Direct electrocortical stimulation (ECS) is a well-established brain mapping technique that helps achieve safe and effective resection of epileptic foci, tumors or vascular malformations. Recent studies using electrocorticography (ECoG) suggest that ECS' exerted effects on brain sites are determined by the roles of those sites in larger networks. However, ECoG has limited spatial coverage. Here, we used functional magnetic resonance imaging from eighteen participants during performance of five language tasks to assess the functional network signatures of cortical sites defined as critical for speech and language by ECS. We found that critical sites causing speech arrests (SA) and language errors (LE) exhibited lower local and global connectivity than non-critical sites. LE sites showed greater connectivity across sub-networks (communities) than both non-critical and SA sites, indicating their role as connectors across functional networks. This connector profile of LE sites was most robust when considering network connectivity across the entire brain. Connector sites were concentrated primarily in temporal and inferior parietal cortices. Finally, these network features accurately predicted which sites were critical in machine learning models. These findings provide a preoperative framework for predicting cortical sites critical for speech and language function, which may ultimately help accelerate or improve brain mapping.

Background:The field of implantable Brain-Computer Interfaces (iBCIs) is rapidly advancing, with individuals with amyotrophic lateral sclerosis (ALS) as key beneficiaries. However, ALS-related cortical degeneration may impair iBCI effectiveness. This study investigated whether structural magnetic resonance imaging (MRI) and functional MRI (fMRI) metrics are associated with the quality of electrocorticography (ECoG) signals critical for iBCI use. Methods:Six late-stage ALS participants and 76 controls underwent T1-weighted structural MRI and task-based fMRI during right-hand movement or attempts thereof. ECoG data of ALS participants was benchmarked using ECoG data acquired in epilepsy patients. Grey matter thickness in the sensorimotor cortex and fMRI activation in the motor-hand area were measured. Results:Four ALS participants showed >0.4 mm thinning in the precentral gyrus, while the postcentral gyrus was spared. ECoG signal quality was significantly associated with precentral grey matter thickness, but not with fMRI activity. Conclusions:These findings suggest that presurgical assessment of precentral grey matter thickness could potentially prove useful for iBCI candidate selection in advanced ALS.

Millions of people worldwide are living with movement and sensory impairments owing to spinal cord injury, stroke and other neurological conditions. Here we report a double neural bypass (DNB), a hybrid neuroprosthetic system designed to restore both immediate and lasting gains in movement and sensation after a severe, complete spinal cord injury. The DNB links an intracortical brain-computer interface with targeted and patterned neuromodulation of the spinal cord and cortex. This allows brain signals associated with movement intention to directly control the movement of the user's own hand in real time while also promoting long-term sensorimotor recovery-even after the system is turned off. The DNB system uses recurrent artificial neural networks and reinforcement learning for fine grasp control, together with patterned spinal cord stimulation and activity-informed intracortical microstimulation ('cortical mirroring') to promote neuroplasticity and durable recovery of function. In a participant with chronic C4 sensory/C5 motor complete tetraplegia, this hybrid approach enabled recovery of functional abilities including self-feeding and manipulation of delicate objects, while also producing significant and persistent improvements in elbow flexion and wrist tactile sensation. These findings demonstrate the potential of combining a sensorimotor neuroprosthesis with targeted brain and spinal neuromodulation to restore clinically relevant function in severe paralysis.

