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Cortical mapping is widely employed to define the sensorimotor area and delineate the central sulcus (CS) during awake craniotomies. The approach involves the gold standard somatosensory evoked potentials (SSEPs) recorded with electrocorticogram (ECoG) strip electrodes. However, the evoked response can be misconstrued from the manual peak interpretation due to the poor spatial resolution of the strip electrode or when the electrode does not precisely cover the desired cortical area. This can lead to unintentional damage to the eloquent cortex. We present a soft real-time computer based visualization system that uses recorded SSEPs with a subdural grid to aid in cortical mapping. The neural data during electrical stimulation of the median nerve at 0.6Hz are picked up with a bio-amplifier at 2.4kHz. The stimulation artifact recorded from the bipolar electromyogram (EMG) is used as the stimulation onset. The ECoG data are assessed online with MATLAB Simulink to process and visualize the SSEPs waveform. The visualization system is programmed to display the SSEPs peak activation as a heat map on a 2D grid and projected onto a screen, showcasing the nature of the cortical activities over the contact surface area. Since the grid occupies a large cortical surface, the heatmap is able to delineate the central sulcus. The map can be viewed at any time point along the SSEP trace without the need for peak interpretation. With the goal to provide additional information during cortical mapping and facilitate interpretation of ECoG grid data, we believe that this visualization system will aid in rapid definition of the sensorimotor area during surgical planning. Clinical Relevance- This real-time visualization system can be used to delineate the central sulcus in a short time during awake craniotomies.

Seizure termination has received significantly less attention than initiation and propagation and consequently, remains a poorly understood phase of seizure evolution. Yet, its study may have a significant impact on the development of efficient interventional approaches, i.e., it may be critical for the design of treatments that induce or reproduce termination mechanisms that are triggered in self-terminating seizures. In this work, we aim to study temporal and spectral features of intracranial EEG (iEEG) during epileptic seizures to find time-frequency signatures that can predict the termination patterns. We propose a deep learning model for classification of multi channel iEEG epileptic seizure termination pattern into burst suppression and continuous bursting. We decompose the raw time series seizure data into time-frequency maps using Morlet Wavelet Transform. A Convolution Neural Network (CNN) is then trained on cross-patient time-frequency maps to classify the seizure termination patterns. For evaluation of classification performance, we compared the proposed method with k-Nearest Neighbour (k-NN). The CNN is shown to achieve an accuracy of 90 % and precision of 92 % as compared to 70% and 72% accuracy and precision achieved with the k-NN respectively. The proposed model is thus able to capture the temporal and spatial patterns which results in high performance of the classifier. This method of classification can be used to predict how a particular seizure will end and can potentially inform seizure management and treatment. Clinical relevance- This method establishes a model that can be used to classify seizure termination patterns with an accuracy of 90 % which can assist in better treatment of epilepsy patients.

Reliability, stability and biocompatibility of an im-plant are the keys to transferring a preclinical study into the re-ality of clinical applications for diagnostics and therapy of pa-tients. Amongst the smallest and most critical components of neuroprostheses are the neural interfaces to the tissue. These could be seen as the most functional and yet most sensitive parts to connect to and interact with the nervous system. Thin film systems in the submicro- to nanometers range with a high num-ber of channels record biological signals and excite nerves aspiring high spatial sensitivity at the scale of few neurons. The im-pairments of the technical material caused by the harsh environ-ment of the human body and potential damage to the tissue by the foreign body state the greatest obstacle to overcome. Here, we present an analysis on impact of acutely and chronically im-planted subdural electrocorticography (ECoG) recording arrays on the neural tissue and the accompanied material failure mechanisms of the thin film neural interfaces in vivo.

