Two Different Approaches of Feature Extraction for Classifying the EEG Signals

The focus of this chapter is to study feature extraction and pattern classification methods from two medical areas, Stabilometry and Electroencephalography (EEG). Stabilometry is the branch of medicine responsible for examining balance in human beings. Balance and dizziness disorders are probably two of the most common illnesses that physicians have to deal with. In Stabilometry, the key nuggets of information in a time series signal are concentrated within definite time periods are known as events. In this chapter, two feature extraction schemes have been developed to identify and characterise the events in Stabilometry and EEG signals. Based on these extracted features, an Adaptive Fuzzy Inference Neural network has been applied for classification of Stabilometry and EEG signals.

A new feature extraction method for signal classification applied to cat spinal cord signals

In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods.

Mathematical modelling of neural processes within the oculomotor system

It is presented a mathematical model of the oculomotor plant, based on experimental data in cats. The system that generates, from the neuronal processes at the motoneuron, the control signals to the eye muscles that moves the eye. In contrast with previous models, that base the eye movement related motoneuron behavior on a first order linear differential equation, non-linear effects are described: A dependency on the eye angular position of the model parameters.

Anteroposterior neural tissue specification by activin-induced mesoderm

The transforming growth factor β superfamily member, activin, is able to induce mesodermal tissues in animal cap explants from Xenopus laevis blastula stage embryos. Activin can act like a morphogen of the dorsoventral axis in that lower doses induce more ventral, and higher doses more dorsal, tissue types. Activin has also previously been reported to induce neural tissues in animal caps. From cell mixing experiments it was inferred that this might be an indirect effect of induced mesoderm signaling to uninduced ectoderm. Here we demonstrate directly that neural tissues do indeed arise by the action of induced mesoderm on uninduced ectoderm. Dorsal mesoderm is itself subdivided into posterior and anterior domains in vivo, but this had not been demonstrated for induced mesoderm. We therefore tested whether different concentrations of activin recreate these different anteroposterior properties as well. We show that the anteroposterior positional value of induced mesoderm, including its neuroinductive properties, depends on the dose of activin applied to the mesoderm, with lower doses inducing more posterior and higher doses giving more anterior markers. We discuss the implications of these results for patterning signals and the relationship between anteroposterior and dorsoventral axes.

Integration of multiple instructive cues by neural crest stem cells reveals cell-intrinsic biases in relative growth factor responsiveness

Growth factors can influence lineage determination of neural crest stem cells (NCSCs) in an instructive manner, in vitro. Because NCSCs are likely exposed to multiple signals in vivo, these findings raise the question of how stem cells would integrate such combined influences. Bone morphogenetic protein 2 (BMP2) promotes neuronal differentiation and glial growth factor 2 (GGF2) promotes glial differentiation; if NCSCs are exposed to saturating concentrations of both factors, BMP2 appears dominant. By contrast, if the cells are exposed to saturating concentrations of both BMP2 and transforming growth factor β1 (which promotes smooth muscle differentiation), the two factors appear codominant. Sequential addition experiments indicate that NCSCs require 48–96 hrs in GGF2 before they commit to a glial fate, whereas the cells commit to a smooth muscle fate within 24 hr in transforming growth factor β1. The delayed response to GGF2 does not reflect a lack of functional receptors; however, because the growth factor induces rapid mitogen-activated protein kinase phosphorylation in naive cells. Furthermore, GGF2 can attenuate induction of the neurogenic transcription factor mammalian achaete-scute homolog 1, by low doses of BMP2. This short-term antineurogenic influence of GGF2 is not sufficient for glial lineage commitment, however. These data imply that NCSCs exhibit cell-intrinsic biases in the timing and relative dosage sensitivity of their responses to instructive factors that influence the outcome of lineage decisions in the presence of multiple factors. The relative delay in glial lineage commitment, moreover, apparently reflects successive short-term and longer-term actions of GGF2. Such a delay may help to explain why glia normally differentiate after neurons, in vivo.

Neural mechanisms underlying binocular fusion and stereopsis: Position vs. phase

The visual system utilizes binocular disparity to discriminate the relative depth of objects in space. Since the striate cortex is the first site along the central visual pathways at which signals from the left and right eyes converge onto a single neuron, encoding of binocular disparity is thought to begin in this region. There are two possible mechanisms for encoding binocular disparity through simple cells in the striate cortex: a difference in receptive field (RF) position between the two eyes (RF position disparity) and a difference in RF profile between the two eyes (RF phase disparity). Although there have been studies supporting each of the two encoding mechanisms, both mechanisms have not been examined in a single study. Therefore, the relative roles of the two mechanisms have not been determined. To address this issue, we have mapped left and right eye RFs of simple cells in the cat’s striate cortex using binary m-sequence noise, and then we have estimated RF position and phase disparities. We find that RF position disparities are generally limited to small values that are not sufficient to encode large binocular disparities. In contrast, RF phase disparities cover a wide range of binocular disparities and exhibit dependencies on orientation and spatial frequency in a manner expected for a mechanism that encodes binocular disparity. These results indicate that binocular disparity is mainly encoded through RF phase disparity. However, RF position disparity may play a significant role for cells with high spatial frequency selectivity, which are constrained to small RF phase disparities.

