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Showing papers in "Computational Intelligence and Neuroscience in 2010"


Journal ArticleDOI
TL;DR: This paper reviews the literature on SSVEP-based BCIs and comprehensively reports on the different RVS choices in terms of rendering devices, properties, and their potential influence on BCI performance, user safety and comfort.
Abstract: Brain-computer interface (BCI) systems based on the steady-state visual evoked potential (SSVEP) provide higher information throughput and require shorter training than BCI systems using other brain signals. To elicit an SSVEP, a repetitive visual stimulus (RVS) has to be presented to the user. The RVS can be rendered on a computer screen by alternating graphical patterns, or with external light sources able to emit modulated light. The properties of an RVS (e.g., frequency, color) depend on the rendering device and influence the SSVEP characteristics. This affects the BCI information throughput and the levels of user safety and comfort. Literature on SSVEP-based BCIs does not generally provide reasons for the selection of the used rendering devices or RVS properties. In this paper, we review the literature on SSVEP-based BCIs and comprehensively report on the different RVS choices in terms of rendering devices, properties, and their potential influence on BCI performance, user safety and comfort.

563 citations


Journal ArticleDOI
TL;DR: In this article, a P300-based BCI speller that uses a genetic algorithm (GA) to detect P300s, and an automatic error-correction system (ECS) based on the single-sweep detection of ErrPs.
Abstract: Error potentials (ErrPs), that is, alterations of the EEG traces related to the subject perception of erroneous responses, have been suggested to be an elegant way to recognize misinterpreted commands in brain-computer interface (BCI) systems. We implemented a P300-based BCI speller that uses a genetic algorithm (GA) to detect P300s, and added an automatic error-correction system (ECS) based on the single-sweep detection of ErrPs. The developed system was tested on-line on three subjects and here we report preliminary results. In two out of three subjects, the GA provided a good performance in detecting P300 (90% and 60% accuracy with 5 repetitions), and it was possible to detect ErrP with an accuracy (roughly 60%) well above the chance level. In our knowledge, this is the first time that ErrP detection is performed on-line in a P300-based BCI. Preliminary results are encouraging, but further refinements are needed to improve performances.

136 citations


Journal ArticleDOI
TL;DR: The new system is more efficient than P300-based BCI system in terms of accuracy, speed, applicability, and cost efficiency and it is possible to improve the communication abilities of those patients who can move their eyes.
Abstract: The aim of this study is to present electrooculogram signals that can be used for human computer interface efficiently. Establishing an efficient alternative channel for communication without overt speech and hand movements is important to increase the quality of life for patients suffering from Amyotrophic Lateral Sclerosis or other illnesses that prevent correct limb and facial muscular responses. We have made several experiments to compare the P300-based BCI speller and EOG-based new system. A five-letter word can be written on average in 25 seconds and in 105 seconds with the EEG-based device. Giving message such as "clean-up" could be performed in 3 seconds with the new system. The new system is more efficient than P300-based BCI system in terms of accuracy, speed, applicability, and cost efficiency. Using EOG signals, it is possible to improve the communication abilities of those patients who can move their eyes.

128 citations


Journal ArticleDOI
TL;DR: Electroencephalographic electrode principles and main points of electronic noise reduction methods in EEG signal acquisition front end are discussed, and some suggestions for improving signal acquisition are presented.
Abstract: The aim of this study is to present some practical state-of-the-art considerations in acquiring satisfactory signals for electroencephalographic signal acquisition. These considerations are important for users and system designers. Especially choosing correct electrode and design strategy of the initial electronic circuitry front end plays an important role in improving the system's measurement performance. Considering the pitfalls in the design of biopotential measurement system and recording session conditions creates better accuracy. In electroencephalogram (EEG) recording electrodes, system electronics including filtering, amplifying, signal conversion, data storing, and environmental conditions affect the recording performance. In this paper, EEG electrode principles and main points of electronic noise reduction methods in EEG signal acquisition front end are discussed, and some suggestions for improving signal acquisition are presented.

122 citations


Journal ArticleDOI
TL;DR: The observed brain network is consistent with existing models of visual object processing and attentional control and may serve as a basis for fMRI studies in clinical populations with neuropsychological deficits in Conners' CPT performance.
Abstract: Functional magnetic resonance imaging (fMRI) was performed in eight healthy subjects to identify the localization, magnitude, and volume extent of activation in brain regions that are involved in blood oxygen level-dependent (BOLD) response during the performance of Conners' Continuous Performance Test (CPT). An extensive brain network was activated during the task including frontal, temporal, and occipital cortical areas and left cerebellum. The more activated cluster in terms of volume extent and magnitude was located in the right anterior cingulate cortex (ACC). Analyzing the dynamic trend of the activation in the identified areas during the entire duration of the sustained attention test, we found a progressive decreasing of BOLD response probably due to a habituation effect without any deterioration of the performances. The observed brain network is consistent with existing models of visual object processing and attentional control and may serve as a basis for fMRI studies in clinical populations with neuropsychological deficits in Conners' CPT performance.