Transcorneal electrical stimulation (TES) is a noninvasive approach for activating the retina and its downstream components through the application of electric current on the cornea. Although previous studies have demonstrated the clinical relevance of TES for modulating neurons with improvements in visual evoked potentials (VEPs) and electroretinograms (ERGs), there are still huge gaps in knowledge of its effect on the brain structures. To determine the short-term impact as well as the aftereffects of TES on neural oscillatory power in retinal degeneration mice, we performed electrocorticography (ECoG) recording in the prefrontal and primary visual cortices at different stages of prolonged TES [transient stage, following prolonged stimulation (post-stimulation stage 1) and long after the end of the retinal stimulation (post-stimulation stage 2)]) under varying stimulation current amplitudes (400 µA, 500 µA and 600 µA). The results revealed asymmetric differences between short-term and long-term pTES under different stimulation current amplitudes. Specifically, in post-stimulation stage 1 we observed significant increase in ECoG power of theta, alpha and beta oscillations respectively compared with baseline pre-stimulation results. These effects were dependent on the stimulation current amplitude and stimulation stage. Transient TES was not sufficient to cause significant changes in the ECoG power of all accessed oscillations except in medium, high and ultra-gamma oscillations which significantly decreased in 400 µA and 500 µA stimulation groups respectively compared with pre-stimulation results. Regarding long-term stimulation, the increase in ECoG power of theta, alpha and beta oscillations observed in post-stimulation stage 1 was significantly maintained in post-stimulation stage 2.Clinical Relevance- These results could be of core importance for human TES protocols suggesting that following pTES and long after the end of the stimulation, TES current amplitudes could have relatively different impacts on the power/activity of cortical oscillations. For example, by increasing the activity of oscillations that have been reported to inhibit irrelevant neural processes and enable the brain to focus on more relevant neural processes thus, inducing better coordination in the cortex.

Increasingly, studies have shown that changes in brain network topology accompany loss of consciousness such that the functional connectivity of the prefrontal-parietal network differs significantly in anesthetized and awake states. In this work, anesthetized and awake segments of electrocorticography were selected from two monkeys. Using phase lag index, functional connectivity matrices were built in multiple frequency bands. Quantifying topological changes in brain network through graph-theoretic properties revealed significant differences between the awake and anesthetized states. Compared to the awake state, there were distinct increases in overall and Delta prefrontal-frontal connectivity, and decreases in Alpha, Beta1 and Beta2 prefrontal-frontal connectivity during the anesthetized state, which indicate a change in the topology of the small-world network. Using functional connectivity features we achieved a satisfactory classification accuracy (93.68%). Our study demonstrates that functional connectivity features are of sufficient power to distinguish awake versus anesthetized state.Clinical Relevance- This explores the brain network topology in awake and anesthetized states, and provides new ideas for clinical depth of anesthesia monitoring.

Interictal epileptiform discharges (IEDs) serve as sensitive but not specific biomarkers of epilepsy that can delineate the epileptogenic zone (EZ) in patients with drug resistant epilepsy (DRE) undergoing surgery. Intracranial EEG (icEEG) studies have shown that IEDs propagate in time across large areas of the brain. The onset of this propagation is regarded as a more specific biomarker of epilepsy than areas of spread. Yet, the limited spatial resolution of icEEG does not allow to identify the onset of this activity with high precision. Here, we propose a new method of mapping the spatiotemporal propagation of IEDs (and identify its onset) by using Electrical Source Imaging (ESI) on icEEG bypassing the spatial limitations of icEEG. We validated our method on icEEG recordings from 8 children with DRE who underwent surgery with good outcome (Engel score = 1). On each icEEG channel, we detected IEDs and identified the propagation onset using an automated algorithm. We localized the propagation of IEDs with dynamic Statistical Parametric Mapping (dSPM) using a time-sliding window approach. We defined two brain regions: the ESI-onset and ESI-spread zone. We estimated the overlap of these regions with resection volume (in percentage), which served as the gold-standard of the EZ. We also estimated the mean distance of these regions from resection and clinically defined seizure onset zone (SOZ). We observed spatiotemporal propagation of IEDs in all patients across several channels (98 [85-102]) with a mean duration of 155 ms [96-186 ms]. A higher overlap with resection was seen for the ESI-onset zone compared to spread (73.3 % [ 47.4-100 %], 36.5 % [20.3-59.9 %], p = 0.008). The distance of the ESI-onset from resection was shorter compared to the ESI-spread zone (4.3 mm [3.4-5.5 mm], 7.4 mm [6.0-20.6 mm], p = 0.008) and the same trend was observed for the distance from the SOZ (11.9 mm [7.2-15.1 mm], 20.6 mm [15.4-27.2 mm], p = 0.02). These findings show that our method can map the spatiotemporal propagation of IEDs and delineate its onset, which is a reliable and focal biomarker of the EZ in children with DRE. CLINICAL RELEVANCE —: ESI on icEEG recordings of children with DRE can localize the spikes propagation phenomenon and help in the delineation of the EZ.

