EEG Potentials Research Articles

Investigating TMS-EEG Indices of Long-Interval Intracortical Inhibition at Different Interstimulus Intervals.

Long-interval intracortical inhibition (LICI) is a transcranial magnetic stimulation (TMS) paradigm that uses paired magnetic stimuli separated by 100-200 ms to investigate the activity of cortical GABAergic interneurons. While commonly applied, the mechanisms contributing to LICI are not well understood, and growing evidence suggests that inhibition observed at different interstimulus intervals (ISI) may involve non-identical processes. This study aims to utilise combined TMS-EEG to more thoroughly characterise LICI at different ISIs, as the TMS-evoked EEG potential (TEP) can provide more direct insight into the cortical response to stimulation that is not subject to variations in spinal cord excitability that can confound the motor evoked potential (MEP). In 12 subjects (22.6 ± 0.9 years), LICI was applied using two ISIs of 100 ms (LICI100) and 150 ms (LICI150), while TEPs were recorded using simultaneous high-definition EEG. Analysis of EEG data within a region of interest (C3 electrode) showed that test alone stimulation produced three consistent TEP peaks (corresponding to P30, N100 and P180) that were all significantly inhibited following paired-pulse stimulation. However, for P30, inhibition varied between LICI conditions, with reduced amplitude following LICI100 (P = 0.03) but not LICI150 (P = 0.3). In contrast, the N100 and P180 were significantly reduced by LICI at both intervals (all P-values < 0.05). In addition, topographical analyses suggested that the global change in P30, N40 and P180 differed between LICI conditions. These findings suggest that LICI100 and LICI150 reflect complex measurements of cortical inhibition with differential contributions from comparable circuits.

Normal Brain Response to Propofol in Advance of Recovery from Unresponsive Wakefulness Syndrome.

Up to 40% of individuals with unresponsive wakefulness syndrome (UWS) actually might be conscious. Recent attempts to detect covert consciousness in behaviorally unresponsive patients via neurophysiological patterns are limited by the need to compare data from brain-injured patients to healthy controls. In this report, we pilot an alternative within-subject approach by using propofol to perturb the brain state of a patient diagnosed with UWS. An auditory stimulation series was presented to the patient before, during, and after exposure to propofol while high-density electroencephalograph (EEG) was recorded. Baseline analysis revealed residual markers in the continuous EEG and event-related potentials (ERPs) that have been associated with conscious processing. However, these markers were significantly distorted by the patient’s pathology, challenging the interpretation of their functional significance. Upon exposure to propofol, changes in EEG characteristics were similar to what is seen in healthy individuals and ERPs associated with conscious processing disappeared. At the 1-month follow up, the patient had regained consciousness. We offer three alternative explanations for these results: (1) the patient was covertly consciousness, and was anesthetized by propofol administration; (2) the patient was unconscious, and the observed EEG changes were a propofol-specific phenomenon; and (3) the patient was unconscious, but his brain networks responded normally in a way that heralded the possibility of recovery. These alternatives will be tested in a larger study, and raise the intriguing possibility of using a general anesthetic as a probe of brain states in behaviorally unresponsive patients.

Prolonged dry apnoea: effects on brain activity and physiological functions in breath-hold divers and non-divers.

The aim of the study was to investigate the effects of voluntary breath-holding on brain activity and physiological functions. We hypothesised that prolonged apnoea would trigger cerebral hypoxia, resulting in a decrease of brain performance; and the apnoea's effects would be more pronounced in breath-hold divers. Trained breath-hold divers and non-divers performed maximal dry breath-holdings. Lung volume, alveolar partial pressures of O2 and CO2, attention and anxiety levels were estimated. Heart rate, blood pressure, arterial blood oxygenation, brain tissue oxygenation, EEG, and DC potential were monitored continuously during breath-holding. There were a few significant changes in electrical brain activity caused by prolonged apnoea. Brain tissue oxygenation index and DC potential were relatively stable up to the end of the apnoea in breath-hold divers and non-divers. We also did not observe any decrease of attention level or speed of processing immediately after breath-holding. Interestingly, trained breath-hold divers had some peculiarities in EEG activity at resting state (before any breath-holding): non-spindled, sharpened alpha rhythm; slowed-down alpha with the frequency nearer to the theta band; and untypical spatial pattern of alpha activity. Our findings contradicted the primary hypothesis. Apnoea up to 5min does not lead to notable cerebral hypoxia or a decrease of brain performance in either breath-hold divers or non-divers. It seems to be the result of the compensatory mechanisms similar to the diving response aimed at centralising blood circulation and reducing peripheral O2 uptake. Adaptive changes during apnoea are much more prominent in trained breath-hold divers.