76 citations


Journal ArticleDOI
TL;DR: Investigating the influence of age via skull conductivity upon surface and subdermal bipolar EEG measurement sensitivity conducted on two realistic head models from the Visible Human Project indicates that both surface andSubdermal EEG measurements benefit better recordings in terms of precision and accuracy on younger patients.
Abstract: Bioelectric source measurements are influenced by the measurement location as well as the conductive properties of the tissues. Volume conductor effects such as the poorly conducting bones or the moderately conducting skin are known to affect the measurement precision and accuracy of the surface electroencephalography (EEG) measurements. This paper investigates the influence of age via skull conductivity upon surface and subdermal bipolar EEG measurement sensitivity conducted on two realistic head models from the Visible Human Project. Subdermal electrodes (a.k.a. subcutaneous electrodes) are implanted on the skull beneath the skin, fat, and muscles. We studied the effect of age upon these two electrode types according to the scalp-to-skull conductivity ratios of 5, 8, 15, and 30 : 1. The effects on the measurement sensitivity were studied by means of the half-sensitivity volume (HSV) and the region of interest sensitivity ratio (ROISR). The results indicate that the subdermal implantation notably enhances the precision and accuracy of EEG measurements by a factor of eight compared to the scalp surface measurements. In summary, the evidence indicates that both surface and subdermal EEG measurements benefit better recordings in terms of precision and accuracy on younger patients.

64 citations


Journal ArticleDOI
TL;DR: This work considers the problem of reconstructing finite energy stimuli encoded with a population of spiking leaky integrate-and-fire neurons, and forms the reconstruction as a spline interpolation problem for scalar as well as vector valued stimuli and shows that the recovery has a unique solution.
Abstract: We consider the problem of reconstructing finite energy stimuli encoded with a population of spiking leaky integrate-and-fire neurons. The reconstructed signal satisfies a consistency condition: when passed through the same neuron, it triggers the same spike train as the original stimulus. The recovered stimulus has to also minimize a quadratic smoothness optimality criterion. We formulate the reconstruction as a spline interpolation problem for scalar as well as vector valued stimuli and show that the recovery has a unique solution. We provide explicit reconstruction algorithms for stimuli encoded with single as well as a population of integrate-and-fire neurons. We demonstrate how our reconstruction algorithms can be applied to stimuli encoded with ON-OFF neural circuits with feedback. Finally, we extend the formalism to multi-input multi-output neural circuits and demonstrate that vector-valued finite energy signals can be efficiently encoded by a neural population provided that its size is beyond a threshold value. Examples are given that demonstrate the potential applications of our methodology to systems neuroscience and neuromorphic engineering.

62 citations


Journal ArticleDOI
TL;DR: Different spherical and realistic head modeling techniques in estimating EEG forward solutions from current dipole sources distributed on a standard cortical space reconstructed from Montreal Neurological Institute (MNI) MRI data are compared.
Abstract: The accuracy of forward models for electroencephalography (EEG) partly depends on head tissues geometry and strongly affects the reliability of the source reconstruction process, but it is not yet clear which brain regions are more sensitive to the choice of different model geometry. In this paper we compare different spherical and realistic head modeling techniques in estimating EEG forward solutions from current dipole sources distributed on a standard cortical space reconstructed from Montreal Neurological Institute (MNI) MRI data. Computer simulations are presented for three different four-shell head models, two with realistic geometry, either surface-based (BEM) or volume-based (FDM), and the corresponding sensor-fitted spherical-shaped model. Point Spread Function (PSF) and Lead Field (LF) cross-correlation analyses were performed for 26 symmetric dipole sources to quantitatively assess models' accuracy in EEG source reconstruction. Realistic geometry turns out to be a relevant factor of improvement, particularly important when considering sources placed in the temporal or in the occipital cortex.

61 citations


Journal ArticleDOI
TL;DR: A method to translate human EEG into music, so as to represent mental state by music, implied that different mental states may be identified by the corresponding music, and so the music from EEG may be a potential tool for EEG monitoring, biofeedback therapy, andSo forth.
Abstract: This paper proposes a method to translate human EEG into music, so as to represent mental state by music. The arousal levels of the brain mental state and music emotion are implicitly used as the bridge between the mind world and the music. The arousal level of the brain is based on the EEG features extracted mainly by wavelet analysis, and the music arousal level is related to the musical parameters such as pitch, tempo, rhythm, and tonality. While composing, some music principles (harmonics and structure) were taken into consideration. With EEGs during various sleep stages as an example, the music generated from them had different patterns of pitch, rhythm, and tonality. 35 volunteers listened to the music pieces, and significant difference in music arousal levels was found. It implied that different mental states may be identified by the corresponding music, and so the music from EEG may be a potential tool for EEG monitoring, biofeedback therapy, and so forth.