Studies on intracranial electroencephalography (icEEG) recordings of patients with drug resistant epilepsy (DRE) show that epilepsy biomarkers propagate in time across brain areas. Here, we propose a novel method that estimates critical features of these propagations for different epilepsy biomarkers (spikes, ripples, and fast ripples), and assess their common onset as a reliable biomarker of the epileptogenic zone (EZ). For each biomarker, an automatic algorithm ranked the icEEG electrodes according to their timing occurrence in propagations and finally dichotomized them as onset or spread. We validated our algorithm on icEEG recordings of 8 children with DRE having a good surgical outcome (Engel score = 1). We estimated the overlap of the onset, spread, and entire zone of propagation with the resection (RZ) and the seizure onset zone (SOZ). Spike and ripple propagations were seen in all patients, whereas fast ripple propagations were seen in 6 patients. Spike, ripple, and fast ripple propagations had a mean duration of 28.3 ± 24.3 ms, 38.7 ± 37 ms, and 25 ± 14 ms respectively. Onset electrodes predicted the RZ and SOZ with higher specificity compared to the entire zone for all three biomarkers (p

This study aims to identify correlations between the directions of the hand movements and the brain signals recorded from the brain surface using electrocorticography (ECoG). We suppose that the coding of the hand movement direction occurs in the motor cortex and could be detected with ECoG. Using representational similarity analysis (RSA) and the cosine tuning assumption we confirmed that indeed, there is a significant concordance between the brain signals and the hand movement directions. And the main areas of the brain responsible for this have been identified.

We report a set of flexible microelectrode arrays (MEAs) with in-plane shielding designs that are capable of high quality electrocorticography (ECoG) signal acquisition. In this work, we have designed and fabricated two different shapes of shielding (i.e. hook-like and bell-type wires) and evaluated their shielding performances. With shielding, our flexible MEAs have better resistance to external disturbances while maintaining good conformability with high-quality signals. We have performed both simulation and in vitro experiments to evaluate the efficiency of two shielding designs, showing that bell-type devices have better performance. As a proof-of-concept, we implanted the device on the brain in a mouse model and high signal-to-noise ratio (SNR) neural signals have been recorded successfully.

Eliciting artificial somatosensation using brain stimulation has become one of the most essential techniques in closed-loop brain machine interface (BMI) system to control robotic limb. To date, however, clinical researchers have struggled to precisely control the quality of induced artificial somatosensation due to limited stimulation methods. Here, we developed dynamic frequency and multi-site cortical stimulation methods using clinically approved cortical stimulators. Additionally, we report results of our tests on one patient to validate these techniques.

Neurological disorders can lead to significant impairments in speech communication and, in severe cases, cause the complete loss of the ability to speak. Brain-Computer Interfaces have shown promise as an alternative communication modality by directly transforming neural activity of speech processes into a textual or audible representations. Previous studies investigating such speech neuroprostheses relied on electrocorticography (ECoG) or microelectrode arrays that acquire neural signals from superficial areas on the cortex. While both measurement methods have demonstrated successful speech decoding, they do not capture activity from deeper brain structures and this activity has therefore not been harnessed for speech-related BCIs. In this study, we bridge this gap by adapting a previously presented decoding pipeline for speech synthesis based on ECoG signals to implanted depth electrodes (sEEG). For this purpose, we propose a multi-input convolutional neural network that extracts speech-related activity separately for each electrode shaft and estimates spectral coefficients to reconstruct an audible waveform. We evaluate our approach on open-loop data from 5 patients who conducted a recitation task of Dutch utterances. We achieve correlations of up to 0.80 between original and reconstructed speech spectrograms, which are significantly above chance level for all patients (p < 0.001). Our results indicate that sEEG can yield similar speech decoding performance to prior ECoG studies and is a promising modality for speech BCIs.