Gait adaptation to visual kinematic perturbations using a real-time closed-loop brain–computer interface to a virtual reality avatar

Objective. The control of human bipedal locomotion is of great interest to the field of lower-body brain–computer interfaces (BCIs) for gait rehabilitation. While the feasibility of closed-loop BCI systems for the control of a lower body exoskeleton has been recently shown, multi-day closed-loop neural decoding of human gait in a BCI virtual reality (BCI-VR) environment has yet to be demonstrated. BCI-VR systems provide valuable alternatives for movement rehabilitation when wearable robots are not desirable due to medical conditions, cost, accessibility, usability, or patient preferences. Approach. In this study, we propose a real-time closed-loop BCI that decodes lower limb joint angles from scalp electroencephalography (EEG) during treadmill walking to control a walking avatar in a virtual environment. Fluctuations in the amplitude of slow cortical potentials of EEG in the delta band (0.1–3 Hz) were used for prediction; thus, the EEG features correspond to time-domain amplitude modulated potentials in the delta band. Virtual kinematic perturbations resulting in asymmetric walking gait patterns of the avatar were also introduced to investigate gait adaptation using the closed-loop BCI-VR system over a period of eight days. Main results. Our results demonstrate the feasibility of using a closed-loop BCI to learn to control a walking avatar under normal and altered visuomotor perturbations, which involved cortical adaptations. The average decoding accuracies (Pearson’s r values) in real-time BCI across all subjects increased from (Hip: 0.18 ± 0.31; Knee: 0.23 ± 0.33; Ankle: 0.14 ± 0.22) on Day 1 to (Hip: 0.40 ± 0.24; Knee: 0.55 ± 0.20; Ankle: 0.29 ± 0.22) on Day 8. Significance. These findings have implications for the development of a real-time closed-loop EEG-based BCI-VR system for gait rehabilitation after stroke and for understanding cortical plasticity induced by a closed-loop BCI-VR system.

Intracranial EEG potentials estimated from MEG sources: A new approach to correlate MEG and iEEG data in epilepsy.

Detection of epileptic spikes in MagnetoEncephaloGraphy (MEG) requires synchronized neuronal activity over a minimum of 4cm2. We previously validated the Maximum Entropy on the Mean (MEM) as a source localization able to recover the spatial extent of the epileptic spike generators. The purpose of this study was to evaluate quantitatively, using intracranial EEG (iEEG), the spatial extent recovered from MEG sources by estimating iEEG potentials generated by these MEG sources. We evaluated five patients with focal epilepsy who had a pre-operative MEG acquisition and iEEG with MRI-compatible electrodes. Individual MEG epileptic spikes were localized along the cortical surface segmented from a pre-operative MRI, which was co-registered with the MRI obtained with iEEG electrodes in place for identification of iEEG contacts. An iEEG forward model estimated the influence of every dipolar source of the cortical surface on each iEEG contact. This iEEG forward model was applied to MEG sources to estimate iEEG potentials that would have been generated by these sources. MEG-estimated iEEG potentials were compared with measured iEEG potentials using four source localization methods: two variants of MEM and two standard methods equivalent to minimum norm and LORETA estimates. Our results demonstrated an excellent MEG/iEEG correspondence in the presumed focus for four out of five patients. In one patient, the deep generator identified in iEEG could not be localized in MEG. MEG-estimated iEEG potentials is a promising method to evaluate which MEG sources could be retrieved and validated with iEEG data, providing accurate results especially when applied to MEM localizations. Hum Brain Mapp 37:1661-1683, 2016. © 2016 Wiley Periodicals, Inc.

Brain Oscillations in Sport: Toward EEG Biomarkers of Performance.

Brain dynamics is at the basis of top performance accomplishment in sports. The search for neural biomarkers of performance remains a challenge in movement science and sport psychology. The non-invasive nature of high-density electroencephalography (EEG) recording has made it a most promising avenue for providing quantitative feedback to practitioners and coaches. Here, we review the current relevance of the main types of EEG oscillations in order to trace a perspective for future practical applications of EEG and event-related potentials (ERP) in sport. In this context, the hypotheses of unified brain rhythms and continuity between wake and sleep states should provide a functional template for EEG biomarkers in sport. The oscillations in the thalamo-cortical and hippocampal circuitry including the physiology of the place cells and the grid cells provide a frame of reference for the analysis of delta, theta, beta, alpha (incl.mu), and gamma oscillations recorded in the space field of human performance. Based on recent neuronal models facilitating the distinction between the different dynamic regimes (selective gating and binding) in these different oscillations we suggest an integrated approach articulating together the classical biomechanical factors (3D movements and EMG) and the high-density EEG and ERP signals to allow finer mathematical analysis to optimize sport performance, such as microstates, coherency/directionality analysis and neural generators.

