Volume 55, Issue 3 - Special Issue: NeuroMath

No abstract available

Volume conductor head models contain thin tissue layers, some of which have highly contrasting conductivity values relative to neighboring tissues. We expound the cerebrospinal fluid (CSF) and the six cortical layers of the gray matter. The dual nature of the CSF competes with the well-known shunting behavior of the skull. The incorporation of the six ultra thin cortical layers demonstrate the significance of the electrical attraction and shunting of lead field currents in multilayered tissues owing to the inherent conductive properties of each tissue. We relate the similar effects of the CSF to the diploë, i.e., the soft bone between the two hard bone layers of the skull. A natural subsequence of this article will allow researchers and clinicians to conceptually understand the measurement sensitivity distribution of a bipolar electroencephalography (EEG) lead. We recommend including the highly conductive thin layers such as the diploë of the skull and the CSF into head models as well as further investigation into the cortical layers I–VI of the gray matter. Comprehensively, when a thin tissue layer differs in relative conductivity from its neighboring layers, it should be included in the model owing to its influence upon the EEG lead fields, i.e., the measurement sensitivity distributions.

In electroencephalography (EEG) a valid conductor model of the head (forward model) is necessary for predicting measurable scalp voltages from intra-cranial current distributions. All inverse models, capable of inferring the spatial distribution of the neural sources generating measurable electrical and magnetic signals outside the brain are normally formulated in terms of a pre-estimated forward model, which implies considering one (or more) current dipole(s) inside the head and computing the electrical potentials generated at the electrode sites on the scalp surface. Therefore, the accuracy of the forward model strongly affects the reliability of the source reconstruction process independently of the specific inverse model. So far, it is as yet unclear which brain regions are more sensitive to the choice of different model geometry, from both quantitative and qualitative points of view. In this paper, we compare the finite difference method-based realistic model with the four-layers sensor-fitted spherical model using simulated cortical sources in the MNI152 standard space. We focused on the investigation of the spatial variation of the lead fields produced by simulated cortical sources which were placed on the reconstructed mesh of the neocortex along the surface electrodes of a 62-channel configuration. This comparison is carried out by evaluating a point spread function all over the brain cortex, with the aim of finding the lead fields mismatch between realistic and spherical geometry. Realistic geometry turns out to be a relevant factor of improvement which is particularly important when considering sources placed in the temporal or in the occipital cortex. In these situations, using a realistic head model will allow a better spatial discrimination of neural sources when compared to the spherical model.

Among various types of clustering methods, partition-based methods such as k-means and FCM are widely used in the analysis of such data. However, when duration between stimuli is different, such methods are not able to provide satisfactory results because they find equal size clusters according to the fundamental running principle of these methods. In such cases, neighborhood-based clustering methods can give more satisfactory results because measurement series are separated from one another according to dramatic breaking points. In recent years, bispectral index (BIS) monitoring, which is used for monitoring the level of anesthesia, has been used in sleep studies. Sleep stages are classically scored according to the Rechtschaffen and Kales (R&K) scoring system. BIS has been shown to have a strong correlation with the R&K scoring system. In this study, fuzzy neighborhood/density-based spatial clustering of applications with noise (FN-DBSCAN) that combines speed of the DBSCAN algorithm and robustness of the NRFJP algorithm is applied to BIS measurement series. As a result of experiments, we can conclude that, by using BIS data, the FN-DBSCAN method estimates sleep stages better than the fuzzy c-means method.

The aim of this study was to find distinctions of the EEG signal in female depression. Experiments were carried out on two groups of 18 female volunteers each: a group of patients with depressive disorder who were not on medication and a group of control subjects. Patients who had Hamilton depression rating scores higher than 14 were selected. Resting EEG was recorded for the duration of 30 min. Spectral asymmetry (SA) of the EEG spectrum was estimated as relative difference in the selected higher and lower EEG frequency band power. Calculated SA values were positive for depressive and negative for healthy subjects (except for 2–3 subjects). The values behaved similarly in all EEG channels and brain hemispheres. Differences in SA between depressive and control groups were significant in all EEG channels. Dependence of SA on EGG signal length appeared not to be identical for depressive and healthy subjects. Our results suggest that SA based on balance between the powers of the higher and the lower EEG frequency bands seems to enable characterization of the EEG in depression.

Brain-computer interfaces (BCIs) present an alternative way of communication for people with severe disabilities. One of the shortcomings in current BCI systems, recently put forward in the fourth BCI competition, is the asynchronous detection of motor imagery versus resting state. We investigated this extension to the three-class case, in which the resting state is considered virtually lying between two motor classes, resulting in a large penalty when one motor task is misclassified into the other motor class. We particularly focus on the behavior of different machine-learning techniques and on the role of multi-class cost-sensitive learning in such a context. To this end, four different kernel methods are empirically compared, namely pairwise multi-class support vector machines (SVMs), two cost-sensitive multi-class SVMs and kernel-based ordinal regression. The experimental results illustrate that ordinal regression performs better than the other three approaches when a cost-sensitive performance measure such as the mean-squared error is considered. By contrast, multi-class cost-sensitive learning enables us to control the number of large errors made between two motor tasks.

Brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs) require minimal user training and can offer higher information throughput compared to other BCI modalities. We focused on SSVEPs elicited by high-frequency stimuli (>30 Hz) because they cause minimal fatigue/annoyance and reduce the risk of inducing photoepileptic seizures. This paper presents an approach that analyzes electroencephalographic activity to automatically obtain the optimum spatial filter for detecting the SSVEP at a given stimulation frequency from a short signal where the stimulation is presented at intermittent periods interspersed with breaks. A vector space generated by sinusoidal signals at the stimulation frequency and harmonics is defined. The spatial filter coefficients result from maximizing the ratio between the energy of the spatially filtered signal and that of its orthogonal component with regard to the vector space. The spatial filters are customized for each BCI user through a short calibration procedure taking into account individual specificity. Our experiments on six subjects applying the spatial filters resulted in an average transfer rate ranging from 20.9 to 22.7 bits/min.

This paper presents the NeuroSelect software for managing the electronic depth control of cerebral CMOS-based microprobes for extracellular in vivo recordings. These microprobes contain up to 500 electronically switchable electrodes which can be appropriately selected with regard to specific neuron locations in the course of a recording experiment. NeuroSelect makes it possible to scan the electrodes electronically and to (re)select those electrodes of best signal quality resulting in a closed-loop design of a neural acquisition system. The signal quality is calculated by the relative power of the spikes compared with the background noise. The spikes are detected by an adaptive threshold using a robust estimator of the standard deviation. Electrodes can be selected in a manual or semi-automatic mode based on the signal quality. This electronic depth control constitutes a significant improvement for multielectrode probes, given that so far the only alternative has been the fine positioning by mechanical probe translation. In addition to managing communication with the hardware controller of the probe array, the software also controls acquisition, processing, display and storage of the neural signals for further analysis.