ElectroCOrticoGraphy (ECoG) technology measures electrical activity in the human brain via electrodes placed directly on the cortical surface during neurosurgery. Through its capability to record activity at an extremely fast temporal resolution, ECoG experiments have allowed scientists to better understand how the human brain processes speech. By its nature, ECoG data is extremely difficult for neuroscientists to directly interpret for two major reasons. Firstly, ECoG data tends to be extremely large in size, as each individual experiment yields data up to several GB. Secondly, ECoG data has a complex, higher-order nature; after signal processing, this type of data is typically organized as a 4-way tensor consisting of trials by electrodes by frequency by time. In this paper, we develop an interpretable dimension reduction approach called Regularized Higher Order Principal Components Analysis, as well as an extension to Regularized Higher Order Partial Least Squares, that allows neuroscientists to explore and visualize ECoG data. Our approach employs a sparse and functional Candecomp-Parafac (CP) decomposition that incorporates sparsity to select relevant electrodes and frequency bands, as well as smoothness over time and frequency, yielding directly interpretable factors. We demonstrate both the performance and interpretability of our method with an ECoG case study on audio and visual processing of human speech.

Long-term, continuous monitoring of human brain activity with closed-loop precise neurostimulation can potentially help to treat conditions such as epilepsy and Parkinson. These implantable devices are used to sense the brain signal, detect an abnormality, and stimulate once the abnormal activity is detected to mitigate the adverse effect. The target is to achieve the maximum efficacy while balancing the signal acquisition and intelligent processing to ensure minimize battery replacement frequency. This paper presents the design of the implantable Electrocorticography (ECoG) based system for intractable epileptic seizure treatment. The challenges, design choices, and trade-offs, related to the neurological disorder in the implantable environment are discussed. A multi-channel ultra-low-power instrumentation amplifier (IA) with digital electrode offset rejection loop (EORL) with a cutoff frequency of < 0.5Hz to mitigate the electrode offset effect with a fast settling of < 0.1 Sec to ensure real-time recording. The implantable system is realized in 180nm CMOS process to ensure area-and-power efficient design.

In present paper we introduce an extended machine-learning-based approach to detect inter-areal functional connectivity based on artificial neural network (ANN). We prove the efficiency of the proposed method by applying multi-layer perceptron to find functional relations between the cortex and thalamus of the pathological WAG/Rij rats during the epileptic seizure based on the multivariate electrocorticography (ECoG) recordings. We show that the proposed algorithm is able to reconstruct the increased coupling within a thalamo-cortical network during the seizure versus a baseline activity.

Surgical removal of the seizure onset zone (SOZ) in epilepsy patients is a potentially curative treatment, but the process heavily relies on accurate localization of the SOZ via visual inspection. SPES (Single-pulse electrical stimulation) is a method recently used to explore inter-areal connectivity in vivo to probe functional brain networks such as language and motor networks, and to a much lesser degree, seizure networks. We hypothesized that a dynamical quantification of the connectivity networks derived from the evoked responses induced by SPES could also be used to localize the SOZ. To test our hypothesis, we used an intracranial EEG (iEEG) data set in which five epilepsy patients underwent extensive SPES evaluation. For each patient, and for each dataset that stimulated a different pair of electrodes, we constructed a state-space model from the patient's data. Specifically, we simultaneously estimated model parameters under an exogenous pulse input to a dynamical system whose state vector consisted of the response iEEG signals. Then, the size of the reachable state space, as quantified by the maximum singular value of the reachability matrix, σ max (R), was computed and denoted as the "largest" network response possible when stimulating the given pair. Our results suggest high agreement between σ max (R) and clinically annotated SOZ for patients with localizable SOZs.Clinical Relevance— Our study applies dynamical systems theory to identify epileptogenic brain regions, creating a novel tool that clinicians may use in surgical planning for medically-refractory epilepsy patients.