ID 300 – Cortical excitability and connectivity in early RRMS using TMS-EEG

Objective Multiple sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system frequently affecting the corpus callosum, the largest interhemispheric fiber bundle, at an early stage of the disease. Here, we employed transcranial magnetic stimulation (TMS) combined with high-density 64-channel electroencephalography (EEG) to investigate cortical excitability and interhemispheric connectivity in 13 patients with early relapsing-remitting MS (RRMS) with mean EDSS 1 ± 1 compared to 16 age- and gender-matched healthy controls. Methods TMS was applied to the left and right M1 separately. Latencies and amplitudes of TMS-evoked EEG potential (TEP) components N45, N100, P180, N280 in the stimulated and non-stimulated hemisphere were analyzed using two different approaches: a cluster-based permutation analysis and an automated extraction of maximum and minimum values. Results Compared to healthy control subjects, RRMS patients did not show any significant differences in TEP latencies or amplitudes in the stimulated or non-stimulated hemisphere, neither for left nor for right M1 stimulation (all p > 0.05). Conclusion and key message We conclude that basic excitability and connectivity measures of TMS-EEG data are unaltered in early RRMS patients, which may indicate a high level of functional compensation for structural deficits in these patients, e.g. by activation of alternate routes of TEP propagation.

Pharmaco-TMS–EEG for testing brain excitability and connectivity

Single-pulse transcranial magnetic stimulation (TMS) of motor cortex elicits as sequence of brain responses in the electroencephalogram (EEG). This presentation will summarize recent pharmacological evidence that inhibitory neurotransmission through GABA-A receptors (GABAAR) is involved in early TMS–evoked EEG potentials (TEPs

Analysis and Enhancements of a Prolific Macroscopic Model of Epilepsy.

Macroscopic models of epilepsy can deliver surprisingly realistic EEG simulations. In the present study, a prolific series of models is evaluated with regard to theoretical and computational concerns, and enhancements are developed. Specifically, we analyze three aspects of the models: (1) Using dynamical systems analysis, we demonstrate and explain the presence of direct current potentials in the simulated EEG that were previously undocumented. (2) We explain how the system was not ideally formulated for numerical integration of stochastic differential equations. A reformulated system is developed to support proper methodology. (3) We explain an unreported contradiction in the published model specification regarding the use of a mathematical reduction method. We then use the method to reduce the number of equations and further improve the computational efficiency. The intent of our critique is to enhance the evolution of macroscopic modeling of epilepsy and assist others who wish to explore this exciting class of models further.

EEG AND EVENT-RELATED POTENTIAL (P300) IN ELDERLY PATIENTS WITH APHASIA AND EPILEPSY AFTER STROKE

Objective. We assessed the changes in EEG and event-related potential (P300) in elderly patients with poststroke aphasia and epilepsy. Materials and Methods. We examined 57 patients with poststroke aphasia (age 55-79 years). Cortical aphasia was revealed in 47 cases, and 10 patients had subcortical aphasia. Control group consisted patients with chronic brain chronic brain ischemia and healthy persons. Results. Local slow activity in the left hemisphere in EEG were revealed in 78% of patients with cortical aphasia, and in 20% of cases with subcortical aphasia. Local epileptic activity along with slow activity was spike-and-slow-wave complexes and sharp waves; temporal intermittent delta activity (TIRDA); both spike-and-wave complexes and TIRDA. In two cases epileptic activity was revealed as periodic lateralizing epileptiform discharges (PLEDs) besides local slow waves, and may deteriorate the aphasia. According to P300 data patients with poststroke aphasia hadn’t severe cognitive impairment, and the worst changes were noted in the group with chronic brain ischemia. In some patients with heavy aphasia it was possible to recorded P300 in passive perception of stimuli. Conclusion. In cases with PLEDs and persistent epileptic activity antiepileptic treatment probably reduces the aphasia. P300 is important in diagnostic of cognitive decline in patients with poststroke aphasia, especially when aphasia is heavy and neuropsychological tests can’t be done.

