Electrocorticogram Signals Research Articles

On the decoding of intracranial data using sparse orthonormalized partial least squares

It has recently been shown that robust decoding of motor output from electrocorticogram signals in monkeys over prolonged periods of time has become feasible (Chao et al 2010 Front. Neuroeng. 3 1–10 ). In order to achieve these results, multivariate partial least-squares (PLS) regression was used. PLS uses a set of latent variables, referred to as components, to model the relationship between the input and the output data and is known to handle high-dimensional and possibly strongly correlated inputs and outputs well. We developed a new decoding method called sparse orthonormalized partial least squares (SOPLS) which was tested on a subset of the data used in Chao et al (2010) (freely obtainable from neurotycho.org (Nagasaka et al 2011 PLoS ONE 6 e22561)). We show that SOPLS reaches the same decoding performance as PLS using just two sparse components which can each be interpreted as encoding particular combinations of motor parameters. Furthermore, the sparse solution afforded by the SOPLS model allowed us to show the functional involvement of beta and gamma band responses in premotor and motor cortex for predicting the first component. Based on the literature, we conjecture that this first component is involved in the encoding of movement direction. Hence, the sparse and compact representation afforded by the SOPLS model facilitates interpretation of which spectral, spatial and temporal components are involved in successful decoding. These advantages make the proposed decoding method an important new tool in neuroprosthetics.

Sound-evoked network calcium transients in mouse auditory cortexin vivo

Population calcium signals generated by the action potential activity of local clusters of neurons have been recorded in the auditory cortex of mice using an optical fibre-based approach. These network calcium transients (NCaTs) occurred spontaneously as well as in response to sound stimulation. Two-photon calcium imaging experiments suggest that neurons and neuropil contribute about equally to the NCaT. Sound-evoked calcium signals had two components: an early, fast increase in calcium concentration, which corresponds to the short-latency spiking responses observed in electrophysiological experiments, and a late, slow calcium transient which lasted for at least 1 s. The slow calcium transients evoked by sound were essentially identical to spontaneous NCaTs. Their sizes were dependent on the spontaneous activity level at sound onset, suggesting that spontaneous and sensory-evoked NCaTs excluded each other. When using pure tones as stimulus, the early evoked calcium transients were more narrowly tuned than the slow NCaTs. The slow NCaTs were correlated with global ‘up states' recorded with epidural potentials, and sound presented during an epidural ‘down state' triggered a calcium transient that was associated with an epidural ‘up state'. Essentially indistinguishable calcium transients were evoked by optogenetic activation of local clusters of layer 5 pyramidal neurons in the auditory cortex, indicating that these neurons play an important role in the generation of the calcium signal. Taken together, our results identify sound-evoked slow NCaTs as an integral component of neuronal signalling in the mouse auditory cortex, reflecting the prolonged neuronal activity of local clusters of neurons that can be activated even by brief stimuli.

Decoding Finger Flexion from Band-Specific ECoG Signals in Humans

This article presents the method that won the brain-computer interface (BCI) competition IV addressed to the prediction of the finger flexion from electrocorticogram (ECoG) signals. ECoG-based BCIs have recently drawn the attention from the community. Indeed, ECoG can provide higher spatial resolution and better signal quality than classical EEG recordings. It is also more suitable for long-term use. These characteristics allow to decode precise brain activities and to realize efficient ECoG-based neuroprostheses. Signal processing is a very important task in BCIs research for translating brain signals into commands. Here, we present a linear regression method based on the amplitude modulation of band-specific ECoG including a short-term memory for individual finger flexion prediction. The effectiveness of the method was proven by achieving the highest value of correlation coefficient between the predicted and recorded finger flexion values on data set 4 during the BCI competition IV.