Surgical resection of the seizure onset zone (SOZ) could potentially lead to seizure-freedom in medically refractory epilepsy patients. However, localizing the SOZ can be a time consuming and tedious process involving visual inspection of intracranial electroencephalographic (iEEG) recordings captured during passive patient monitoring. Single pulse electrical stimulation (SPES) is currently performed on patients undergoing invasive EEG monitoring for the main purposes of mapping functional brain networks such as language and motor networks. We hypothesize that evoked responses from SPES can also be used to localize the SOZ as they may express the natural frequencies and connectivity of the iEEG network. To test our hypothesis, we construct patient specific single-input multi-output transfer function models from the evoked responses recorded from five epilepsy patients that underwent SPES evaluation and iEEG monitoring. Our preliminary results suggest that the stimulation electrodes that produced the highest gain transfer functions, as measured by the ${\mathcal{H}_\infty }$ norm, correspond to those electrodes clinically defined in the SOZ in successfully treated patients.Clinical Relevance- This study creates an innovative tool that allows clinicians to identify the seizure onset zone in medically refractory epilepsy patients using quantitative metrics thereby increasing surgical success outcomes, mitigating patient risks, and decreasing costs.

Early detection of mental fatigue and changes in vigilance could be used to initiate neurostimulation to treat patients suffering from brain injury and mental disorders. In this study, we analyzed electrocorticography (ECoG) signals chronically recorded from two non-human primates (NHPs) as they performed a cognitively demanding task over extended periods of time. We employed a set of biomarkers to identify mental fatigue and a gradient boosting classifier to predict the performance outcome, seconds prior to the actual behavior response. An average F1 score of 75.4% ± 8.4% and 86.4%±6.6% was obtained for the two studied NHPs. Our preliminary results demonstrate the feasibility of detecting mental fatigue in healthy primates that could be used for closed-loop control of neurostimulation therapy.

Real-time emotional state recognition from neural activities has great potentials, e.g., enabling closed-loop systems to treat neuropsychiatric disorders. Besides its utility in identifying epileptogenic brain regions, electrocorticography (ECoG) provides the opportunity to study brain activities during various emotional events. In this study, we aim to test if the second order statistics such as the electrode network connectivity are relevant in discriminating different emotions. Specifically, we adopt the statistical dependence as the connectivity measure and use sparse logistic regression for classification based on the connectivity features. The practical issues of limited samples and imbalanced data are addressed. We show that the data with the highest synchronization in the γ band provide the most competitive performance. The identified brain regions involved in the most discriminative connectivity patterns are consistent with the previous findings in the literature.

Several recent studies proposed various methods for reconstructing natural images from human functional magnetic resonance imaging (fMRI) data. However, few studies have proposed reconstruction methods for electrophysiolgical brain activities such as electroencephalography (EEG) and electrocorticography (ECoG). To investigate whether natural images can be reconstructed from electrophysiological brain activities, we conducted a large-scale experiment on natural image reconstruction from ECoG signals using deep learning. We first recorded ECoG signals from two macaque monkeys while presenting diverse natural images. Then, we trained several deep learning models for reconstructing presented images from ECoG signals. Comparing reconstruction models, we find that models trained with an adversarial loss produced reconstructions that contain visible features in presented images. Furthermore, our results with downsampled ECoG signals show the importance of rich temporal dynamics in ECoG signals for image reconstruction. Our results indicate the possibility of reconstructing diverse natural images from electrophysiological brain activities using deep learning.

Motor decoding at the level of individual finger movements is critical for high-performance brain-machine interface (BMI) applications. In this work, we propose to exploit the temporal dynamics of the multi-channel electrocorticography (ECoG) signal from human subjects and modern machine learning algorithms to improve the finger-level movement classification accuracy. Using a decision tree ensemble as the classifier and the temporally-concatenated features of ECoG as input, we achieved an average classification accuracy of 71.3%±7.1% on 3 subjects, 6.3% better than the state-of-the-art approach based on conditional random fields (CRF) on the same dataset. Our proposed method could enable a high-performance and minimally invasive cortical BMI for paralyzed patients.