In vitro-generated neural precursors participate in mammalian brain development

During embryogenesis, pluripotent stem cells segregate into daughter lineages of progressively restricted developmental potential. In vitro, this process has been mimicked by the controlled differentiation of embryonic stem cells into neural precursors. To explore the developmental potential of these cell-culture-derived precursors in vivo, we have implanted them into the ventricles of embryonic rats. The transplanted cells formed intraventricular neuroepithelial structures and migrated in large numbers into the brain tissue. Embryonic-stem-cell-derived neurons, astrocytes, and oligodendrocytes incorporated into telencephalic, diencephalic, and mesencephalic regions and assumed phenotypes indistinguishable from neighboring host cells. These observations indicate that entirely in vitro-generated neural precursors are able to respond to environmental signals guiding cell migration and differentiation and have the potential to reconstitute neuronal and glial lineages in the central nervous system.

An approach to probe some neural systems interaction by functional MRI at neural time scale down to milliseconds

In this paper, we demonstrate an approach by which some evoked neuronal events can be probed by functional MRI (fMRI) signal with temporal resolution at the time scale of tens of milliseconds. The approach is based on the close relationship between neuronal electrical events and fMRI signal that is experimentally demonstrated in concurrent fMRI and electroencephalographic (EEG) studies conducted in a rat model with forepaw electrical stimulation. We observed a refractory period of neuronal origin in a two-stimuli paradigm: the first stimulation pulse suppressed the evoked activity in both EEG and fMRI signal responding to the subsequent stimulus for a period of several hundred milliseconds. When there was an apparent site–site interaction detected in the evoked EEG signal induced by two stimuli that were primarily targeted to activate two different sites in the brain, fMRI also displayed signal amplitude modulation because of the interactive event. With visual stimulation using two short pulses in the human brain, a similar refractory phenomenon was observed in activated fMRI signals in the primary visual cortex. In addition, for interstimulus intervals shorter than the known latency time of the evoked potential induced by the first stimulus (≈100 ms) in the primary visual cortex of the human brain, the suppression was not present. Thus, by controlling the temporal relation of input tasks, it is possible to study temporal evolution of certain neural events at the time scale of their evoked electrical activity by noninvasive fMRI methodology.

Signals and signs in the nervous system: The dynamic anatomy of electrical activity is probably information-rich

The dichotomy between two groups of workers on neuroelectrical activity is retarding progress. To study the interrelations between neuronal unit spike activity and compound field potentials of cell populations is both unfashionable and technically challenging. Neither of the mutual disparagements is justified: that spikes are to higher functions as the alphabet is to Shakespeare and that slow field potentials are irrelevant epiphenomena. Spikes are not the basis of the neural code but of multiple codes that coexist with nonspike codes. Field potentials are mainly information-rich signs of underlying processes, but sometimes they are also signals for neighboring cells, that is, they exert influence. This paper concerns opportunities for new research with many channels of wide-band (spike and slow wave) recording. A wealth of structure in time and three-dimensional space is different at each scale—micro-, meso-, and macroactivity. The depth of our ignorance is emphasized to underline the opportunities for uncovering new principles. We cannot currently estimate the relative importance of spikes and synaptic communication vs. extrasynaptic graded signals. In spite of a preponderance of literature on the former, we must consider the latter as probably important. We are in a primitive stage of looking at the time series of wide-band voltages in the compound, local field, potentials and of choosing descriptors that discriminate appropriately among brain loci, states (functions), stages (ontogeny, senescence), and taxa (evolution). This is not surprising, since the brains in higher species are surely the most complex systems known. They must be the greatest reservoir of new discoveries in nature. The complexity should not deter us, but a dose of humility can stimulate the flow of imaginative juices.

Sonic hedgehog protein signals not as a hydrolytic enzyme but as an apparent ligand for Patched

The amino-terminal signaling domain of the Sonic hedgehog secreted protein (Shh-N), which derives from the Shh precursor through an autoprocessing reaction mediated by the carboxyl-terminal domain, executes multiple functions in embryonic tissue patterning, including induction of ventral and suppression of dorsal cell types in the developing neural tube. An apparent catalytic site within Shh-N is suggested by structural homology to a bacterial carboxypeptidase. We demonstrate here that alteration of residues presumed to be critical for a hydrolytic activity does not cause a loss of inductive activity, thus ruling out catalysis by Shh-N as a requirement for signaling. We favor the alternative, that Shh-N functions primarily as a ligand for the putative receptor Patched (Ptc). This possibility is supported by new evidence for direct binding of Shh-N to Ptc and by a strong correlation between the affinity of Ptc-binding and the signaling potency of Shh-N protein variants carrying alterations of conserved residues in a particular region of the protein surface. These results together suggest that direct Shh-N binding to Ptc is a critical event in transduction of the Shh-N signal.