45 citations


Journal ArticleDOI
TL;DR: This contribution presents approaches to actually identify the individual neurons involved in assemblies, and may complement other methods and also provide a way to reduce data sets to the “relevant” neurons, thus allowing to carry out a refined analysis of the detailed correlation structure due to reduced computation time.
Abstract: The chance of detecting assembly activity is expected to increase if the spiking activities of large numbers of neurons are recorded simultaneously. Although such massively parallel recordings are now becoming available, methods able to analyze such data for spike correlation are still rare, as a combinatorial explosion often makes it infeasible to extend methods developed for smaller data sets. By evaluating pattern complexity distributions the existence of correlated groups can be detected, but their member neurons cannot be identified. In this contribution, we present approaches to actually identify the individual neurons involved in assemblies. Our results may complement other methods and also provide a way to reduce data sets to the "relevant" neurons, thus allowing us to carry out a refined analysis of the detailed correlation structure due to reduced computation time.

44 citations


Journal ArticleDOI
TL;DR: This work uses recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting ‘‘hidden’ Multivariate Autoregressive models and uses this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons.
Abstract: Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting "hidden" Multivariate Autoregressive models. We use this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons. We discuss the relative merits and limitations of the new method.

Journal ArticleDOI
TL;DR: An algorithm for amplitude-threshold spikes detection and a spike detection hardware design on FPGA, whose feasibility was verified in terms of CLBs number, memory occupation and temporal requirements; once realized, it will be able to execute on-line detection and real time waveform analysis, reducing data storage problems.
Abstract: Neurons cultured in vitro on MicroElectrode Array (MEA) devices connect to each other, forming a network. To study electrophysiological activity and long term plasticity effects, long period recording and spike sorter methods are needed. Therefore, on-line and real time analysis, optimization of memory use and data transmission rate improvement become necessary. We developed an algorithm for amplitude-threshold spikes detection, whose performances were verified with (a) statistical analysis on both simulated and real signal and (b) Big O Notation. Moreover, we developed a PCA-hierarchical classifier, evaluated on simulated and real signal. Finally we proposed a spike detection hardware design on FPGA, whose feasibility was verified in terms of CLBs number, memory occupation and temporal requirements; once realized, it will be able to execute on-line detection and real time waveform analysis, reducing data storage problems.

Journal ArticleDOI
TL;DR: Investigation of sleep spindles with Independent Component Analysis indicated that SCs can be extracted by reconstructing the EEG through back-projection of separate groups of ICs, based on a temporal and spectral analysis ofICs.
Abstract: Sleep spindles are bursts of sleep electroencephalogram (EEG) quasirhythmic activity within the frequency band of 11-16 Hz, characterized by progressively increasing, then gradually decreasing amplitude. The purpose of the present study was to process sleep spindles with Independent Component Analysis (ICA) in order to investigate the possibility of extracting, through visual analysis of the spindle EEG and visual selection of Independent Components (ICs), spindle "components" (SCs) corresponding to separate EEG activity patterns during a spindle, and to investigate the intracranial current sources underlying these SCs. Current source analysis using Low-Resolution Brain Electromagnetic Tomography (LORETA) was applied to the original and the ICA-reconstructed EEGs. Results indicated that SCs can be extracted by reconstructing the EEG through back-projection of separate groups of ICs, based on a temporal and spectral analysis of ICs. The intracranial current sources related to the SCs were found to be spatially stable during the time evolution of the sleep spindles.

Journal ArticleDOI
TL;DR: The sensitivity of DTI-derived indices to MS-related tissue abnormalities indicates that the optimized sequence may be a powerful tool in studies aimed at monitoring the disease course and severity.
Abstract: Background. Magnetic Resonance (MR) diffusion tensor imaging (DTI) is able to quantify in vivo tissue microstructure properties and to detect disease related pathology of the central nervous system. Nevertheless, DTI is limited by low spatial resolution associated with its low signal-to-noise-ratio (SNR). Aim. The aim is to select a DTI sequence for brain clinical studies, optimizing SNR and resolution. Methods and Results. We applied 6 methods for SNR computation in 26 DTI sequences with different parameters using 4 healthy volunteers (HV). We choosed two DTI sequences for their high SNR, they differed by voxel size and b-value. Subsequently, the two selected sequences were acquired from 30 multiple sclerosis (MS) patients with different disability and lesion load and 18 age matched HV. We observed high concordance between mean diffusivity (MD) and fractional anysotropy (FA), nonetheless the DTI sequence with smaller voxel size displayed a better correlation with disease progression, despite a slightly lower SNR. The reliability of corpus callosum (CC) fiber tracking with the chosen DTI sequences was also tested. Conclusion. The sensitivity of DTI-derived indices to MS-related tissue abnormalities indicates that the optimized sequence may be a powerful tool in studies aimed at monitoring the disease course and severity.