3D hand motion trajectory prediction from EEG mu and beta bandpower.

A motion trajectory prediction (MTP) - based brain-computer interface (BCI) aims to reconstruct the three-dimensional (3D) trajectory of upper limb movement using electroencephalography (EEG). The most common MTP BCI employs a time series of bandpass-filtered EEG potentials (referred to here as the potential time-series, PTS, model) for reconstructing the trajectory of a 3D limb movement using multiple linear regression. These studies report the best accuracy when a 0.5-2Hz bandpass filter is applied to the EEG. In the present study, we show that spatiotemporal power distribution of theta (4-8Hz), mu (8-12Hz), and beta (12-28Hz) bands are more robust for movement trajectory decoding when the standard PTS approach is replaced with time-varying bandpower values of a specified EEG band, ie, with a bandpower time-series (BTS) model. A comprehensive analysis comprising of three subjects performing pointing movements with the dominant right arm toward six targets is presented. Our results show that the BTS model produces significantly higher MTP accuracy (R~0.45) compared to the standard PTS model (R~0.2). In the case of the BTS model, the highest accuracy was achieved across the three subjects typically in the mu (8-12Hz) and low-beta (12-18Hz) bands. Additionally, we highlight a limitation of the commonly used PTS model and illustrate how this model may be suboptimal for decoding motion trajectory relevant information. Although our results, showing that the mu and beta bands are prominent for MTP, are not in line with other MTP studies, they are consistent with the extensive literature on classical multiclass sensorimotor rhythm-based BCI studies (classification of limbs as opposed to motion trajectory prediction), which report the best accuracy of imagined limb movement classification using power values of mu and beta frequency bands. The methods proposed here provide a positive step toward noninvasive decoding of imagined 3D hand movements for movement-free BCIs.

Neuromarkers of anxiety and depression in a patient after neuro-ophthalmic surgery of the meningioma – effect of individually-tailored tDCS and neurofeedback

The aim of the study was to evaluate the effectiveness of individually tailored anodal tDCs/ neurofeedback protocol for the reduction of post-operative depression after a neuroophtalmological operation of the meningioma. The neuromarkers in Quantitative EEG (QEEG) and Event-related potentials (ERPs) were utilized in the construction of protocol and evaluation. A 45-year-old female after successful neuro-ophthalmic surgery of the meningioma, complained of severe pain and anxiety, difficulties with sleeping, attention and memory problems, as well as inability to continue working in her given profession. Neuropsychological testing showed lack of cognitive disturbances and post-operative depression. Two working hypotheses were tested to find neuromarkers of depression and anxiety. In line with the 'depression hypothesis' a frontal alpha asymmetry pattern was found in the patient, and in line with the 'anxiety' hypothesis an increased left temporal P1 wave in response to visual stimuli was found in ERPs. A specific alpha asymmetry neurofeedback protocol combined with an anodal tDCS was suggested. Twenty sessions of individually-tailored anodal tDCs/ neurofeedback protocol were performed. The QEEG frontal asymmetry pattern and the excessive temporal P1 wave were normalized after the intervention. The patient recovered from post-operative depression and returned to work after 20 sessions of the combined neurofeedback/tDCS protocol. Specific patterns of QEEG and ERPs serve as neuromarkers for constructing the protocol and for monitoring the results of intervention.

Electrophysiological Indices in Sportsmen: Correlations with the Contents of Iron and Copper in the Organism

Using the roentgen-fluorescent analysis, we found mostly below-norm contents of iron and copper in stable tissue samples (hairs) of 18- to 20-year-old sportsmen (university students). Correlations of the results of such analysis with sets of indices characterizing the state of the CNS (parameters of background EEG and of evoked/event-related EEG potentials), that of the cardiovascular system (CVS, data of electrocardiography and rheography), and of the mechanisms providing autonomic regulation of the latter system (indices of cardiointervalography) were calculated. According to the number of significant correlations (0.44 < r < 0.56) and their intensity, we found that the content of copper is more significant for the indices characterizing functioning of the CNS, especially for those of EEG phenomena related to mental processes. At the same time, the content of iron exerts more significant influences on the functional state of the CVS. It should be taken into account that the pattern of correlations found in our study is typical of a relatively specific sampling of the subjects (with a clearly pronounced deficiency of the above-mentioned elements in the organism) and can be dissimilar in the case of normal (and, especially, increased) contents of these metals.