Face value: emotion categorization augments gamma oscillations in the prefrontal and inferior parietal cortex

Event Abstract Back to Event Face value: emotion categorization augments gamma oscillations in the prefrontal and inferior parietal cortex Avgusta Shestyuk1*, Heidi Kirsch2, Edward Chang2, Nicholas M. Barbaro2, Nathan E. Crone3 and Robert T. Knight1 1 Helen Wills Neuroscience Institute, University of California Berkeley, United States 2 University of California, San Francisco, United States 3 Johns Hopkins University, United States Activity in the fronto-pariatal cortical regions in response to facial stimuli has been observed in several studies. However, it remains unclear whether this activation is automatic or can be modulated by depth of processing and emotional properties of facial expressions. To examine this question, intracranial electrocorticogram (ECoG) signals were recorded in 4 subjects with intractable epilepsy. Participants completed two tasks with the same stimulus set: an explicit emotional categorization (i.e, discriminating between angry and neutral facial expressions), and a perceptual gender categorization task (i.e., discriminating between male and female faces). The task order was counterbalanced, and each stimulus was presented for 300 ms. Increases in spectral power in the gamma range (70-150 Hz) were used to index functional activation of the underlying cortex. Subjects found the perceptual task easier and more automatic than the emotional categorization task, as evidenced by shorter reaction times and fewer errors. There were no differences in reaction times between angry and neutral faces. Electrodes positioned over the left inferior-middle frontal and inferior parietal cortices recorded an increase in gamma power during the explicit emotional categorization relative to perceptual categorization (p < .01). Angry faces categorized based on emotion elicited greater gamma responses than neutral faces (p < .01). When present, increased gamma oscillations in the prefrontal cortex were time-locked (~250 ms) to stimulus presentation and lasted until subjects made the response. These results highlight the role of the fronto-temporal brain areas (also implicated in attention, working memory, and mimicking) in processing of emotional facial information and response selection. Funding: Supported by NIH Grants: MH075317, NS21135, NS065120. Keywords: gamma band, Intra-cranial Electrophysiology Conference: XI International Conference on Cognitive Neuroscience (ICON XI), Palma, Mallorca, Spain, 25 Sep - 29 Sep, 2011. Presentation Type: Poster Presentation Topic: Abstracts Citation: Shestyuk A, Kirsch H, Chang E, Barbaro NM, Crone NE and Knight RT (2011). Face value: emotion categorization augments gamma oscillations in the prefrontal and inferior parietal cortex. Conference Abstract: XI International Conference on Cognitive Neuroscience (ICON XI). doi: 10.3389/conf.fnhum.2011.207.00066 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 15 Nov 2011; Published Online: 25 Nov 2011. * Correspondence: Dr. Avgusta Shestyuk, Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, United States, ashestyuk@berkeley.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Avgusta Shestyuk Heidi Kirsch Edward Chang Nicholas M Barbaro Nathan E Crone Robert T Knight Google Avgusta Shestyuk Heidi Kirsch Edward Chang Nicholas M Barbaro Nathan E Crone Robert T Knight Google Scholar Avgusta Shestyuk Heidi Kirsch Edward Chang Nicholas M Barbaro Nathan E Crone Robert T Knight PubMed Avgusta Shestyuk Heidi Kirsch Edward Chang Nicholas M Barbaro Nathan E Crone Robert T Knight Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Model Generalization in Prediction of Continuous Arm Movements from the ECoG

Model Generalization in Prediction of Continuous Arm Movements from the ECoG

Simultaneous recording of ECoG and intracortical neuronal activity using a flexible multichannel electrode-mesh in visual cortex