Background:Electrocorticographic (ECoG) speech brain-computer interfaces (BCIs) show promise for restoring communication in amyotrophic lateral sclerosis (ALS), but the long-term stability of speech-related neural signals and decoding performance during disease progression remains unclear. We tracked signal characteristics and decoding over 25 months in a participant with ALS to determine how high-gamma (HG, 70-170 Hz) activity changes over time and whether these changes affect offline speech decoding. Methods:We implanted two 8×8 subdural ECoG grids over left sensorimotor cortex (SMC) in a participant with slowly progressive bulbar variant ALS. Across 25 months, the participant performed an overt syllable-repetition task (12 consonant-vowel tokens) during simultaneous ECoG and audio recording. We quantified HG activation ratio (ActR), spectral signal-to-noise ratio (SNR; HG/HF, where HF = 300-499 Hz), and peak z-scored HG responses. Speech acoustics were evaluated using first/second formants (F1/F2) and the triangular vowel space area (tVSA). Offline EEGNet-based decoders were assessed in two stages: models trained on post-implant months 1-6 were tested on months 7-25, while models trained on stabilized data (months 7-11) were tested on the remaining period (months 12-25). Electrode-level saliency assessed spatial contributions to decoding. Results:Acoustic analyses showed a significant reduction in tVSA over two years (-44.6 Hz2/day; P < 10-7), consistent with mild intelligibility decline. Neural metrics (ActR and SNR) followed a biphasic trajectory: increasing during the first 6 months, after which ActR stabilized (0.041%/day; P = 0.13), and SNR declined gradually (-0.46%/day, P < 10- 4). The model trained on months 1-6 achieved 55.7% accuracy (chance: 8.33%), but performance declined over time (-0.019%/day; P = 2.1×10-4). Conversely, the model trained on months 7-11 achieved higher accuracy (65.9%) on subsequent data with no significant temporal decline (P = 0.23). Conclusions:Speech-related HG features exhibited an initial unstable period followed by a long-term gradual SNR reduction, potentially reflecting disease progression. Models trained after signal stabilization generalized robustly to data recorded over a year later. These findings confirm that despite reduced absolute HG power and mild acoustic degradation of speech, cortical features remain stable enough to support durable ECoG speech BCIs without frequent recalibration. These findings will motivate future adaptive calibration algorithms that account for slow signal changes while leveraging stable spatial representations in ventral SMC. ClinicalTrialsgov Identifier:NCT03567213.

Abstract Brain-computer interfaces (BCIs) hold promise as assistive communication technology for people with severe paralysis. Although such BCIs should be available 24/7, feasibility of nocturnal BCI use has not been investigated. Here, we addressed this question using data from an electrocorticography-BCI user with amyotrophic lateral sclerosis. We investigated nocturnal dynamics of neural signal features used for BCI control. Additionally, we assessed nocturnal performance of a decoder trained on daytime data, by quantifying the number of unintentional BCI activations at night. Finally, we developed a nightmode functionality and assessed its performance. Mean and variance of low and high frequency band power were significantly higher at night than during the day. When applied to night data, daytime decoders caused unintentional BCI activations in 100% of nights (245 unintended click-commands and 13 unintended caregiver-calls per hour). The specifically developed nightmode functionality, however, functioned error-free in 79% of nights over a period of ± 1.5 years, allowing the user to reliably call the caregiver. Reliable nighttime use of a BCI requires strategies to adjust to circadian and sleep-related signal changes. This demonstration of a reliable nightmode and its long-term use by an individual with amyotrophic lateral sclerosis underscores the importance of 24/7 BCI reliability.

OBJECTIVE:Temporal lobe epilepsy (TLE) is the most common form of medically refractory epilepsy in adults. While patients with concordant noninvasive findings and hippocampal sclerosis (HS) might proceed directly to surgery, those with discordant data, suspected bilateral involvement, or atypical presentation often require stereoelectroencephalography (SEEG). The diagnostic and therapeutic implications of bilateral temporal sampling remain uncertain. The aim of this study was to evaluate the yield and clinical impact of bilateral SEEG in TLE. METHODS:The authors retrospectively reviewed data collected from patients with medically refractory epilepsy who underwent bilateral SEEG at a single institution from March 2017 and June 2025. Inclusion criteria were a pre-SEEG hypothesis of temporal onset, nonlesional or mesial TLE, with or without HS, and bilateral hippocampal/amygdala sampling. Patients were grouped as concordant, discordant, or bilateral based on pre-SEEG noninvasive data. The diagnostic yield, number of SEEG studies required to alter 1 patient's initial hypothesis, and seizure outcomes were analyzed. RESULTS:Of 197 patients who underwent SEEG, 54 met inclusion criteria. Pre-SEEG hypotheses were concordant in 20 patients, discordant in 8 patients, and bilateral in 26 patients. SEEG revealed contralateral or bilateral seizure onset in 35% of concordant cases and confirmed unilateral onset in 46% of presumed bilateral cases. Overall, 21% of presumed unilateral TLE showed bilateral involvement. The number needed to treat was 2.9 for the concordant group and 2.2 for the bilateral group. Following SEEG, 38 patients underwent resection, laser ablation, or neuromodulation. At the last follow-up, 49% of patients achieved Engel class I or II outcomes, with best results for those with resection/ablation (79% Engel class I or II). CONCLUSIONS:Bilateral SEEG provides clinically meaningful information in both unilateral and bilateral TLE, uncovering contralateral involvement in presumed unilateral cases and confirming unilateral foci in nearly half of presumed bilateral cases. Although SEEG is not necessary for all patients with TLE-HS and concordant studies, when it is indicated, bilateral sampling might be important to avoid misclassification and guide resective/ablative versus neuromodulatory treatment planning.