Journal ArticleDOI
TL;DR: It is found that local spiking activity can explain a significant portion of LFP power at most recording sites and it is demonstrated that removing the spike-correlated component can affect measurements of auditory tuning of the LFP.
Abstract: Neurophysiologists have recently become interested in studying neuronal population activity through local field potential (LFP) recordings during experiments that also record the activity of single neurons. This experimental approach differs from early LFP studies because it uses high impendence electrodes that can also isolate single neuron activity. A possible complication for such studies is that the synaptic potentials and action potentials of the small subset of isolated neurons may contribute disproportionately to the LFP signal, biasing activity in the larger nearby neuronal population to appear synchronous and cotuned with these neurons. To address this problem, we used linear filtering techniques to remove features correlated with spike events from LFP recordings. This filtering procedure can be applied for well-isolated single units or multiunit activity. We illustrate the effects of this correction in simulation and on spike data recorded from primary auditory cortex. We find that local spiking activity can explain a significant portion of LFP power at most recording sites and demonstrate that removing the spike-correlated component can affect measurements of auditory tuning of the LFP.

Journal ArticleDOI
TL;DR: A state-space model for estimating the spike rate function that provides a maximum likelihood estimate of the spike rates, model goodness-of-fit assessments, as well as confidence intervals for the Spike rate function and any other associated quantities of interest is described.
Abstract: The accurate characterization of spike firing rates including the determination of when changes in activity occur is a fundamental issue in the analysis of neurophysiological data. Here we describe a state-space model for estimating the spike rate function that provides amaximum likelihood estimate of the spike rate, model goodness-of-fit assessments, as well as confidence intervals for the spike rate function and any other associated quantities of interest. Using simulated spike data, we first compare the performance of the state-space approach with that of Bayesian adaptive regression splines (BARS) and a simple cubic spline smoothing algorithm. We show that the state-space model is computationally efficient and comparable with other spline approaches. Our results suggest both a theoretically sound and practical approach for estimating spike rate functions that is applicable to a wide range of neurophysiological data.

Journal ArticleDOI
TL;DR: Results show that the model of a single cortical region is able to simulate the occurrence of multiple power spectral density peaks; in particular the new inhibitory loop seems to have a critical role in the activation in gamma (γ) band, in agreement with experimental studies.
Abstract: An original neural mass model of a cortical region has been used to investigate the origin of EEG rhythms. The model consists of four interconnected neural populations: pyramidal cells, excitatory interneurons and inhibitory interneurons with slow and fast synaptic kinetics, GABAA, slow and GABAA, fast respectively. A new aspect, not present in previous versions, consists in the inclusion of a self-loop among GABAA, fast interneurons. The connectivity parameters among neural populations have been changed in order to reproduce different EEG rhythms. Moreover, two cortical regions have been connected by using different typologies of long range connections. Results show that the model of a single cortical region is able to simulate the occurrence of multiple power spectral density (PSD) peaks; in particular the new inhibitory loop seems to have a critical role in the activation in gamma (γ) band, in agreement with experimental studies. Moreover the effect of different kinds of connections between two regions has been investigated, suggesting that long range connections toward GABAA, fast interneurons have a major impact than connections toward pyramidal cells. The model can be of value to gain a deeper insight into mechanisms involved in the generation of γ rhythms and to provide better understanding of cortical EEG spectra.

Journal ArticleDOI
TL;DR: High-resolution EEG statistical techniques have been proved to be proved to able to generate useful insights about the particular fruition of TV messages, related to both commercial as well as political fields.
Abstract: The use of modern brain imaging techniques could be useful to understand what brain areas are involved in the observation of video clips related to commercial advertising, as well as for the support of political campaigns, and also the areas of Public Service Announcements (PSAs). In this paper we describe the capability of tracking brain activity during the observation of commercials, political spots, and PSAs with advanced high-resolution EEG statistical techniques in time and frequency domains in a group of normal subjects. We analyzed the statistically significant cortical spectral power activity in different frequency bands during the observation of a commercial video clip related to the use of a beer in a group of 13 normal subjects. In addition, a TV speech of the PrimeMinister of Italy was analyzed in two groups of swing and "supporter" voters. Results suggested that the cortical activity during the observation of commercial spots could vary consistently across the spot. This fact suggest the possibility to remove the parts of the spot that are not particularly attractive by using those cerebral indexes. The cortical activity during the observation of the political speech indicated a major cortical activity in the supporters group when compared to the swing voters. In this case, it is possible to conclude that the communication proposed has failed to raise attention or interest on swing voters. In conclusions, high-resolution EEG statistical techniques have been proved to able to generate useful insights about the particular fruition of TV messages, related to both commercial as well as political fields.

Journal ArticleDOI
TL;DR: A neural network model is used to elucidate the neural correlates of visual-tactile interactions in exogenous and endogenous attention and suggests that a competitive/cooperative interaction with biased competition may mediate both forms of cross-modal attention.
Abstract: Many studies have revealed that attention operates across different sensory modalities, to facilitate the selection of relevant information in the multimodal situations of every-day life. Cross-modal links have been observed either when attention is directed voluntarily (endogenous) or involuntarily (exogenous). The neural basis of cross-modal attention presents a significant challenge to cognitive neuroscience. Here, we used a neural network model to elucidate the neural correlates of visual-tactile interactions in exogenous and endogenous attention. The model includes two unimodal (visual and tactile) areas connected with a bimodal area in each hemisphere and a competition between the two hemispheres. The model is able to explain cross-modal facilitation both in exogenous and endogenous attention, ascribing it to an advantaged activation of the bimodal area on the attended side (via a top-down or bottom-up biasing), with concomitant inhibition towards the opposite side. The model suggests that a competitive/cooperative interaction with biased competition may mediate both forms of cross-modal attention.