Reconstructing Large-Scale Brain Resting-State Networks from High-Resolution EEG: Spatial and Temporal Comparisons with fMRI.

Functional magnetic resonance imaging (fMRI) studies utilizing measures of hemodynamic signal, such as the blood oxygenation level-dependent (BOLD) signal, have discovered that resting-state brain activities are organized into multiple large-scale functional networks, coined as resting-state networks (RSNs). However, an important limitation of the available fMRI studies is that hemodynamic signals only provide an indirect measure of the neuronal activity. In contrast, electroencephalography (EEG) directly measures electrophysiological activity of the brain. However, little is known about the brain-wide organization of such spontaneous neuronal population signals at the resting state. It is not entirely clear if or how the network structure built upon slowly fluctuating hemodynamic signals is represented in terms of fast, dynamic, and spontaneous neuronal activity. In this study, we investigated the electrophysiological representation of RSNs from simultaneously acquired EEG and fMRI data in the resting human brain. We developed a data-driven analysis approach that reconstructed multiple large-scale electrophysiological networks from high-resolution EEG data alone. The networks derived from EEG were then compared with RSNs independently derived from simultaneously acquired fMRI in their spatial structures as well as temporal dynamics. Results reveal spatially and temporally specific electrophysiological correlates for the fMRI-RSNs. Findings suggest that the spontaneous activity of various large-scale cortical networks is reflected in macroscopic EEG potentials.

Choosing to Stop: Responses Evoked by Externally Triggered and Internally Generated Inhibition Identify a Neural Mechanism of Will.

Inhibiting inappropriate action is key to human behavioral control. Studies of action inhibition largely investigated external stop signals, yet these are rare in everyday life. Instead healthy adults exert "self-control," implying an ability to decide internally to stop actions. We added "choose for yourself" stimuli to a conventional go/no-go task to compare reactive versus intentional action and inhibition. No-go reactions showed the N2 EEG potential characteristic of inhibiting prepotent motor responses, whereas go reactions did not. Interestingly, the N2 component was present for intentional choices both to act and also to inhibit. Thus, free choices involved a first step of intentionally inhibiting prepotent responses before generating or withholding an action. Intentional inhibition has a crucial role breaking the flow of stimulus-driven responding, allowing expression of volitional decisions. Even decisions to initiate self-generated actions require this prior negative form of volition, ensuring the "freedom from immediacy" characteristic of human behavior.

Evaluation of the effectiveness of neurofeedback in the reduction of Posttraumatic stress disorder (PTSD) in a patient following high-voltage electric shock with the use of ERPs

The aim of our research was an evaluation of the effectiveness of neurofeedback in reducing the symptoms of Post-trauma stress disorder (PTSD), which had developed as a result of a high-voltage electric burn to the head. Quantitative EEG (QEEG) and Event related potentials (ERPs) were utilised in the evaluation. A 21-year-old patient, experienced 4(th) degree burns to his head as a result of a high-voltage electric burn. The patient was repeatedly operated on and despite the severity of the injuries was to recover. However the patient complained of flashbacks, difficulties with sleeping as well as an inability to continue work in his given profession. Specialist tests were to show the presence within him of PTSD. As a result of which the patient was provided with neurofeedback therapy. The effectiveness of this therapy in the reduction (eradication) of the symptoms of PTSD were evaluated through the utilisation of qantitative eeg (Qeeg) and event related potentials (ERPs). It was found that in the first examination that ERPs display the most significant deviations from the reference in the two components: (1) the one component is generated within the cingulate cortex. The pattern of its deviation from the norms is similar to that found in a group of OCD patients. In contrast to healthy subjects the component repeats itself twice; (2) the second component is generated in the medial prefrontal cortex. Its pattern (neuromarker) is similar to that found in PTSD patients. There is a delay in the late part of the component, which probably reflects the flashbacks. In the second examination, after neurofeedback training, the ERPs were similar to the norm. The patient returned to work. Chronic PTSD developed within the patient as a result of a high-voltage electric burn. The application of a method of therapy (neurofeedback) resulted in the withdrawal of the syndrome symptoms. ERPs in a GO/NOGO task can be used to plan neurofeedback and in the assessment of functional brain changes induced by neurotherapeutic programmes. Funds Collection: Private sources.