PURPOSE:To develop a method for statistically significant tumor delineation. Glioblastoma (GBM) remains resistant to current therapeutic strategies and is unequivocally associated with a dismal prognosis. Hyperpolarized 13C MRI (hpMRI) provides unique insights into tissue metabolism, enabling outlining of tumor boundaries. Statistical confidence of this outline may help guide surgical resection, inform therapeutic decisions, and evaluate treatment efficacy. METHODS:The new method was applied to time-resolved hyperpolarized [1-13C]pyruvate data from a previous study in a rat glioma model as well as from a new clinical acquisition from a patient with brain cancer. MATLAB R2021a was used for the comprehensive extraction and analysis of metabolite profiles. Time-points corresponding to high pyruvate and lactate signal intensities were combined, smoothed using a two-dimensional (2D) moving average, and analyzed with a sliding window to identify regions statistically distinct from manually segmented normal tissue. The negative predictive value (NPV) and the positive predictive value (PPV) of the detection method were evaluated by comparison with cancer regions delineated using standard radiological criteria. RESULTS:The new approach enabled glioblastoma delineation with the NPV of 99% and the PPV of 59% in small animals. While in the larger brain of the clinical patient, with bicarbonate taken into account, NPV and PPV obtained 95% and 92% respectively. CONCLUSION:Combining time-points of high pyruvate and lactate signal intensities increases the statistical power of two-dimensional testing. This promising technique requires further evaluation in a larger patient cohort.

OBJECTIVE:The aim of this study was to evaluate the feasibility of using the Layer 7 Cortical Interface, a high-density micro-electrocorticography (μECoG) array, for intraoperative neural recordings and real-time brain-computer interface (BCI) applications, including speech decoding and cursor control. METHODS:Four patients (age range 23-43 years) who underwent awake craniotomy for tumor resection near the eloquent cortex were enrolled. The Layer 7 µECoG device (1024 channels, approximately 1.5-cm2 coverage) was placed on the motor cortex following standard cortical mapping. Intraoperative tasks included a joystick-controlled center-out movement paradigm (n = 3) and an auditory-cued speech repetition task (n = 1). Neural data were recorded at 20 kHz, preprocessed, and used to train decoders intraoperatively. A transformer-based model was applied for real-time speech synthesis and a convolutional neural network was trained for speech classification, while a convolutional recurrent neural network was trained to classify 2D cursor direction. RESULTS:All 4 patients tolerated the procedure without device-related adverse events. The mean electrode impedances across 6 arrays (6144 channels) ranged from 1.21 to 1.99 MΩ, with 954-990 channels per array retained for analysis. In the speech task, a 4-word classification model achieved 77.5% accuracy, and a real-time synthesis model was able to distinguish speech and silence during approximately 20 minutes of data recording in the operating room. In the motor task, a 4-direction classification model achieved 78%-84% accuracy. Recordings remained stable during tumor resection. CONCLUSIONS:The Layer 7 Cortical Interface device enabled high-resolution nonpenetrating cortical recordings that supported real-time speech classification and cursor control within the limited timeframe of an intraoperative session. These findings highlight the potential clinical applications of high-density µECoG for functional mapping, diagnostic assessment, and future chronic BCI systems for patients with motor and communication impairments.