Journal ArticleDOI
TL;DR: A robust method to help identify the population of neurons used for decoding motor tasks is developed and a new metric for quantifying the relative contribution of a neuron towards the decoded output, called “fractional sensitivity,” is developed.
Abstract: A robust method to help identify the population of neurons used for decoding motor tasks is developed. We use sensitivity analysis to develop a new metric for quantifying the relative contribution of a neuron towards the decoded output, called "fractional sensitivity." Previous model-based approaches for neuron ranking have been shown to largely depend on the collection of training data. We suggest the use of an ensembleof models that are trained on random subsets of trials to rank neurons. For this work, we tested a decoding algorithm on neuronal data recorded from two male rhesus monkeys while they performed a reach to grasp a bar at three orientations (45°, 90°, or 135°). An ensemble approach led to a statistically significant increase of 5% in decoding accuracy and 25% increase in identification accuracy of simulated noisy neurons, when compared to a single model. Furthermore, ranking neurons based on the ensemble fractional sensitivities resulted in decoding accuracies 10%-20% greater than when randomly selecting neurons or ranking based on firing rates alone. By systematically reducing the size of the input space, we determine the optimal number of neurons needed for decoding the motor output. This selection approach has practical benefits for other BMI applications where limited number of electrodes and training datasets are available, but high decoding accuracies are desirable.

Journal ArticleDOI
TL;DR: Findings show that the dcMEG with a bandwidth up to 8 Hz records both slow and faster neuronal responses, whereas the vascular response is confirmed to change on a scale of seconds.
Abstract: Neuronal and vascular responses due to finger movements were synchronously measured using dc-magnetoencephalography (dcMEG) and time-resolved near-infrared spectroscopy (trNIRS). The finger movements were monitored with electromyography (EMG). Cortical responses related to the finger movement sequence were extracted by independent component analysis from both the dcMEG and the trNIRS data. The temporal relations between EMG rate, dcMEG, and trNIRS responses were assessed pairwise using the cross-correlation function (CCF), which does not require epoch averaging. A positive lag on a scale of seconds was found for the maximum of the CCF between dcMEG and trNIRS. A zero lag is observed for the CCF between dcMEG and EMG. Additionally this CCF exhibits oscillations at the frequency of individual finger movements. These findings show that the dcMEG with a bandwidth up to 8 Hz records both slow and faster neuronal responses, whereas the vascular response is confirmed to change on a scale of seconds.

Journal ArticleDOI
TL;DR: Results of classification of units indicate that the developed classifier is able to isolate activity having linear relationship with muscle length, which is a step towards online model-based estimation of muscle length that can be used in a closed-loop functional electrical stimulation system with natural sensory feedback.
Abstract: Afferent muscle spindle activity in response to passive muscle stretch was recorded in vivo using thin-film longitudinal intrafascicular electrodes. A neural spike detection and classification scheme was developed for the purpose of separating activity of primary and secondary muscle spindle afferents. The algorithm is based on the multiscale continuous wavelet transform using complex wavelets. The detection scheme outperforms the commonly used threshold detection, especially with recordings having low signal-to-noise ratio. Results of classification of units indicate that the developed classifier is able to isolate activity having linear relationship with muscle length, which is a step towards online model-based estimation of muscle length that can be used in a closed-loop functional electrical stimulation system with natural sensory feedback.

Journal ArticleDOI
TL;DR: The time evolution of ridges in the wavelet spectrum of spike trains can be used for quantification of the dynamical stability of the neuronal responses to a stimulus to suggest that the neural coding scheme in trigeminal nuclei is not fixed, but instead it adapts to the stimulus characteristics.
Abstract: Sensory information handling is an essentially nonstationary process even under a periodic stimulation. We show how the time evolution of ridges in the wavelet spectrum of spike trains can be used for quantification of the dynamical stability of the neuronal responses to a stimulus. We employ this method to study neuronal responses in trigeminal nuclei of the rat provoked by tactile whisker stimulation. Neurons from principalis (Pr5) and interpolaris (Sp5i) show the maximal stability at the intermediate (50 ms) stimulus duration, whereas Sp5o cells "prefer" shorter (10 ms) stimulation. We also show that neurons in all three nuclei can perform as stimulus frequency filters. The response stability of about 33% of cells exhibits low-pass frequency dynamics. About 57% of cells have band-pass dynamics with the optimal frequency at 5 Hz for Pr5 and Sp5i, and 4 Hz for Sp5o, and the remaining 10% show no prominent dependence on the stimulus frequency. This suggests that the neural coding scheme in trigeminal nuclei is not fixed, but instead it adapts to the stimulus characteristics.