Absence epilepsy is a common childhood disorder featuring frequent cortical spike-wave seizures with a loss of awareness and behavior. Using the calcium indicator GCaMP6 with in vivo 2-photon cellular microscopy and simultaneous electrocorticography, we examined the collective activity profiles of individual neurons and surrounding neuropil patches in layer 2/3 (L2/3) of the visual cortex during spike-wave seizure activity over prolonged periods in 2 different stargazer mice. Our long-term objective is to predict in real-time a seizure. In this work, we focused on identifying the neuronal networks activated during epochs of interictal activity (i.e., between seizure activities) and seizure activity and analyzed their functional network connectivity. During interictal activity, most neurons are functionally connected with a large number of neighbors within the field of view, while in seizure epochs, the connectivity is reduced substantially. We also examined the discriminating power of groups of neurons in identifying seizure events. An SVM model based on the firing activity of neurons can reasonably accurately classify the interictal activity vs. seizure (e.g., 77.9% for the total accuracy with sensitivity equal to 85.3%, and specificity 73%).

A multi-channel ADC structure is presented, which is dedicated to the digitization of multi-channel ECoG signals. The ADC is designed to decompose the signals on all the channels into one shared common baseline component and channel-specific differential components containing channel activities. The baseline component is digitized using a low-resolution ADC (BL-ADC), and the differential channel activities are converted to digital using a time-multiplexed sub-ADC. Designed to benefit from the spatial redundancy that exists in the common baseline signal, the proposed structure exhibits low power consumption and small silicon area. Another advantage of the proposed ADC scheme is the reduction achieved in the extent of the output digital data. This is because of sharing the common baseline data among all the channels. Fully-integrated version of the 8-channel ADC was designed in a 0.18-µm CMOS process, occupying about 0.015 mm2 of silicon area. Operating at a supply voltage of 1.8 V, sampling frequency of 1 kS/s for BL-ADC and 8 kS/s for the time-shared sub-ADC, the entire circuit dissipates 0.82µW.

In patients with epilepsy, convulsive seizures are often followed by a postictal generalized EEG suppression (PGES) state characterized by reduced background activity. Recent studies found a correlation between seizure termination state and PGES duration, and suggested that PGES is the result of the cessation of neuronal activity. To test that assertion, we investigated ten seizure records obtained from intracranial EEG (iEEG) from six patients, four of which had Engel Class 1 surgical outcome. In each case expert neurologists identified the most likely seizure onset electrode. We found the iEEG equivalent of PGES and an artifact-free preictal quiescent state of the same window size. Using index of cross-frequency coupling (ICFC) we identified the degree of coupling and dominant frequency bands involved in PGES and preictal quiescent states, and quantified the areas of high ICFC. We found that there was an increase in the degree of coupling between the 0.5-1.5Hz with high gamma frequency bands in the PGES states. We found that among all of the patients, as well as in Engel Class 1 patients specifically, the change in the quantified area of high ICFC was significant (p < 0.05) between PGES and preictal quiescent states. Furthermore, we were able to identify whether a recording was from a depth or subdural electrode, or whether it was from seizure onset zone or not using ICFC markers in PGES. This suggests that there are frequency coupling markers that successfully identify PGES and that there are underlying dynamics that occur in this seemingly quiet postictal state.

Implantable sensors for recording neural activity are often used for a variety of applications, including epilepsy studies. Current versions of these recorders are highly-invasive impractical implants and undesirable in daily life. To address this, for the first time we present a novel fully- implantable and passive system for recording characteristic epileptic activity. In this paper, we focus on recording interictal epileptiform discharges (IEDs), known indicators of clinical significance. IEDs can serve to identify the location of seizure onset zones. Notably, in the case of temporal lobe epilepsy (TLE). Here, we present IED signals recorded using neural probes previously tested with our system and demonstrated to be capable of sensing signals as low as 15 µVpp in amplitude. These recordings refer to actual animal experiments and are indicative of the broad spectrum of neural signals that can be recorded.