Introduction:More than 50 million people worldwide suffer from epilepsy. Approximately 30% of epileptic patients suffer from medically refractory epilepsy (MRE), which means that over 15 million people must seek extensive treatment. One such treatment involves surgical removal of the epileptogenic zone (EZ) of the brain. However, because there is no clinically validated biomarker of the EZ, surgical success rates vary between 30%-70%. The current standard for EZ localization often requires invasive monitoring of patients for several weeks in the hospital during which intracranial EEG (iEEG) data is captured. This process is time-consuming as the clinical team must wait for seizures and visually interpret the iEEG during these events. Hence, an iEEG biomarker that does not rely on seizure observations is desirable to improve EZ localization and surgical success rates. Recently, the source-sink index (SSI) was proposed as an interictal (between seizure) biomarker of the EZ, which captures regional interactions in the brain and in particular identifies the EZ as regions being inhibited ("sinks") by neighbors ("sources") when patients are not seizing. The SSI only requires 5-min snapshots of interictal iEEG recordings. However, one limitation of the SSI is that it is computed heuristically from the parameters of dynamical network models (DNMs). Methods:In this work, we propose a formal method for detecting sink regions from DNMs, which has a strong foundation in linear systems theory. In particular, the steady-state solution of the DNM highlights the sinks and is characterized by the leading eigenvector of the state-transition matrix of the DNM. To test this, we build patient-specific DNMs from interictal iEEG data collected from 65 patients treated across 6 centers. From each DNM, we compute the average leading eigenvectors and evaluate their potential as a biomarker to accurately predict EZ and surgical success. Results:Our findings show the ability of the leading eigenvector to accurately predict EZ (average accuracy 66.81% ± 0.19%) and surgical success (average accuracy 71.9% ± 0.22%) with data from 65 patients across 6 centers from 5 min of data, which we show is comparable with the current method of localizing the EZ over several weeks. Discussion:This eigenvector biomarker has the potential to assist clinicians in localizing the EZ quickly and thus increase surgical success in patients with MRE, resulting in an improvement in patient care and quality of life.

Brain-computer interfaces (BCIs) have the potential to preserve or restore communication and device control in people with paralysis from a variety of causes. For people living with amyotrophic lateral sclerosis (ALS), however, the progressive loss of cortical motor neurons could theoretically pose a challenge to the stability of BCI performance. Here we tested the stability of gesture decoding with a chronic electrocorticographic (ECoG) BCI in a man living with ALS and participating in a clinical trial (ClinicalTrials.gov, NCT03567213). We evaluated offline decoding performance of attempted gestures over two periods: a 5-week period beginning roughly 2 years post-implant and a 6-week period ending roughly 5 months later. Decoder sensitivity was high in both periods (90 - 98%), while classification accuracy was 37 - 68% in the first period and worsened to 23 - 39% in the second. We investigated multiple frequency bands that were used as model features in both periods, and we observed reductions in high gamma band power (70 - 110 Hz) and between-class separation during the second period compared to the first. Over the 5-month period motor function did not appreciably decline. These results, albeit preliminary, suggest that declines in the neural population responses that drive ECoG BCI performance can occur without overt signs of disease progression in people living with ALS, and could serve as a biomarker for disease progression in the future.

The logopenic variant of PPA (lvPPA) is characterized by impaired single-word retrieval and impaired repetition, which suggests a phonological working memory impairment. Phonological working memory is an essential mechanism for verbal learning; thus, one would expect that the verbal learning skills of this population would be thoroughly investigated. Nonetheless, the relevant research is scarce. As a preliminary investigation aiming to deepen our understanding of verbal learning in lvPPA, we studied how resting-state electroencephalographic (EEG) activity relates to the verbal learning abilities in this group. Specifically, short low-density (i.e., 8 channels, bilaterally) EEG recordings were collected from nine lvPPA individuals at resting-state. Activity at the five frequency bands (delta, theta, alpha, beta, gamma) was extracted and correlated with Sum of Trials performance on the Rey Auditory Verbal Learning Test (RAVLT). Our results showed a statistically significant association between delta band activity and verbal learning across the brain, particularly in the left parietal region. Specifically, higher delta power (on average across channels, as well as at each of the eight channels separately) was associated with lower RAVLT scores. EEG activity at the other frequency bands did not show a statistically significant association with verbal learning. These findings provide the first insights into the association of resting-state electrophysiological activity with verbal learning in lvPPA. The findings of this preliminary investigation can be used as guiding evidence in future neuromodulation studies to target activity at specific frequency bands for electrical stimulation both in lvPPA, as well as in other populations with similar learning impairments.

