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BioSig is an open source software library for biomedical signal processing. The aim of the BioSig project is to foster research in biomedical signal processing by providing free and open source software tools for many different application areas. Some of the areas where BioSig can be employed are neuroinformatics, brain-computer interfaces, neurophysiology,(More)
Looking for somebody's face in a crowd is one of the most important examples of visual search. For this goal, attention has to be directed to a well-defined perceptual category. When this categorically selective process starts is, however, still unknown. To this end, we used magnetoencephalography (MEG) recorded over right human occipitotemporal cortex to(More)
We compare the performance of a chip-scale atomic magnetometer ͑CSAM͒ with that of a superconducting quantum interference device ͑SQUID͒ sensor in two biomedical applications. Magnetocardiograms ͑MCGs͒ of healthy human subjects were measured simultaneously by a CSAM and a multichannel SQUID sensor in a magnetically shielded room. The typical features of(More)
BACKGROUND Behavioral studies on facial emotion recognition yielded heterogeneous results in patients with Borderline Personality Disorder (BPD). Extrastriate cortex hyperactivation has been demonstrated in imaging studies in patients with BPD during face recognition, but electrophysiological studies are lacking. The aim was to investigate temporal(More)
Functional magnetic resonance imaging (fMRI) visualizes activated brain areas with a high spatial resolution. The activation signal is determined by the local change of cerebral blood oxygenation, blood volume and blood flow which serve as surrogate marker for the neuronal signal itself. Here, the complex coupling between these parameters and the(More)
The 170-ms electrophysiological processing stage (N170 in EEG, M170 in MEG) is considered an important computational step in face processing. Hence its neuronal sources have been modelled in several studies. The current study aimed to specify the relation of the dipolar sources underlying N170 and M170. Whole head EEG and MEG were measured simultaneously(More)
Standard analyses of neurophysiologically evoked response data rely on signal averaging across many epochs associated with specific events. The amplitudes and latencies of these averaged events are subsequently interpreted in the context of the given perceptual, motor, or cognitive tasks. Can such critical timing properties of event-related responses be(More)
To reduce physiological artifacts in magnetoencephalographic (MEG) and electroencephalographic recordings, a number of methods have been applied in the past such as principal component analysis, signal-space projection, regression using secondary information, and independent component analysis. This method has become popular as it does not have constraints(More)
First, the intrinsic random noise sources of a biopotential measurement in general are reviewed. For the special case of an electroencephalographic (EEG) measurement we have extended the commonly used amplifier noise model by biological generated background noise. As the strongest of all noise sources involved will dominate the resulting signal to noise(More)
Motivation ' ' ' From the multitude of algorithms described theoretically it is very difficult for the practitioner to choose a suitable algorithm for the task at hand. Only very few studies address this for experimental data. In the present study the focus is on the two most common biological artifacts in MEG data: and. [1-3] ' heart beat eye movements(More)