Electrocorticogram (ECoG) is a well-balanced methodology for stably mapping brain surface local field potentials (LFPs) over a wide cortical region with high signal fidelity and minimal invasiveness to the brain tissue. To directly compare surface ECoG signals with intracortical neuronal activity immediately underneath, we fabricated a flexible multichannel electrode array with mesh-form structure using micro-electro-mechanical systems. A Parylene-C-based “electrode-mesh” for rats contained a 6×6 gold electrode array with 1-mm interval. Specifically, the probe had 800×800μm2 fenestrae in interelectrode spaces, through which simultaneous penetration of microelectrode was capable. This electrode-mesh was placed acutely or chronically on the dural/pial surface of the visual cortex of Long–Evans rats for up to 2weeks. We obtained reliable trial-wise profiles of visually evoked ECoG signals through individual eye stimulation. Visually evoked ECoG signals from the electrode-mesh exhibited as well or larger signal amplitudes as intracortical LFPs and less across-trial variability than conventional silver-ball ECoG. Ocular selectivity of ECoG responses was correlated with that of intracortical spike/LFP activities. Moreover, single-trial ECoG signals carried sufficient information for predicting the stimulated eye with a correct performance approaching 90%, and the decoding was significantly generalized across sessions over 6hours. Electrode impedance or signal quality did not obviously deteriorate for 2weeks following implantation. These findings open up a methodology to directly explore ECoG signals with reference to intracortical neuronal sources and would provide a key to developing minimally invasive next-generation brain–machine interfaces.

Analysis of signal fluctuations of Cortical Spreading Depression: Preliminary findings

In this work, we apply Detrended Fluctuation Analysis (DFA) to study the dynamics of electrical cortical activity in rats during the phenomenon of Cortical Spreading Depression (CSD), as well as the periods before and after this phenomenon. The characteristic of CSD is reduced electrical activity that occurs and spreads in the cerebral cortex after the application of electrical, chemical or mechanical stimulus. Our results show that the electrocorticogram signal shows long range temporal correlations and scaling behavior, except during the pre-CSD burst phase (significant increase of amplitude provoked by stimulus).

Regression of forearm reach and grasp movement trajectories using intracortical local field potentials and electrocorticogram signals in monkey primary motor cortex

Regression of forearm reach and grasp movement trajectories using intracortical local field potentials and electrocorticogram signals in monkey primary motor cortex

EEG Generator—A Model of Potentials in a Volume Conductor

EEG generator-a model of potentials in a volume conductor. The potential recorded over the cortex electro-corticogram (ECoG) or over the scalp [electroencephalograph (EEG)] derives from the activity of many sources known as "EEG generators." The recorded amplitude is basically a function of the unitary potential of a generator and the statistical relationship between different EEG generators in the recorded population. In this study, we first suggest a new definition of the EEG generator. We use the theory of potentials in a volume conductor and model the contribution of a single synapse activated to the surface potential. We then model the contribution of the generator to the surface potential. Once the generator and its contribution are well defined, we can quantitatively assess the degree of synchronization among generators. The measures obtained by the model for a real life scenario of a group of generators organized in a specific statistical way were consistent with the expected values that were reported experimentally. The study sheds new light on macroscopic modeling approaches which make use of mean soma membrane potential. We showed major contribution of activity of superficial apical synapses to the ECoG signal recorded relative to lower somatic or basal synapses activity.

The Electrocorticogram Signal Can Be Modulated With Deep Brain Stimulation of the Subthalamic Nucleus in the Hemiparkinsonian Rat

Electrocorticogram (ECoG) recordings of the 6-hydroxydopamine (6-OHDA)-lesioned parkinsonian rat have shown an increase in the power of cortical beta-band (15-30 Hz) oscillations ipsilateral to the lesion. The power of these oscillations is decreased with dopamine agonist administration. Here, we demonstrate that stimulation of an electrode implanted in the subthalamic nucleus alters the power of cortical beta and gamma oscillations in 6-OHDA-lesioned animals. These alterations are dependent on stimulation frequency, charge, and amplitude/pulse width. Oscillations were significantly reduced during 200- and 350-Hz stimulation. A minimum charge of 4 nC was required to elicit a reduction in oscillation power. A number of amplitude and pulse width combinations that reached 4 nC were tested; it was found that only the combinations of 33 microA/120 micros and 65 microA/60 micros significantly reduced cortical oscillations. The reduction in beta/gamma oscillation power due to deep brain stimulation (DBS) was consistent with a significant reduction in the animals' rotational behavior, a typical symptom of parkinsonism in the rat. A significant shift from high beta to low gamma was observed in the peak frequencies of ECoG recordings while animals were at rest versus walking on a treadmill. However, DBS exhibited no differential effect on oscillations between these two states. EEG recordings from rodent models of DBS may provide surrogate information about the neural signatures of Parkinson's disease relative to the efficacy of DBS.