The clinical success of brain computer interfaces (BCI) depends on overcoming both biological and material challenges to ensure a long-term stable connection for neural recording and stimulation. This study systematically quantified damage that microelectrodes sustained during chronical implantation in three people with tetraplegia for 956–2130 days. Using scanning electron microscopy (SEM), we imaged 980 microelectrodes from eleven Neuroport arrays tipped with platinum (Pt, n = 8) and sputtered iridium oxide film (SIROF, n = 3). Arrays were implanted/explanted from posterior parietal, motor and somatosensory cortices across three clinical sites (Caltech/UCLA, Caltech/USC, APL/Johns Hopkins). From the electron micrographs, we quantified and correlated physical damage with functional outcomes measured in vivo, prior to explant (recording quality, noise, impedance and stimulation ability).Despite greater physical degradation, SIROF electrodes were twice as likely to record neural activity than Pt (measured by SNR). For SIROF, 1 kHz impedance significantly correlated with all physical damage metrics, recording metrics, and stimulation performance, suggesting a reliable measurement of in vivo degradation. We observed a new degradation type, primarily on stimulated electrodes (“pockmarked” vs “cracked”) electrodes; however, no significant degradation due to stimulation or amount of charge delivered. We hypothesize erosion of the silicon shank accelerates damage to the electrode / tissue interface, following damage to the tip metal.These findings link quantitative measurements to the microelectrodes’ physical condition and their capacity to record/stimulate. These data could lead to improved manufacturing processes or novel electrode designs to improve long-term performance of BCIs, making them vitally important as multi-year clinical trials of BCIs are becoming more common. Statement of significance Long-term performance stability of the electrode-tissue interface is essential for clinical viability of brain computer interface (BCI) devices; currently, materials degradation is a critical component for performance loss. Across three human participants, ten micro-electrode arrays (plus one control) were implanted for 956–2130 days. Using scanning electron microscopy (SEM), we analyzed degradation of 980 electrodes, comparing two types of commonly implanted electrode tip metals: Platinum (Pt) and Sputtered Iridium Oxide Film (SIROF). We correlated observed degradation with in vivo electrode performance: recording (signal-to-noise ratio, noise, impedance) and stimulation (evoked somatosensory percepts). We hypothesize penetration of the electrode tip by biotic processes leads to erosion of the supporting silicon core, which then accelerates further tip metal damage. These data could lead to improved manufacturing processes or novel electrode designs towards the goal of a stable BCI electrical interface, spanning a multi-decade participant lifetime.

Brain-computer interfaces can potentially restore autonomy to people with paralysis, including the ability to control their own environment via smart devices in the home. BCI applications for controlling smart devices to date have required visual displays or auditory cues, limiting the ability of users to autonomously and privately issue commands. In this study, a clinical trial participant with amyotrophic lateral sclerosis (ALS) used a chronically implanted electrocorticographic (ECoG) BCI to control smart devices with self-paced silent speech commands. Across 18 experimental sessions, silently mimed speech commands were detected in real time and decoded with a median accuracy of 97.1% (chance: 7.14%). These results demonstrate that silently attempted speech can be reliably decoded without exogenous timing cues, supporting the feasibility of reliable autonomous smart device control with an implantable BCI. ### Competing Interest Statement The authors have declared no competing interest. ### Clinical Trial NCT03567213 ### Funding Statement Research reported in this publication was supported by the National Institute Of Neurological Disorders And Stroke of the National Institutes of Health under Award Number UH3NS114439 (PI N.E.C., co-PI N.F.R.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Institutional Review Board (IRB) of the Johns Hopkins Medicine gave ethical approval for this work and the Food and Drug Administration (FDA) gave approval under an investigational device exemption (IDE) I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Performance data used to support the findings are available in paper or supplementary materials. Processed neural data will be made available upon manuscript acceptance. Raw neural recordings are available from the corresponding author upon reasonable request. They are not publicly available as they contain information that might compromise participant privacy.