Song selectivity and sensorimotor signals in vocal learning and production

Bird song, like human speech, is a learned vocal behavior that requires auditory feedback. Both as juveniles, while they learn to sing, and as adults, songbirds use auditory feedback to compare their own vocalizations with an internal model of a target song. Here we describe experiments that explore a role for the songbird anterior forebrain pathway (AFP), a basal ganglia-forebrain circuit, in evaluating song feedback and modifying vocal output. First, neural recordings in anesthetized, juvenile birds show that single AFP neurons are specialized to process the song stimuli that are compared during sensorimotor learning. AFP neurons are tuned to both the bird's own song and the tutor song, even when these stimuli are manipulated to be very different from each other. Second, behavioral experiments in adult birds demonstrate that lesions to the AFP block the deterioration of song that normally follows deafening. This observation suggests that deafening results in an instructive signal, indicating a mismatch between feedback and the internal song model, and that the AFP is involved in generating or transmitting this instructive signal. Finally, neural recordings from behaving birds reveal robust singing-related activity in the AFP. This activity is likely to originate from premotor areas and could be modulated by auditory feedback of the bird's own voice. One possibility is that this activity represents an efference copy, predicting the sensory consequences of motor commands. Overall, these studies illustrate that sensory and motor processes are highly interrelated in this circuit devoted to vocal learning, as is true for brain areas involved in speech.

Simple neural networks for the amplification and utilization of small changes in neuron firing rates

I describe physiologically plausible “voter-coincidence” neural networks such that secondary “coincidence” neurons fire on the simultaneous receipt of sufficiently large sets of input pulses from primary sets of neurons. The networks operate such that the firing rate of the secondary, output neurons increases (or decreases) sharply when the mean firing rate of primary neurons increases (or decreases) to a much smaller degree. In certain sensory systems, signals that are generally smaller than the noise levels of individual primary detectors, are manifest in very small increases in the firing rates of sets of afferent neurons. For such systems, this kind of network can act to generate relatively large changes in the firing rate of secondary “coincidence” neurons. These differential amplification systems can be cascaded to generate sharp, “yes–no” spike signals that can direct behavioral responses.

Disruption of the MacMARCKS gene prevents cranial neural tube closure and results in anencephaly.

MacMARCKS is a member of the MARCKS family of protein kinase C (PKC) substrates. Biochemical evidence demonstrates that these proteins integrate calcium and PKC-dependent signals to regulate actin structure at the membrane. We report here that deletion of the MacMARCKS gene prevents cranial neural tube closure in the developing brain, resulting in anencephaly. This suggests a central role for MacMARCKS and the PKC signal transduction pathway in the folding of the anterior neural plate during the early phases of brain formation, and supports the hypothesis that actin-based motility directs cranial neural tube closure.

A complexity measure for selective matching of signals by the brain.

We have previously derived a theoretical measure of neural complexity (CN) in an attempt to characterize functional connectivity in the brain. CN measures the amount and heterogeneity of statistical correlations within a neural system in terms of the mutual information between subsets of its units. CN was initially used to characterize the functional connectivity of a neural system isolated from the environment. In the present paper, we introduce a related statistical measure, matching complexity (CM), which reflects the change in CN that occurs after a neural system receives signals from the environment. CM measures how well the ensemble of intrinsic correlations within a neural system fits the statistical structure of the sensory input. We show that CM is low when the intrinsic connectivity of a simulated cortical area is randomly organized. Conversely, CM is high when the intrinsic connectivity is modified so as to differentially amplify those intrinsic correlations that happen to be enhanced by sensory input. When the input is represented by an individual stimulus, a positive value of CM indicates that the limited mutual information between sensory sheets sampling the stimulus and the rest of the brain triggers a large increase in the mutual information between many functionally specialized subsets within the brain. In this way, a complex brain can deal with context and go "beyond the information given."

Processing of speech signals for physical and sensory disabilities.

Assistive technology involving voice communication is used primarily by people who are deaf, hard of hearing, or who have speech and/or language disabilities. It is also used to a lesser extent by people with visual or motor disabilities. A very wide range of devices has been developed for people with hearing loss. These devices can be categorized not only by the modality of stimulation [i.e., auditory, visual, tactile, or direct electrical stimulation of the auditory nerve (auditory-neural)] but also in terms of the degree of speech processing that is used. At least four such categories can be distinguished: assistive devices (a) that are not designed specifically for speech, (b) that take the average characteristics of speech into account, (c) that process articulatory or phonetic characteristics of speech, and (d) that embody some degree of automatic speech recognition. Assistive devices for people with speech and/or language disabilities typically involve some form of speech synthesis or symbol generation for severe forms of language disability. Speech synthesis is also used in text-to-speech systems for sightless persons. Other applications of assistive technology involving voice communication include voice control of wheelchairs and other devices for people with mobility disabilities.