Chronic but not Acute Dopaminergic Transmission Interruption Promotes a Progressive Increase in Cortical Beta Frequency Synchronization: Relationships to Vigilance State and Akinesia

Dopaminergic (DA) denervation results in the appearance of an excessive cortical beta frequency synchronization in parkinsonian patients and animal models of the disease. The present study analyzed electrocorticogram signals in awake rats to further characterize this excessive synchronization in terms of time course, relation to motor activity and state of vigilance. Using substantia nigra pars compacta lesions and both acute and chronic pharmacological interruptions of DA transmission, the present data demonstrated that the appearance of excessive beta synchronization requires a prolonged interruption in DA transmission and builds up progressively. This synchronization was vigilance-state dependent and observed solely during awake-like activity. Furthermore, these data demonstrated for the first time that the appearance of akinesia preceded the excessive cortical beta synchronization. In addition, this synchronization was stronger in the motor than in the somato-sensory cortex and in unilaterally compared with bilaterally lesioned animals. Finally, excessive beta synchronization was accompanied by an increased coherence between motor and somato-sensory cortical activities. These data suggest that excessive beta synchronization is associated with plastic processes whose time course is delayed with respect to the akinesia. Moreover, the expression of this phenomenon, which likely reflects functional changes in the cortico-basal ganglia circuits, requires a specific brain state.

Voluntary brain regulation and communication with electrocorticogram signals

Brain–computer interfaces (BCIs) can be used for communication in writing without muscular activity or for learning to control seizures by voluntary regulation of brain signals such as the electroencephalogram (EEG). Three of five patients with epilepsy were able to spell their names with electrocorticogram (ECoG) signals derived from motor-related areas within only one or two training sessions. Imagery of finger or tongue movements was classified with support-vector classification of autoregressive coefficients derived from the ECoG signals. After training of the classifier, binary classification responses were used to select letters from a computer-generated menu. Offline analysis showed increased theta activity in the unsuccessful patients, whereas the successful patients exhibited dominant sensorimotor rhythms that they could control. The high spatial resolution and increased signal-to-noise ratio in ECoG signals, combined with short training periods, may offer an alternative for communication in complete paralysis, locked-in syndrome, and motor restoration.

Three-dimensional brain current source reconstruction from intra-cranial ECoG recordings

We have investigated 3-dimensional brain current density reconstruction (CDR) from intracranial electrocorticogram (ECoG) recordings by means of finite element method (FEM). The brain electrical sources are modeled by a current density distribution and estimated from the ECoG signals with the aid of a weighted minimum norm estimation algorithm. A series of computer simulations were conducted to evaluate the performance of ECoG-CDR by comparing with the scalp EEG based CDR results. The present computer simulation results indicate that the ECoG-CDR provides enhanced performance in localizing single dipole sources which are located in regions underneath the implanted subdural ECoG grids, and in distinguishing and imaging multiple separate dipole sources, in comparison to the CDR results as obtained from the scalp EEG under the same conditions. We have also demonstrated the applicability of the present ECoG-CDR method to estimate 3-dimensional current density distribution from the subdural ECoG recordings in a human epilepsy patient. Eleven interictal epileptiform spikes (seven from the frontal region and four from parietal region) in an epilepsy patient undergoing surgical evaluation were analyzed. The present promising results indicate the feasibility and applicability of the developed ECoG-CDR method of estimating brain sources from intracranial electrical recordings, with detailed forward modeling using FEM.

