A brain-computer interface (BCI) based on motor imagery (MI) provides additional control pathways by decoding the intentions of the brain. MI ability has great intra-individual variability, and the majority of MI-BCI systems are unable to adapt to this variability, leading to poor training effects. Therefore, prediction of MI ability is needed. In this study, we propose an MI ability predictor based on multi-frequency EEG features. To validate the performance of the predictor, a video-guided paradigm and a traditional MI paradigm are designed, and the predictor is applied to both paradigms. The results demonstrate that all subjects achieved > 85

Historically, eloquent functions have been viewed as localized to focal areas of human cerebral cortex, while more recent studies suggest they are encoded by distributed networks. We examined the network properties of cortical sites defined by stimulation to be critical for speech and language, using electrocorticography from sixteen participants during word-reading. We discovered distinct network signatures for sites where stimulation caused speech arrest and language errors. Both demonstrated lower local and global connectivity, whereas sites causing language errors exhibited higher inter-community connectivity, identifying them as connectors between modules in the language network. We used machine learning to classify these site types with reasonably high accuracy, even across participants, suggesting that a site’s pattern of connections within the task-activated language network helps determine its importance to function. These findings help to bridge the gap in our understanding of how focal cortical stimulation interacts with complex brain networks to elicit language deficits. It is unknown how cortical stimulation identifies brain regions critical to speech and language when they depend upon broader brain networks. Here the authors show that these critical areas function as connectors between modules in the language network.

Objective: As single pulse electrical stimulation (SPES) is increasingly utilized to help localize the seizure onset zone (SOZ), it is important to understand how stimulation intensity can affect the ability to use cortico-cortical evoked potentials (CCEPs) to delineate epileptogenic regions.Methods: We studied 15 drug-resistant epilepsy patients undergoing intracranial EEG monitoring and SPES with titrations of stimulation intensity. The N1 amplitude and distribution of CCEPs elicited in the SOZ and non-seizure onset zone (nSOZ) were quantified at each intensity. The separability of the SOZ and nSOZ using N1 amplitudes was compared between models using responses to titrations, responses to one maximal intensity, or both.Results: At 2 mA and above, the increase in N1 amplitude with current intensity was greater for responses within the SOZ, and SOZ response distribution was maximized by 4-6 mA. Models incorporating titrations achieved better separability of SOZ and nSOZ compared to those using one maximal intensity. Conclusions: We demonstrated that differences in CCEP amplitude over a range of current intensities can improve discriminability of SOZ regions.Significance: This study provides insight into the underlying excitability of the SOZ and how differences in current-dependent amplitudes of CCEPs may be used to help localize epileptogenic sites.(c) 2022 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Neural activity emerges and propagates swiftly between brain areas. Investigation of these transient large-scale flows requires sophisticated statistical models. We present a method for assessing the statistical confidence of event-related neural propagation. Furthermore, we propose a criterion for statistical model selection, based on both goodness of fit and width of confidence intervals. We show that event-related causality (ERC) with two-dimensional (2D) moving average, is an efficient estimator of task-related neural propagation and that it can be used to determine how different cognitive task demands affect the strength and directionality of neural propagation across human cortical networks. Using electrodes surgically implanted on the surface of the brain for clinical testing prior to epilepsy surgery, we recorded electrocorticographic (ECoG) signals as subjects performed three naming tasks: naming of ambiguous and unambiguous visual objects, and as a contrast, naming to auditory description. ERC revealed robust and statistically significant patterns of high gamma activity propagation, consistent with models of visually and auditorily cued word production. Interestingly, ambiguous visual stimuli elicited more robust propagation from visual to auditory cortices relative to unambiguous stimuli, whereas naming to auditory description elicited propagation in the opposite direction, consistent with recruitment of modalities other than those of the stimulus during object recognition and naming. The new method introduced here is uniquely suitable to both research and clinical applications and can be used to estimate the statistical significance of neural propagation for both cognitive neuroscientific studies and functional brain mapping prior to resective surgery for epilepsy and brain tumors.