Time course of early visual cortex dynamics during top-down modulated attention shifts within or between feature dimensions

Shifting attention from one color to another color or from color to another feature dimension such as orientation is imperative when searching for a certain object in a cluttered scene. Most attention models that emphasize feature-based selection implicitly assume that within- and cross-dimensional shifts take equally long. In contrast, the dimensional weighting account (DWA) predicts that cross-dimensional shifts take longer than shifts within a dimension because attentional weights need to be shifted not only between features but also from one dimension to another. We investigated whether shifting costs between feature dimensions is a general mechanism beyond singleton feature search in a non-search task. To this end, we recorded time courses of behavioral data and steady state visual evoked potentials (SSVEPs) in human EEG that provide an objective electrophysiological measure of neural dynamics in early visual cortex. SSVEPs were elicited by four random dot kinematograms (RDKs) that flickered at different frequencies. Each RDK comprised dashes that uniquely combined two features from dimensions color (red or blue) and orientation (slash or backslash). Subjects were cued to either shift attention within a feature dimension (e.g. from color to color) or cross-dimensionally (e.g. from color to orientation) and to detect and respond to short coherent motion events of a subset of dots at the to-be-attended dimension while ignoring all other events. For behavioral as well as electrophysiological data we found that shifts between feature dimensions took longer than shifts within dimensions. Interestingly, shifts towards color were took less time than shifts towards orientation, regardless of the initially to-be-attended feature. In conclusion: While the first result is in line with the DWA the second might reflect – in line with previous studies – that color and orientation are preferentially processed at different stages of the visual cortical hierarchy that receive attentional top-down modulations with different latencies. Meeting abstract presented at VSS 2015

Precursors to morality in development as a complex interplay between neural, socioenvironmental, and behavioral facets

The nature and underpinnings of infants' seemingly complex, third-party, social evaluations remain highly contentious. Theoretical perspectives oscillate between rich and lean interpretations of the same expressed preferences. Although some argue that infants and toddlers possess a "moral sense" based on core knowledge of the social world, others suggest that social evaluations are hierarchical in nature and the product of an integration of rudimentary general processes such as attention allocation and approach and avoidance. Moreover, these biologically prepared minds interact in social environments that include significant variation, which are likely to impact early social evaluations and behavior. The present study examined the neural underpinnings of and precursors to moral sensitivity in infants and toddlers (n = 73, ages 12-24 mo) through a series of interwoven measures, combining multiple levels of analysis including electrophysiological, eye-tracking, behavioral, and socioenvironmental. Continuous EEG and time-locked event-related potentials (ERPs) and gaze fixation were recorded while children watched characters engaging in prosocial and antisocial actions in two different tasks. All children demonstrated a neural differentiation in both spectral EEG power density modulations and time-locked ERPs when perceiving prosocial or antisocial agents. Time-locked neural differences predicted children's preference for prosocial characters and were influenced by parental values regarding justice and fairness. Overall, this investigation casts light on the fundamental nature of moral cognition, including its underpinnings in general processes such as attention and approach-withdrawal, providing plausible mechanisms of early change and a foundation for forward movement in the field of developmental social neuroscience.

E3D hand movement velocity reconstruction using power spectral density of EEG signals and neural network.

Three dimensional (3D) limb motion trajectory is predictable with a non-invasive brain-computer interface (BCI). To date, most non-invasive motion trajectory prediction BCIs use potential values of electroencephalographic (EEG) signals as the input to a multiple linear regression (mLR) based kinetic data estimator. We investigated the possible improvement in accuracy of 3D hand movement prediction (i.e., the correlation of registered and reconstructed hand velocities) by replacing raw EEG potentials with spectrum power values of specific EEG bands. We also investigated if a non-linear neural network based estimator outperformed the mLR approach. The spectrum power model provided significantly higher accuracy (R~0.60) compared to the similar EEG potentials based approach (R~0.45). Additionally, when replacing the mLR based kinetic data estimation module with a feed-forward neural network (NN) we found the NN based spectrum power model provided higher accuracy (R~0.70) compared to the similar mLR based approach (R~0.60).

A new algorithm for neural connectivity estimation of EEG event related potentials.

We propose a new algorithm for estimating neural connectivity during event related potentials (ERP) in EEG. It is composed of two steps: the estimation of a time-varying multivariate-autoregressive (MVAR) model and the calculation of the generalized partial directed coherence (gPDC) for assessing the connectivities between channels where MVAR estimation is done via an adapted version of the Nuttall-Strand algorithm, a multivariate generalization of Burg's spectral estimation algorithm. Successful algorithm validation was performed through simulations using toys model with physiologically ERP inspired features.