Journal ArticleDOI
TL;DR: The event-related hemodynamic response obtained from fMRI showed delayed BOLD peak latency in the contralateral primary motor area suggesting a less efficient activity of the neuronal populations driving fine movements, which are specifically impaired in ULD.
Abstract: We performed simultaneous acquisition of EEG-fMRI in seven patients with Unverricht-Lundborg disease (ULD) and in six healthy controls using self-paced finger extension as a motor task. The event-related desynchronization/synchronization (ERD/ERS) analysis showed a greater and more diffuse alpha desynchronization in central regions and a strongly reduced post-movement beta-ERS in patients compared with controls, suggesting a significant dysfunction of the mechanisms regulating active movement and movement end. The event-related hemodynamic response obtained from fMRI showed delayed BOLD peak latency in the contralateral primary motor area suggesting a less efficient activity of the neuronal populations driving fine movements, which are specifically impaired in ULD.

Journal ArticleDOI
TL;DR: The current study investigated the functional connectivity of the primary sensory system with resting state fMRI and applied such knowledge into the design of the neural architecture of autonomous humanoid robots, with separate processing units and another consolidation center to coordinate the two systems.
Abstract: The current study investigated the functional connectivity of the primary sensory system with resting state fMRI and applied such knowledge into the design of the neural architecture of autonomous humanoid robots. Correlation and Granger causality analyses were utilized to reveal the functional connectivity patterns. Dissociation was within the primary sensory system, in that the olfactory cortex and the somatosensory cortex were strongly connected to the amygdala whereas the visual cortex and the auditory cortex were strongly connected with the frontal cortex. The posterior cingulate cortex (PCC) and the anterior cingulate cortex (ACC) were found to maintain constant communication with the primary sensory system, the frontal cortex, and the amygdala. Such neural architecture inspired the design of dissociated emergent-response system and fine-processing system in autonomous humanoid robots, with separate processing units and another consolidation center to coordinate the two systems. Such design can help autonomous robots to detect and respond quickly to danger, so as to maintain their sustainability and independence.

Journal ArticleDOI
TL;DR: A body of mathematical techniques, known as high-resolution EEG, was developed to estimate precisely the cortical activity from noninvasive EEG measurements, and the rationale of a multimodal approach is that neural activity, modulating neuronal firing and generating EEG/MEG potentials, increases glucose and oxygen demands.
Abstract: Human neocortical processes involve temporal and spatial scales spanning several orders of magnitude, from the rapidly shifting somatosensory processes characterized by a temporal scale of milliseconds and a spatial scales of few square millimeters to the memory processes, involving time periods of seconds and spatial scale of square centimeters. Information about the brain activity can be obtained by measuring different physical variables arising from the brain processes, such as the increase in consumption of oxygen by the neural tissues or a variation of the electric potential over the scalp surface. All these variables are connected in direct or indirect way to the neural ongoing processes, and each variable has its own spatial and temporal resolution. The different neuroimaging techniques are then confined to the spatiotemporal resolution offered by the monitored variables. For instance, it is known from physiology that the temporal resolution of the hemodynamic deoxyhemoglobin increase/decrease lies in the range of 1-2 seconds, while its spatial resolution is generally observable with the current imaging techniques at few millimeter scale. Today, no neuroimaging method allows a spatial resolution on a millimeter scale and a temporal resolution on a millisecond scale. Nevertheless, the issue of several temporal and spatial domains is critical in the study of the brain functions, since different properties could become observable, depending on the spatiotemporal scales at which the brain processes are measured. It is well knownm that the electroencephalography (EEG) and magnetoencephalography (MEG) are useful techniques for the study of brain dynamics, due to their high temporal resolution. However, it has been said that the spatial resolution of the EEG is rather low, due to the different electrical conductivities of brain, skull, and scalp that markedly blur the EEG potential distributions, making the localization of the underlying cortical generators problematic. In the last ten years, a body of mathematical techniques, known as high-resolution EEG, was developed to estimate precisely the cortical activity from noninvasive EEG measurements. Such techniques include the use of a large number of scalp electrodes, realistic models of the head derived from magnetic resonance images (MRIs), and advanced processing methodologies related to the solution of the so-called “inverse problem,” that is, the estimation of the brain activity (i.e., electromagnetic generators) from the EEG/MEG measurements. The approach implies both the use of thousands of equivalent current dipoles as a source model and the realistic head models, reconstructed from magnetic resonance images, as the volume conductor medium. The use of geometrical constraints on the position of the neural source or sources within the head model generally reduces the solution space (i.e., the set of all possible combinations of the cortical dipoles strengths). An additional constraint is to force the dipoles to explain the recorded data with a minimum or a low amount of energy (minimum-norm solutions). The solution space can be further reduced by using information deriving from hemodynamic measures (i.e., fMRI-BOLD phenomena) recorded during the same task. The rationale of a multimodal approach is that neural activity, modulating neuronal firing and generating EEG/MEG potentials, increases glucose and oxygen demands. This results in an increase in the local hemodynamic response that can be measured by functional magnetic resonance images (fMRIs). Hence, fMRI responses and cortical sources of EEG/MEG data can be spatially related, and the fMRI information can be used as a prior in the solution of the inverse problem. As a result of all these computational approaches, it is possible to estimate the cortical activity with a spatial resolution of few millimeters and with a temporal resolution of milliseconds from noninvasive EEG measurements. In the framework of a COST Action BM0601 NeuroMath, there was organized a workshop in Rome in 2009 on the themes of the processing of neuroelectromagnetic and hemodynamic signals. Selected papers from this conference were subjected to standard peer-review and compiled in this special issue. With this issue we want to illustrate ongoing and emerging research in the development and application of mathematical methods to the recording, analysis, integration, and modeling of neural activity. The selected papers, written by world class scientists, cover diverse issues ranging from computational models to concrete applications of the methods within the neurosciences. We hope that the readership of CIN could appreciate this special issue as we appreciated it during its composition. Laura Astolfi Sara Gonzalez Andino Fabrizio De Vico Fallani Fabio Babiloni