Speaking in sentences requires selection from contextually determined lexical representations. Although posterior temporal cortex (PTC) and Broca's areas play important roles in storage and selection, respectively, of lexical representations, there has been no direct evidence for physiological interactions between these areas on time scales typical of lexical selection. Using intracranial recordings of cortical population activity indexed by high-gamma power (70-150 Hz) modulations, we studied the causal dynamics of cortical language networks while epilepsy surgery patients performed a sentence completion task in which the number of potential lexical responses was systematically varied. Prior to completion of sentences with more response possibilities, Broca's area was not only more active, but also exhibited more local network interactions with and greater top-down influences on PTC, consistent with activation of, and competition between, more lexical representations. These findings provide the most direct experimental support yet for network dynamics playing a role in lexical selection among competing alternatives during speech production.

Functional human brain mapping is commonly performed during invasive monitoring with intracranial electroencephalographic (iEEG) electrodes prior to resective surgery for drug- resistant epilepsy. The current gold standard, electrocortical stimulation mapping (ESM), is time -consuming, sometimes elicits pain, and often induces after discharges or seizures. Moreover, there is a risk of overestimating eloquent areas due to propagation of the effects of stimulation to a broader network of language cortex. Passive iEEG spatial-temporal functional mapping (STFM) has recently emerged as a potential alternative to ESM. However, investigators have observed less correspondence between STFM and ESM maps of language than between their maps of motor function. We hypothesized that incongruities between ESM and STFM of language function may arise due to propagation of the effects of ESM to cortical areas having strong effective connectivity with the site of stimulation. We evaluated five patients who underwent invasive monitoring for seizure localization, whose language areas were identified using ESM. All patients performed a battery of language tasks during passive iEEG recordings. To estimate the effective connectivity of stimulation sites with a broader network of task-activated cortical sites, we measured cortico-cortical evoked potentials (CCEPs) elicited across all recording sites by single-pulse electrical stimulation at sites where ESM was performed at other times. With the combination of high gamma power as well as CCEPs results, we trained a logistic regression model to predict ESM results at individual electrode pairs. The average accuracy of the classifier using both STFM and CCEPs results combined was 87.7%, significantly higher than the one using STFM alone (71.8%), indicating that the correspondence between STFM and ESM results is greater when effective connectivity between ESM stimulation sites and task-activated sites is taken into consideration. These findings, though based on a small number of subjects to date, provide preliminary support for the hypothesis that incongruities between ESM and STFM may arise in part from propagation of stimulation effects to a broader network of cortical language sites activated by language tasks, and suggest that more studies, with larger numbers of patients, are needed to understand the utility of both mapping techniques in clinical practice.

Behavioral responses to a perceptual stimulus are typically faster with repeated exposure to the stimulus (behavioral priming). This implicit learning mechanism is critical for survival but impaired in a variety of neurological disorders, including Alzheimer's disease. Many studies of the neural bases for behavioral priming have encountered an interesting paradox: in spite of faster behavioral responses, repeated stimuli usually elicit weaker neural responses (repetition suppression). Several neurophysiological models have been proposed to resolve this paradox, but noninvasive techniques for human studies have had insufficient spatial-temporal precision for testing their predictions. Here, we used the unparalleled precision of electrocorticography (ECoG) to analyze the timing and magnitude of task-related changes in neural activation and propagation while patients named novel vs repeated visual objects. Stimulus repetition was associated with faster verbal responses and decreased neural activation (repetition suppression) in ventral occipito-temporal cortex (VOTC) and left prefrontal cortex (LPFC). Interestingly, we also observed increased neural activation (repetition enhancement) in LPFC and other recording sites. Moreover, with analysis of high gamma propagation we observed increased top-down propagation from LPFC into VOTC, preceding repetition suppression. The latter results indicate that repetition suppression and behavioral priming are associated with strengthening of top-down network influences on perceptual processing, consistent with predictive coding models of repetition suppression, and they support a central role for changes in large-scale cortical dynamics in achieving more efficient and rapid behavioral responses.