The clinical success of brain computer interfaces (BCI) depends on overcoming both biological and material challenges to ensure a long-term stable connection for neural recording and stimulation. This study systematically quantified damage that microelectrodes sustained during chronical implantation in three people with tetraplegia for 956-2130 days. Using scanning electron microscopy (SEM), we imaged 980 microelectrodes from eleven Neuroport arrays tipped with platinum (Pt, n = 8) and sputtered iridium oxide film (SIROF, n = 3). Arrays were implanted/explanted from posterior parietal, motor and somatosensory cortices across three clinical sites (Caltech/UCLA, Caltech/USC, APL/Johns Hopkins). From the electron micrographs, we quantified and correlated physical damage with functional outcomes measured in vivo, prior to explant (recording quality, noise, impedance and stimulation ability). Despite greater physical degradation, SIROF electrodes were twice as likely to record neural activity than Pt (measured by SNR). For SIROF, 1 kHz impedance significantly correlated with all physical damage metrics, recording metrics, and stimulation performance, suggesting a reliable measurement of in vivo degradation. We observed a new degradation type, primarily on stimulated electrodes ("pockmarked" vs "cracked") electrodes; however, no significant degradation due to stimulation or amount of charge delivered. We hypothesize erosion of the silicon shank accelerates damage to the electrode / tissue interface, following damage to the tip metal. These findings link quantitative measurements to the microelectrodes' physical condition and their capacity to record/stimulate. These data could lead to improved manufacturing processes or novel electrode designs to improve long-term performance of BCIs, making them vitally important as multi-year clinical trials of BCIs are becoming more common. Statement of significance: Long-term performance stability of the electrode-tissue interface is essential for clinical viability of brain computer interface (BCI) devices; currently, materials degradation is a critical component for performance loss. Across three human participants, ten micro-electrode arrays (plus one control) were implanted for 956-2130 days. Using scanning electron microscopy (SEM), we analyzed degradation of 980 electrodes, comparing two types of commonly implanted electrode tip metals: Platinum (Pt) and Sputtered Iridium Oxide Film (SIROF). We correlated observed degradation with in vivo electrode performance: recording (signal-to-noise ratio, noise, impedance) and stimulation (evoked somatosensory percepts). We hypothesize penetration of the electrode tip by biotic processes leads to erosion of the supporting silicon core, which then accelerates further tip metal damage. These data could lead to improved manufacturing processes or novel electrode designs towards the goal of a stable BCI electrical interface, spanning a multi-decade participant lifetime.

OBJECTIVE:Intracranial language localization with electrical stimulation mapping (ESM) and high-gamma modulation (HGM) mapping relies on artificial, repetitive tasks, requiring sustained cooperation from patients. Herein, we tested the validity of unstructured, interpersonal, naturalistic conversation for language localization, using a novel methodology: Behavior-iEEG-Spectral-Power correlation (BESPoC). We first validated BESPoC against ESM, HGM, and neuropsychological outcomes using well-established language tasks, then demonstrated the validity of naturalistic conversation. METHODS:We included 134 patients (59 females), aged 2-29 years, undergoing standard-of-care stereo-electroencephalography monitoring who engaged in picture naming, auditory naming, story listening, and conversed with a family member. ESM and HGM analysis were performed using established methods. BESPoC methodology quantified correlation between stereo-electroencephalography spectral power from task recordings, obviating the need for any trial-based epochs, and behavioral markers. The large sample size allowed mixed-effects modeling to compare BESPoC with HGM and ESM. RESULTS:BESPoC showed high specificity (0.79-0.83) and sensitivity (0.64-0.86) for localizing HGM language sites across the tasks. BESPoC also compared well with HGM across all language tasks for localizing ESM speech/language sites. With conventional tasks, BESPoC was superior to HGM for modeling neuropsychological deficits seen, despite preserving ESM speech/language sites. Naturalistic conversation compared well with standard tasks for localization of HGM and ESM language sites, and determined neuropsychological outcomes better than conventional tasks. INTERPRETATION:Using BESPoC methodology naturalistic conversation is shown to produce valid cortical language maps of both expressive and receptive language, and determine neuropsychological outcomes after epilepsy surgery. ANN NEUROL 2025;98:1096-1110.