Recently, high-frequency oscillations (HFOs) of range 80-500 Hz in electroencephalogram (EEG) recordings of epilepsy patients are considered as a reliable marker of epileptic seizure. In the present work, an automatic detection of HFOs represents an isolated peak (an `island') in a time-frequency plot based on convolutional neural network (CNN) was proposed. Initially, three patients with medically intractable epilepsy were recruited. They underwent a presurgical monitoring individually with around 54-90 channels of intracranial electroencephalograph (iEEG). Then, a specific CNN with five layers was developed with a total of 18,400 time-frequency island pictures marked with a label of either a real HFO or a false HFO. They are in the range of 80-500 Hz in the recorded iEEGs of 312 hours. Besides, over 7940 pictures including 3970 real HFO events and 3970 false HFO events except the training set were used to evaluate the performance of the current proposed method. As a result, the obtained precision of HFO events, the value of the recall, and the F1 score of the proposed CNN were found to be 94.19%, 89.37%, and 91.71%, respectively. Additionally, the automatic detection time of each HFO event is limited within 1-3 seconds. In summary, the proposed HFOs detector with deep learning would be more efficient and useful in the diagnosis of epilepsy as compared with the current manual determination of each HFOs from a long-term multichannel iEEGs recordings.

Motor Brain-Computer Interfaces (BCIs) are systems that allow severely motor-impaired patients to use their brain activity to interact with their environment. Electrocorticography (ECoG) arrays may be profitably used to develop safe and chronic motor BCI systems. BCI signal processing pipelines generally include neuronal signal pre-processing, feature extraction and classification/regression. The article presents a comparative study addressing the problem of neural feature classification in asynchronous multi-limb ECoG-driven BCIs. Several conventional classifiers often reported in the BCI literature were coupled with two preprocessing techniques and with a conventional feature extraction approach. They were compared to artificial neural network (ANN) end-to-end classifiers which mimic conventional BCI signal processing pipelines. Different initializations of ANNs were particularly studied. The comparison study was carried out using publicly available datasets (BCI competition IV).

High-gamma (Hγ) activity from electrocorticography (ECoG) is a common-used signal for understanding the human brain, but its interpretation is impeded by a lack of spatial localization. To address this, we developed a novel recording approach to simultaneously record μECoG cortical surface electrical potentials (CSEPs) and laminar multiunit activity (MUA). We demonstrate that stimulus evoked CSEPs carry a multi-modal frequency response, peaking in the Hγ range. Laminar MUA responses exhibited similar tuning to CSEP Hγ directly over the intracortical recording site, suggesting a functional relationship. We fit CSEP Hγ to the simultaneously-recorded laminar MUA using a state-of-the-art sparse multi-linear regression model to identify laminar contributions to cortical surface Hγ. Our results indicate that CSEP Hγ recorded by ECoG reflects spiking activity from neurons in layer 3. These results provide initial insight into localizing the sources of CSEPs, which will guide clinical and BMI device decisions.

Cortical stimulation mapping (CSM) is a common clinical procedure for mapping eloquent cortex in epilepsy patients. Electrical responses to the stimulation, or after-discharges (ADs), that occur in response to stimulation can point to unstable regions of cortex that are more prone to spontaneous seizures. Clinicians are interested in identifying regions that start seizures, i.e., the epileptogenic zone (EZ), so that they can target treatment. However, during CSM, not all regions are stimulated, as it would be time-consuming and potentially harmful to the patient. This limits the clinician's ability to fully explore ADs to reliably localize the EZ. In this paper, we develop a virtual CSM procedure that processes pre-seizure intracranial EEG recordings obtained from epilepsy patients being treated at three different epilepsy centers. First, we identify a linear time varying network (LTVN) model from electrocorticography (ECoG) and stereo-EEG (SEEG) data using sparse least squares estimation for each patient. We then construct an virtual CSM by applying impulse perturbations to each electrode contact in the LTVN model and then measuring the ADs of the network. We summarize the l2-norm of the responses in the form of a heatmap that shows the spatio-temporal evolution of the ADs before, during, and after seizures. Finally we compute an impulse response ratio (IRR) metric from each heatmap, that measures the ratio between the mean norm of ADs of clinically annotated EZ contacts and the mean norm of ADs of the remaining contacts. We find that the IRR is higher in maps derived from patients with successful surgical outcomes and lower in failed surgical outcomes. This suggests that virtual CSM may provide valuable information to clinicians regarding EZ location.