Statistical Modeling and Analysis of Laser-Evoked Potentials of Electrocorticogram Recordings from Awake Humans

This article is devoted to statistical modeling and analysis of electrocorticogram (ECoG) signals induced by painful cutaneous laser stimuli, which were recorded from implanted electrodes in awake humans. Specifically, with statistical tools of factor analysis and independent component analysis, the pain-induced laser-evoked potentials (LEPs) were extracted and investigated under different controlled conditions. With the help of wavelet analysis, quantitative and qualitative analyses were conducted regarding the LEPs' attributes of power, amplitude, and latency, in both averaging and single-trial experiments. Statistical hypothesis tests were also applied in various experimental setups. Experimental results reported herein also confirm previous findings in the neurophysiology literature. In addition, single-trial analysis has also revealed many new observations that might be interesting to the neuroscientists or clinical neurophysiologists. These promising results show convincing validation that advanced signal processing and statistical analysis may open new avenues for future studies of such ECoG or other relevant biomedical recordings.

Classifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects

We summarize results from a series of related studies that aim to develop a motor-imagery-based brain-computer interface using a single recording session of electroencephalogram (EEG) or electrocorticogram (ECoG) signals for each subject. We apply the same experimental and analytical methods to 11 nonparalysed subjects (eight EEG, three ECoG), and to five paralyzed subjects (four EEG, one ECoG) who had been unable to communicate for some time. While it was relatively easy to obtain classifiable signals quickly from most of the nonparalyzed subjects, it proved impossible to classify the signals obtained from the paralyzed patients by the same methods. This highlights the fact that though certain BCI paradigms may work well with healthy subjects, this does not necessarily indicate success with the target user group. We outline possible reasons for this failure to transfer.

Controlled test for predictive power of Lyapunov exponents: their inability to predict epileptic seizures.

Lyapunov exponents are a set of fundamental dynamical invariants characterizing a system's sensitive dependence on initial conditions. For more than a decade, it has been claimed that the exponents computed from electroencephalogram (EEG) or electrocorticogram (ECoG) signals can be used for prediction of epileptic seizures minutes or even tens of minutes in advance. The purpose of this paper is to examine the predictive power of Lyapunov exponents. Three approaches are employed. (1) We present qualitative arguments suggesting that the Lyapunov exponents generally are not useful for seizure prediction. (2) We construct a two-dimensional, nonstationary chaotic map with a parameter slowly varying in a range containing a crisis, and test whether this critical event can be predicted by monitoring the evolution of finite-time Lyapunov exponents. This can thus be regarded as a "control test" for the claimed predictive power of the exponents for seizure. We find that two major obstacles arise in this application: statistical fluctuations of the Lyapunov exponents due to finite time computation and noise from the time series. We show that increasing the amount of data in a moving window will not improve the exponents' detective power for characteristic system changes, and that the presence of small noise can ruin completely the predictive power of the exponents. (3) We report negative results obtained from ECoG signals recorded from patients with epilepsy. All these indicate firmly that, the use of Lyapunov exponents for seizure prediction is practically impossible as the brain dynamical system generating the ECoG signals is more complicated than low-dimensional chaotic systems, and is noisy.

Electrocorticogram spectral analysis and somatosensory evoked potentials as tools to assess electrical stunning efficiency in ducks

1. Fast Fourier transformations (FFTs) of electrocorticogram (ECoG) signals and averaging of somatosensory evoked potentials (SEPs) were used for assessing the impact of electrical stunning of ducks in a waterbath set to deliver a constant current of 150 mA, 600 Hz alternating current (AC) for 4 s. The effectiveness of stunning was determined on the basis of induction of epileptiform activity in the ECoG followed by a decrease in total power content to less than 10% of pre-stun values and abolition of SEPs. 2. One out of 10 birds was killed by the stun. FFT analysis of the ECoG signals of the remaining 9 birds showed that only one bird had a decrease of the total power to less than 10% of the pre-stun values for up to 70 s post-stun. The SEPs were retained in 6 out of 9 ducks and and 4 of them retained the evoked responses throughout the post-stun period. In the two birds showing abolition of SEPs, this was associated with a decrease in the total power content to below 10% of the pre-stun value. 3. The present experiment confirmed that the abolition of SEPs and the decrease of the total power below 10% of the pre-stun value for assessing unconsciousness after an electrical stunning in various species are also applicable to ducks. Based on this, it is concluded that electrical waterbath stunning of ducks using 150 mA of 600 Hz AC is ineffective.

