analysis has been performed on the developed multi-scale models of various parts of a human limb: on wrist, elbow and shoulder characterized by different maximal muscle and skeletal length properties. The timescale analysis results have been represented on a scale-map, that can be used effectively to direct the simplification of multi-scale models for control-related application purposes.

No abstract

No abstract

Electroencephalography (EEG) signals are frequently contaminated by ocular, muscle, and cardiac artefacts whose removal normally requires manual inspection or the use of reference channels (EOG, EMG, ECG). We present a novel, fully automatic method for the detection and removal of ECG artefacts that works without a reference ECG channel. Independent Component Analysis (ICA) is applied to the measured data and the independent components are examined for the presence of QRS waveforms using an adaptive threshold-based QRS detection algorithm. Detected peaks are subsequently classified by a rule-based classifier as ECG or non-ECG components. Components manifesting ECG activity are marked for removal, and then the artefact-free signal is reconstructed by removing these components before performing the inverse ICA. The performance of the proposed method is evaluated on a number of EEG datasets and compared to results reported in the literature. The average sensitivity of our ECG artefact removal method is above 99 %, which is better than known literature results.