High-Frequency-Oscillations (HFO) are biomarkers of the epileptogenic-zone (EZ) and thus a potential aid in guiding epilepsy-surgery. HFO are normally sub-divided according to their oscillating-frequency into Ripples (80-250 Hz) and Fast-Ripples (FR) (250-500 Hz) and are known to also occur in the non-epileptic brain. We address two challenges faced by HFO: firstly, estimating the margins of the EZ using the HFO occurrence-rate from each intracranial EEG channel; secondly, selecting HFO sub-groups with a higher probability of being purely epileptic. We propose the clustering of channels with high HFO occurrence-rates as a deterministic method to delimit the EZ. Additionally, we perform the EZ estimation using 9 sub-groups of HFO; these sub-groups are determined by their temporal and spatial coincidence with intracranial interictal-epileptic-spikes (IES) and are assumed to have varying levels of epileptogenicity. The EZ estimated with the different HFO-sub-groups are compared between themselves and with a proxy of the factually undefinable EZ, namely the resected-volume (RV). The proposed clustering method proved to be deterministic and allowed estimating the EZ for each patient and each HFO-sub-group. Those Ripples assumed to be more epileptogenic occurred in lower numbers than all Ripples but showed the highest correspondence with the RV. All FR sub-groups showed a high specificity to the RV. The proposed clustering method successfully extracted the information from the HFO occurrence-rate to estimate the EZ. The selection of more epileptogenic HFO based on their coincidence with IES proved to be of value for both Ripples and FR.

Seizures in patients with medically refractory epilepsy (MRE) cannot be controlled with drugs. For focal MRE, seizures originate in the epileptogenic zone (EZ), which is the minimum amount of cortex that must be treated to be seizure free. Localizing the EZ is often a laborious process wherein clinicians first inspect scalp EEG recordings during several seizure events, and then formulate an implantation plan for subsequent invasive monitoring. The goal of implantation is to place electrodes into the brain region covering the EZ. Then, during invasive monitoring, clinicians visually inspect intracranial EEG recordings to more precisely localize the EZ. Finally, the EZ is then surgically ablated, removed or treated with electrical stimulation. Unfortunately success rates average at 50%. Such grim outcomes call for analytical assistance in creating more accurate implantation plans from scalp EEG. In this paper, we introduce a method that combines imaging data (CT and MRI scans) with scalp EEG to derive an implantation distribution. Specifically, scalp EEG data recorded over a seizure event is converted into a time-gamma frequency map, which is then processed to derive a spectrally annotated implantation distribution (SAID). The SAID represents a distribution of gamma power in each of eight cortical lobe/hemisphere partitions. We applied this method to 4 MRE patients who underwent treatment, and found that the SAID distribution overlapped more with clinical implantations in success cases than in failed cases. These preliminary findings suggest that the SAID may help in improving EZ localization accuracy and surgical outcomes.

Deep brain stimulation (DBS) is well recognized as an effective treatment for symptoms of movement disorders such as Parkinson's disease (PD), Essential Tremor, and dystonia. The selection of the appropriate contact on the DBS lead for optimal clinical efficacy can be challenging, particularly when considering directional leads. Electroencephalograms (EEG) and electrocorticography has been utilized to better understand the pathophysiology of PD but a methodology to provide an objective biomarker of effective stimulation has yet to be developed. Using machine learning techniques for feature extraction and classification, we contrast high resolution EEG captured during DBS against its resting state counterpart with the DBS off. We demonstrate, using 16 patients under DBS treatment for movement disorders, EEG's informative capacity to detect both effective DBS and the region undergoing stimulation.

the infrequent nature of seizure events in epilepsy has engaged researchers to find alternative ways to accurately predict seizure onset regions. We implemented a hybrid approach combining the granger causality based network estimation with subsequent measurement of graph centrality measures in the interictal electrocorticography (ECoG) data to predict seizure onset zones. Critical to the success of this method was dividing analysis into two stages: first, detecting epileptic spikes and second, identifying their functional network. We analyzed interictal data from 8 epileptic patients and found that the epileptic spike functional network is a robust predictor of physician-identified seizure onset compared to the commonly used total ECoG network. Moreover, we discovered that PageRank, indegree and hub centrality measures best predict seizure onset regions. This establishes several features for automated detection of seizure onset zones, and can assist physicians in surgical planning and decisions.