Any given area in human cortex may receive input from multiple, functionally heterogeneous areas, potentially representing different processing threads. Alpha (8-13 Hz) and beta oscillations (13-20 Hz) have been hypothesized by other investigators to gate local cortical processing, but their influence on cortical responses to input from other cortical areas is unknown. To study this, we measured the effect of local oscillatory power and phase on cortical responses elicited by single-pulse electrical stimulation (SPES) at distant cortical sites, in awake human subjects implanted with intracranial electrodes for epilepsy surgery. In 4 out of 5 subjects, the amplitudes of corticocortical evoked potentials (CCEPs) elicited by distant SPES were reproducibly modulated by the power, but not the phase, of local oscillations in alpha and beta frequencies. Specifically, CCEP amplitudes were higher when average oscillatory power just before distant SPES (-110 to -10 ms) was high. This effect was observed in only a subset (0-33%) of sites with CCEPs and, like the CCEPs themselves, varied with stimulation at different distant sites. Our results suggest that although alpha and beta oscillations may gate local processing, they may also enhance the responsiveness of cortex to input from distant cortical sites.

Neural keyword spotting could form the basis of a speech brain-computer-interface for menu-navigation if it can be done with low latency and high specificity comparable to the "wake-word" functionality of modern voice-activated AI assistant technologies. This study investigated neural keyword spotting using motor representations of speech via invasively-recorded electrocorticographic signals as a proof-of-concept. Neural matched filters were created from monosyllabic consonant-vowel utterances: one keyword utterance, and 11 similar non-keyword utterances. These filters were used in an analog to the acoustic keyword spotting problem, applied for the first time to neural data. The filter templates were cross-correlated with the neural signal, capturing temporal dynamics of neural activation across cortical sites. Neural vocal activity detection (VAD) was used to identify utterance times and a discriminative classifier was used to determine if these utterances were the keyword or non-keyword speech. Model performance appeared to be highly related to electrode placement and spatial density. Vowel height (/a/ vs /i/) was poorly discriminated in recordings from sensorimotor cortex, but was highly discriminable using neural features from superior temporal gyrus during self-monitoring. The best performing neural keyword detection (5 keyword detections with two false-positives across 60 utterances) and neural VAD (100% sensitivity, ~1 false detection per 10 utterances) came from high-density (2 mm electrode diameter and 5 mm pitch) recordings from ventral sensorimotor cortex, suggesting the spatial fidelity and extent of high-density ECoG arrays may be sufficient for the purpose of speech brain-computer-interfaces.

Cortical networks for speech production are believed to be widely distributed and highly organized over temporal, parietal, and frontal lobes areas in the human brain cortex. Effective connectivity demonstrates an inherent element of directional information propagation, and is therefore an information dense measure for the relevant activity over different cortical regions. Connectivity analysis of electrocorticographic (ECoG) recordings has been widely studied for its excellent signal-to-noise ratio as well as high temporal and spatial resolutions, providing an important approach to human electrophysiological researches. In this paper, we evaluate two patients undergoing invasive monitoring for seizure localization, in which both micro-electrode and standard clinical electrodes are used for ECoG recordings from speech-related cortical areas during syllable reading test. In order to explore the dynamics of speech processing, we extract the high gamma frequency band (70-110 Hz) power from ECoG signals by the multi-taper method. The trial-averaged results show that there is a consistent task-related increase in high gamma response for micro-ECoG electrodes for patient 1 and standard-ECoG electrodes for both patients 1 and 2. We demonstrate that high gamma response provides reliable speech localization compared with electrocortical stimulation. In addition, a directed connectivity network is built in single trial involving both standard ECoG electrodes and micro-ECoG arrays using time-varying dynamic Bayesian networks (TV-DBN). The TV-DBN is used to model the time-varying effective connectivity between pairs of ECoG electrodes selected by high gamma power, with less parameter optimization required and higher computational simplicity than short-time direct directed transfer function. We observe task-related connectivity modulations of connectivity between large-scale cortical networks (standard ECoG) and local cortical networks (micro-ECoG), as well as between large-scale and local cortical networks. In addition, cortical connectivity is modulated differently before and after response articulation onset. In other words, electrodes located over sensorimotor cortex show higher connectivity before articulation onset, while connectivity appears gradually between sensorimotor and auditory cortex after articulation onset. Also, the connectivity patterns observed during articulation are significantly different for three different places of articulation for the consonants. This study offers insights into preoperative evaluation during epilepsy surgery, dynamic real-time brain connectivity visualization, and assistance to understand the dynamic processing of language pronunciation in the language cortex.