Inability of Lyapunov exponents to predict epileptic seizures.

It has been claimed that Lyapunov exponents computed from electroencephalogram or electrocorticogram (ECoG) time series are useful for early prediction of epileptic seizures. We show, by utilizing a paradigmatic chaotic system, that there are two major obstacles that can fundamentally hinder the predictive power of Lyapunov exponents computed from time series: finite-time statistical fluctuations and noise. A case study with an ECoG signal recorded from a patient with epilepsy is presented.

Developmental changes in the complexity of the electrocortical activity in foetal sheep

The foetal sheep brain develops organised sleep states from 115–120 d gestational age (dGA, term 150 dGA) alternating between REM and NREM sleep. We aimed to investigate whether maturation of REM or NREM sleep generating structures leads to the development of distinct sleep states. The electrocorticogram (ECoG) was recorded from five unanaesthetised chronically instrumented foetal sheep in utero and was analysed every 5th day between 115–130 dGA by two different non-linear methods. We calculated a non-linear prediction error which quantifies the causality of the ECoG and applied bispectral analysis which quantifies non-linear interrelations of single frequency components within the ECoG signal. The prediction error during REM sleep was significantly higher than during NREM sleep at each investigated age ( P<0.0001) coincidental with poor organisation of the rhythmic pattern in the ECoG during REM sleep. At 115 dGA, organised sleep states defined behaviourally were not developed yet. The prediction error, however, showed already different states of electrocortical activity that were not detectable using power spectral analysis. The prediction error of the premature NREM sleep ECoG decreased significantly during emergence of organised sleep states between 115 and 120 dGA and continued to decrease after the emergence of distinct sleep states ( P<0.05). The prediction error of the premature REM sleep ECoG did not change until 120 dGA and began to increase at 125 dGA ( P<0.05). Using bispectral analysis, we showed couplings between delta waves (1.5–4 Hz) and frequencies in the range of spindle waves (4–8 and 8–12 Hz) during NREM sleep that became closer during development. The results show that maturation of ECoG synchronisation mediating structures is important for the development of organised sleep states. The further divergence of the prediction error of NREM and REM sleep after development of organised sleep states reveals continuous functional development. Thus, complementary application of non-linear ECoG analysis to power spectral analysis provide new insights in the collective behaviour of the neuronal network during the emergence of sleep states.

Electrocortical activity in fetal sheep in the last seven days of gestation.

1. Electrocorticogram (ECoG) and myometrial electromyogram (EMG) were recorded continuously in chronically instrumented late gestation sheep fetuses (n = 9) to analyse: (1) 24 h ECoG patterns, (2) relationships between ECoG and myometrial contractility, and (3) 24 h ECoG patterns at the spontaneous onset of labour. 2. Spontaneous onset of labour was determined from the myometrial EMG. ECoG signals were purified by a denoising procedure of wavelet decomposition. High-voltage slow ECoG activity (HV) and low-voltage fast ECoG activity (LV) were determined mathematically, and HV-LV cycle periodicity was calculated by periodogram analysis. 3. Twenty-four hour rhythms were present in fetal ECoG HV-LV cycles in the 3-5 days prior to spontaneous onset of labour (P < 0.01). Cycle frequency was lower at 08.00-12.00 h and higher at 16.00-20.00 h (lights on at 07.00 h, lights off at 21.00 h). LV duration was longer at 20.00-24.00 h, and HV was shorter at 16.00-20.00 h. No relationship was found between ECoG cycles and myometrial contracture cycles. Twenty-four hour ECoG rhythms disappeared 1 day before the spontaneous onset of labour. 4. ECoG patterns changed 7 to 4 h prior to spontaneous onset of labour, percentage of time spent and duration of HV ECoG increased, and percentage of time spent in LV decreased significantly. ECoG HV-LV cyclicity was reduced 4-5 h prior to spontaneous onset of labour, indicating that the altered fetal hormonal and blood gas environment around the spontaneous onset of labour alter fetal neural function.