Accurately time-aligned spectral targets are essential for training electrocorticographic (ECoG) brain-computer interfaces (BCIs) intended for real-time speech output. This alignment is particularly challenging with "silent speech," in which speech occurs without phonation, or in the extreme, without articulation. Crafting suitably precise targets for silent speech is complex and error-prone due to multiple sources of temporal imprecision. We investigated how these temporal inaccuracies impact deep neural network performance in synthesizing speech from a BCI clinical trial participant who retained some speech capability. By simulating silent speech conditions through distortions in known acoustic target timings, we observed significant performance degradations at both phonetic and syllabic timescales. Accuracy-based measures offered a more reliable assessment of intelligibility than traditional metrics like the short-time objective intelligibility index, which may overestimate performance in low-precision contexts. These results underscore the need for advanced alignment techniques with precise phonetic and syllabic guarantees for silent speech BCIs focused on providing immediate output.

Objective. Brain-computer interfaces hold significant promise for restoring communication in individuals with partial or complete loss of the ability to speak due to paralysis from amyotrophic lateral sclerosis (ALS), brainstem stroke, and other neurological disorders. Many of the approaches to speech decoding reported in the BCI literature have required time-aligned target representations to allow successful training-a major challenge when translating such approaches to people who have already lost their voice.Approach. In this pilot study, we made a first step toward scenarios in which no ground truth is available. We utilized a graph-based clustering approach to identify temporal segments of speech production from electrocorticographic (ECoG) signals alone. We then used the estimated speech segments to train a voice activity detection (VAD) model using only ECoG signals. We evaluated our approach using a leave-one-day-out cross-validation on open-loop recordings of a single dysarthric clinical trial participant living with ALS, and we compared the resulting performance to previous solutions trained with ground truth acoustic voice recordings.Main results. Our approach achieves a median timing error of around 530 ms with respect to the actual spoken speech. Embedded into a real-time BCI, our approach is capable of providing VAD results with a latency of only 10 ms.Significance. To the best of our knowledge, our results show for the first time that speech activity can be predicted purely from unlabeled ECoG signals, a crucial step toward individuals who cannot provide this information anymore due to their neurological condition, such as patients with locked-in syndrome.Clinical Trial Information. ClinicalTrials.gov, registration number NCT03567213.

Recent studies have demonstrated that speech can be decoded from brain activity which in turn can be used for brain-computer interface (BCI)-based communication. It is however also known that the area often used as a signal source for speech decoding BCIs, the sensorimotor cortex (SMC), is also engaged when people perceive speech, thus making speech perception a potential source of false positive activation of the BCI. The current study investigated if and how speech perception may interfere with reliable speech BCI control. We recorded high-density electrocorticography (HD-ECoG) data from five subjects while they performed a speech perception and a speech production task. We first evaluated whether speech perception and production activated the SMC. Second, we trained a support-vector machine (SVM) on the speech production data (including rest). To test the occurrence of false positives, this decoder was then tested on speech perception data where every perception segment that was classified as a produced syllable rather than rest was considered a false positive. Finally, we investigated whether perceived speech could be distinguished from produced speech and rest. Our results show that both the perception and production of speech activate the SMC. In addition, we found that decoders that are highly reliable at detecting self-produced syllables from brain signals may generate false positive BCI activations during the perception of speech and that it is possible to distinguish perceived speech from produced speech and rest, with high accuracy. We conclude that speech perception can interfere with reliable BCI control, and that efforts to limit the occurrence of false positives during daily-life BCI use should be implemented in BCI design to increase the likelihood of successful adoptation by end users.

Brain-computer Interface (BCI) research has recently reached the stage where it has become feasible and prudent to perform long-term implantations and studies of efficacy in patients. One such clinical trial implanted a patient suffering from progressive amyotrophic lateral sclerosis with electrocorticographic arrays over the sensorimotor cortex for approximately 2.5 years, during which the patient performed a variety of speech and hand motor tasks in a controlled laboratory setting. The present analysis utilizes this longitudinal data to train interpretable convolutional neural networks using EEGNet to compare the decoding performance and resulting data-driven filters across hand motor, speech, and combined representations. These results aim to elucidate the common neural activations and substrates across speech and grasp tasks, and provide new insights into the spatial and spectral characteristics of the relevant neural features for the decoding tasks.