Language tasks require the coordinated activation of multiple subnetworks-groups of related cortical interactions involved in specific components of task processing. Although electrocorticography (ECoG) has sufficient temporal and spatial resolution to capture the dynamics of event-related interactions between cortical sites, it is difficult to decompose these complex spatiotemporal patterns into functionally discrete subnetworks without explicit knowledge of each subnetwork's timing. We hypothesized that subnetworks corresponding to distinct components of task-related processing could be identified as groups of interactions with co-varying strengths. In this study, five subjects implanted with ECoG grids over language areas performed word repetition and picture naming. We estimated the interaction strength between each pair of electrodes during each task using a time-varying dynamic Bayesian network (tvDBN) model constructed from the power of high gamma (70-110Hz) activity, a surrogate for population firing rates. We then reduced the dimensionality of this model using principal component analysis (PCA) to identify groups of interactions with co-varying strengths, which we term functional network components (FNCs). This data-driven technique estimates both the weight of each interaction's contribution to a particular subnetwork, and the temporal profile of each subnetwork's activation during the task. We found FNCs with temporal and anatomical features consistent with articulatory preparation in both tasks, and with auditory and visual processing in the word repetition and picture naming tasks, respectively. These FNCs were highly consistent between subjects with similar electrode placement, and were robust enough to be characterized in single trials. Furthermore, the interaction patterns uncovered by FNC analysis correlated well with recent literature suggesting important functional-anatomical distinctions between processing external and self-produced speech. Our results demonstrate that subnetwork decomposition of event-related cortical interactions is a powerful paradigm for interpreting the rich dynamics of large-scale, distributed cortical networks during human cognitive tasks.

Editors' Note: In WriteClick this week, Dr. Nirenberg responds to “Augmentation and impulsive behaviors in restless legs syndrome: Coexistence or association?” with the hypothesis that augmentation in restless legs syndrome may be a forme fruste of dopamine agonist withdrawal syndrome. Authors Djamshidian et al. report no association between augmentation and medication nonadherence, but agree with monitoring these patients closely for addictive behaviors. In reference to “Spatial-temporal functional mapping of language at the bedside with electrocorticography,” Drs. Babajani-Feremi et al. and authors Wang et al. discuss the use of high-gamma electrocorticography, electrocortical stimulation mapping, …

Intracranial electroencephalographic (iEEG) signals from two human subjects were used to achieve simultaneous neural control of reaching and grasping movements with the Johns Hopkins University Applied Physics Lab (JHU/APL) Modular Prosthetic Limb (MPL), a dexterous robotic prosthetic arm. We performed functional mapping of high gamma activity while the subject made reaching and grasping movements to identify task-selective electrodes. Independent, online control of reaching and grasping was then achieved using high gamma activity from a small subset of electrodes with a model trained on short blocks of reaching and grasping with no further adaptation. Classification accuracy did not decline (p <; 0.05, one-way ANOVA) over three blocks of testing in either subject. Mean classification accuracy during independently executed overt reach and grasp movements for (Subject 1, Subject 2) were (0.85, 0.81) and (0.80, 0.96), respectively, and during simultaneous execution they were (0.83, 0.88) and (0.58, 0.88), respectively. Our models leveraged knowledge of the subject's individual functional neuroanatomy for reaching and grasping movements, allowing rapid acquisition of control in a time-sensitive clinical setting. We demonstrate the potential feasibility of verifying functionally meaningful iEEG-based control of the MPL prior to chronic implantation, during which additional capabilities of the MPL might be exploited with further training.

Language function mapping has been one of the major concerns for epilepsy surgeries due to individual diversity and the complicated distribution over the cortex. The general linear model (GLM) was used here to analyze the power change of electrocorticograpic (ECoG) signals in the Beta (8-13 Hz) and High gamma (60-90 Hz) bands to obtain dynamic functional mapping for different stages during a single character word reading task. The high temporal resolution of ECoG can distinguish the motor cortex and related language preparing brain area. The mapping results were presented in a custom 3D visualization platform for individual brain structures to show more accurate and intuitive activation of the speech areas. Comparison with functional magnetic resonance imaging (fMRI) demonstrates that this method provides both reliable localization and temporal processing of language functional areas with high gamma responses using ECoG signals with high temporal resolution (300-400 ms).