Journal ArticleDOI
TL;DR: In this paper, a neural network model of object semantic representation is used to simulate learning of new words from a foreign language, where the network consists of feature areas, devoted to description of object properties, and a lexical area devoted to words representation.
Abstract: A neural network model of object semantic representation is used to simulate learning of new words from a foreign language. The network consists of feature areas, devoted to description of object properties, and a lexical area, devoted to words representation. Neurons in the feature areas are implemented as Wilson-Cowan oscillators, to allow segmentation of different simultaneous objects via gamma-band synchronization. Excitatory synapses among neurons in the feature and lexical areas are learned, during a training phase, via a Hebbian rule. In this work, we first assume that some words in the first language (L1) and the corresponding object representations are initially learned during a preliminary training phase. Subsequently, second-language (L2) words are learned by simultaneously presenting the new word together with the L1 one. A competitive mechanism between the two words is also implemented by the use of inhibitory interneurons. Simulations show that, after a weak training, the L2 word allows retrieval of the object properties but requires engagement of the first language. Conversely, after a prolonged training, the L2 word becomes able to retrieve object per se. In this case, a conflict between words can occur, requiring a higher-level decision mechanism.

Journal ArticleDOI
TL;DR: Signal processing and statistics have been playing a pivotal role in computational neuroscience and neural engineering research as mentioned in this paper, and a wide range of signal processing techniques and approaches for neural spike train analysis, including detection, sorting, encoding, and decoding.
Abstract: Signal processing and statistics have been playing a pivotal role in computational neuroscience and neural engineering research. Advances in technology have enabled us to simultaneously record extracellular neuronal signals through hundreds of electrode arrays, from which spike trains and local field potentials (LFPs) measurements are obtained. Neural spikes, also known as action potentials, are 0 and 1 observations recorded and subsequently extracted from the output of spiking neurons. To obtain a discrete form of neural spike trains, from either single or multiunits, raw neuronal signals have to be processed properly by some operations, such as filtering, thresholding, detection, and sorting. In the past, signal processing theories and algorithms have been applied for neural spike train analysis. The main objective of this special issue of Computational Intelligence and Neuroscience (CIN) is to provide a forum for revisiting some fundamental and important issues with newly developed signal-processing tools. This special issue includes 10 contributions that cover a wide range of signal-processing techniques and approaches for neural spike train analysis, including detection, sorting, encoding, and decoding. Loosely, these 10 papers may be classified into three categories. (1) Recording and Analysis of Neuronal Spikes from Single Neuron. The first paper “Quantitative estimation of the non-stationary behavior of neural spontaneous activity” by Destro-Filho et al. describes a quantitative approach to estimate the nonstationary behavior of neuronal spontaneous activity. The authors use a detrended fluctuation analysis (DFA) algorithm to estimate the “stationary time,” and apply the analysis to interspike interval time series to retrieve information from neural signals. These quantitative measures may help to characterize and reveal nonstationary nature underlying important physiological phenomena. The paper “Stability of neural firing in the trigeminal nuclei under mechanical whisker stimulation” by Makarov et al. describes a method that uses the time evolution of ridges in the wavelet transform spectrum of spike trains to quantify the dynamical stability of neuronal responses to a sensory stimulus. The nonstationary nature of the neuronal responses to tactile whisker stimulation is studied therein, showing that neurons from rat trigeminal nuclei can perform as stimulus frequency filter and adapt their neural coding scheme according to the stimulus characteristic. The paper “Spike sorting of muscle spindle afferent nerve activity recorded with thin-film intrafascicular electrodes” by Djilas et al. describes a neuronal spike detection and classification scheme for separating activity of primary and secondary afferents of muscle spindle activity. The detection algorithm, based on a multiscale continuous wavelet transform, is shown to outperform the standard threshold detection scheme especially under a low signal-to-noise ratio condition. Because of its strength of isolating activity related to the muscle length, this algorithm has a potential application for the closed-loop functional electrical stimulation (FES) system with natural sensory feedback. The paper “State-space algorithms for estimating spike rate functions” by Smith et al. addresses a fundamental question of determining changes in activity in neural firing, and the authors propose a state-space model to estimate the spike rate function and compare their approach with the established Bayesian adaptive regression splines (BARSs) algorithm and a cubic spline smoothing algorithm. Their algorithm is computationally efficient and is practically applicable to a wide range of neurophysiological data. (2) Signal Processing Algorithms for Analysis of Multiple Spike Trains from Neuronal Ensembles. The paper “Consistent recovery of sensory stimuli encoded with MIMO neural circuits” by Lazar and Pnevmatikakis describes a method for reconstructing finite-energy stimuli encoded with a population of spiking leaky integrate-and-fire neurons and demonstrates how the algorithm can be applied to stimuli encoded with recurrent or multiinput multioutput (MIMO) neural circuits. The paper “Multivariate auto-regressive modeling and Granger causality analysis of multiple spike trains” by Krumin and Shoham describes a new method for estimating the Granger causality of multiple spike trains, which generalizes the causality concept from continuous- to discrete-valued measurements. The new framework allows performing Granger causality analysis to extract the directed information flow pattern amongst neuronal ensembles. The paper “Efficient identification of assembly neurons within massively parallel spike trains” by Berger et al. describes a method for efficient identification of complexity patterns amongst assembly neurons using parallel spike train recordings. The authors propose a number of test statistics and use surrogate data to test the validity of their approach, which allows them to a refined analysis of the detailed high-order correlation structure with desirably reduced computational burden. (3) Signal Processing Applications. The paper “Development and validation of a spike detection and classification algorithm aimed at implementation on hardware devices” by Biffi et al. proposes a practical spike detection and classification algorithm aiming at FPGA (field-programmable gay array) hardware implementation, which requires online and real-time analysis, and efficient memory usage and data transmission rate. The proposed method using an amplitude-threshold detection and PCA-based hierarchical classifier is evaluated on simulated data with demonstrated efficiency. The paper “Ensemble fractional sensitivity: a quantitative approach to neuron selection for decoding motor tasks” by Singhal et al. proposes an ensemble fractional sensitivity metric for selecting population of neurons, which leads to an improvement in neural decoding accuracy of motor tasks. This approach has potential benefits in designing BCIs where high decoding accuracy is required given limited number of electrodes or training data. The last paper “Decoupling action potential bias from cortical local field potentials” by David et al. aims to solve a practical issue frequently occurring in experiments, where the LFP signals are corrupted by the bias contributed from action potentials induced by small subset of isolated neurons. The authors propose a linear filtering method to remove the features correlated with spike events from LFP recordings, and they show that removing spike-correlated components can affect the auditory tuning of the LFP in real data analysis. In summary, this special issue covers practical signal processing issues and new developments for neural spike train analysis in the growing field of computational neuroscience. We hope that the readership of CIN could appreciate this special issue and feel the excitement while reading these articles. That being said, we are reminded that there remain many open research questions that call for further development of signal-processing tools and applications. Lastly, the guest editors of this special issue are very grateful to all contributed authors, reviewers, and editorial staffs who had all put tremendous effort to make this issue a reality. Theodore W. Berger Zhe (Sage) Chen Andrzej Cichocki Karim G. Oweiss Rodrigo Quian Quiroga Nitish V. Thakor

Journal ArticleDOI
TL;DR: A method to measure the coupling strength between gamma signals on a short time scale as the maximum cross-correlation over a range of time lags within a sliding variable-width window indicates that this may be a useful method for mapping coupling patterns among signals in EEG datasets.
Abstract: An important goal in neuroscience is to identify instances when EEG signals are coupled. We employ a method to measure the coupling strength between gamma signals (40-100 Hz) on a short time scale as the maximum cross-correlation over a range of time lags within a sliding variable-width window. Instances of coupling states among several signals are also identified, using a mixed multivariate beta distribution to model coupling strength across multiple gamma signals with reference to a common base signal. We first apply our variable-window method to simulated signals and compare its performance to a fixed-window approach. We then focus on gamma signals recorded in two regions of the rat hippocampus. Our results indicate that this may be a useful method for mapping coupling patterns among signals in EEG datasets.

Journal ArticleDOI
TL;DR: A method based on linear data structures is proposed to define the fiber paths regarding their diffusivity and the implementation results are promising, so that the method will be developed as a rapid fiber tractography algorithm for the clinical use as future study.
Abstract: The brain activity during perception or cognition ismostly examined by functional magnetic resonance imaging (fMRI). However, the cause of the detected activity relies on the anatomy. Diffusion tensor magnetic resonance imaging (DTMRI) as a noninvasive modality providing in vivo anatomical information allows determining neural fiber connections which leads to brain mapping. Still a complete map of fiber paths representing the human brain is missing in literature. One of the main drawbacks of reliable fiber mapping is the correct detection of the orientation of multiple fibers within a single imaging voxel. In this study a method based on linear data structures is proposed to define the fiber paths regarding their diffusivity. Another advantage of the proposed method is that the analysis is applied on entire brain diffusion tensor data. The implementation results are promising, so that the method will be developed as a rapid fiber tractography algorithm for the clinical use as future study.