Stability of ECoG high gamma signals during speech and implications for a speech BCI system in an individual with ALS: a year-long longitudinal study.

OBJECTIVE: Speech brain-computer interfaces (BCIs) have the potential to augment communication in individuals with impaired speech due to muscle weakness, for example in ALS and other neurological disorders. However, to achieve long-term, reliable use of a speech BCI, it is essential for speech-related neural signal changes to be stable over long periods of time. Here we study, for the first time, the stability of speech-related electrocorticographic (ECoG) signals recorded from a chronically implanted ECoG BCI over a 12 month period. APPROACH: ECoG signals were recorded by an ECoG array implanted over the ventral sensorimotor cortex (vSMC) in a clinical trial participant with ALS. Because ECoG-based speech decoding has most often relied on broadband high gamma signal changes relative to baseline (non-speech) conditions, we studied longitudinal changes of high gamma band (HG) power at baseline and during speech, and we compared these with residual high frequency (HF) noise levels at baseline. Stability was further assessed by longitudinal measurements of signal-to-noise ratio (SNR), activation ratio (ActR), and peak speech-related HG response magnitude (HG response peaks). Lastly, we analyzed the stability of the event-related HG power changes (HG responses) for individual syllables at each electrode. MAIN RESULTS: We found that speech-related ECoG signal responses were stable over a range of syllables activating different articulators for the first year after implantation. SIGNIFICANCE: Together, our results indicate that ECoG can be a stable recording modality for long-term speech BCI systems for those living with severe paralysis. CLINICALTRIALS: gov, registration number NCT03567213.

Online speech synthesis using a chronically implanted brain-computer interface in an individual with ALS.

Brain-computer interfaces (BCIs) that reconstruct and synthesize speech using brain activity recorded with intracranial electrodes may pave the way toward novel communication interfaces for people who have lost their ability to speak, or who are at high risk of losing this ability, due to neurological disorders. Here, we report online synthesis of intelligible words using a chronically implanted brain-computer interface (BCI) in a man with impaired articulation due to ALS, participating in a clinical trial (ClinicalTrials.gov, NCT03567213) exploring different strategies for BCI communication. The 3-stage approach reported here relies on recurrent neural networks to identify, decode and synthesize speech from electrocorticographic (ECoG) signals acquired across motor, premotor and somatosensory cortices. We demonstrate a reliable BCI that synthesizes commands freely chosen and spoken by the participant from a vocabulary of 6 keywords previously used for decoding commands to control a communication board. Evaluation of the intelligibility of the synthesized speech indicates that 80% of the words can be correctly recognized by human listeners. Our results show that a speech-impaired individual with ALS can use a chronically implanted BCI to reliably produce synthesized words while preserving the participant's voice profile, and provide further evidence for the stability of ECoG for speech-based BCIs.

Stable Decoding from a Speech BCI Enables Control for an Individual with ALS without Recalibration for 3 Months.

Brain-computer interfaces (BCIs) can be used to control assistive devices by patients with neurological disorders like amyotrophic lateral sclerosis (ALS) that limit speech and movement. For assistive control, it is desirable for BCI systems to be accurate and reliable, preferably with minimal setup time. In this study, a participant with severe dysarthria due to ALS operates computer applications with six intuitive speech commands via a chronic electrocorticographic (ECoG) implant over the ventral sensorimotor cortex. Speech commands are accurately detected and decoded (median accuracy: 90.59%) throughout a 3-month study period without model retraining or recalibration. Use of the BCI does not require exogenous timing cues, enabling the participant to issue self-paced commands at will. These results demonstrate that a chronically implanted ECoG-based speech BCI can reliably control assistive devices over long time periods with only initial model training and calibration, supporting the feasibility of unassisted home use.

Online speech synthesis using a chronically implanted brain-computer interface in an individual with ALS.

Recent studies have shown that speech can be reconstructed and synthesized using only brain activity recorded with intracranial electrodes, but until now this has only been done using retrospective analyses of recordings from able-bodied patients temporarily implanted with electrodes for epilepsy surgery. Here, we report online synthesis of intelligible words using a chronically implanted brain-computer interface (BCI) in a clinical trial participant (ClinicalTrials.gov, NCT03567213) with dysarthria due to amyotrophic lateral sclerosis (ALS). We demonstrate a reliable BCI that synthesizes commands freely chosen and spoken by the user from a vocabulary of 6 keywords originally designed to allow intuitive selection of items on a communication board. Our results show for the first time that a speech-impaired individual with ALS can use a chronically implanted BCI to reliably produce synthesized words that are intelligible to human listeners while preserving the participants voice profile.

Evoking natural thermal perceptions using a thin-film thermoelectric device with high cooling power density and speed.

Multimodal sensory feedback from upper-limb prostheses can increase their function and usability. Here we show that intuitive thermal perceptions during cold-object grasping with a prosthesis can be restored in a phantom hand through targeted nerve stimulation via a wearable thin-film thermoelectric device with high cooling power density and speed. We found that specific regions of the residual limb, when thermally stimulated, elicited thermal sensations in the phantom hand that remained stable beyond 48 weeks. We also found stimulation sites that selectively elicited sensations of temperature, touch or both, depending on whether the stimulation was thermal or mechanical. In closed-loop functional tasks involving the identification of cold objects by amputees and by non-amputee participants, and compared with traditional bulk thermoelectric devices, the wearable thin-film device reliably elicited cooling sensations that were up to 8 times faster and up to 3 times greater in intensity while using half the energy and 1/600th the mass of active thermoelectric material. Wearable thin-film thermoelectric devices may allow for the non-invasive restoration of thermal perceptions during touch.

Shared Control of Bimanual Robotic Limbs With a Brain-Machine Interface for Self-Feeding.

Advances in intelligent robotic systems and brain-machine interfaces (BMI) have helped restore functionality and independence to individuals living with sensorimotor deficits; however, tasks requiring bimanual coordination and fine manipulation continue to remain unsolved given the technical complexity of controlling multiple degrees of freedom (DOF) across multiple limbs in a coordinated way through a user input. To address this challenge, we implemented a collaborative shared control strategy to manipulate and coordinate two Modular Prosthetic Limbs (MPL) for performing a bimanual self-feeding task. A human participant with microelectrode arrays in sensorimotor brain regions provided commands to both MPLs to perform the self-feeding task, which included bimanual cutting. Motor commands were decoded from bilateral neural signals to control up to two DOFs on each MPL at a time. The shared control strategy enabled the participant to map his four-DOF control inputs, two per hand, to as many as 12 DOFs for specifying robot end effector position and orientation. Using neurally-driven shared control, the participant successfully and simultaneously controlled movements of both robotic limbs to cut and eat food in a complex bimanual self-feeding task. This demonstration of bimanual robotic system control via a BMI in collaboration with intelligent robot behavior has major implications for restoring complex movement behaviors for those living with sensorimotor deficits.

Perceived timing of cutaneous vibration and intracortical microstimulation of human somatosensory cortex.

BACKGROUND: Intracortical microstimulation (ICMS) of somatosensory cortex can partially restore the sense of touch. Though ICMS bypasses much of the neuraxis, prior studies have found that conscious detection of touch elicited by ICMS lags behind the detection of cutaneous vibration. These findings may have been influenced by mismatched stimulus intensities, which can impact temporal perception. OBJECTIVE: Evaluate the relative latency at which intensity-matched vibration and ICMS are perceived by a human participant. METHODS: One person implanted with microelectrode arrays in somatosensory cortex performed reaction time and temporal order judgment (TOJ) tasks. To measure reaction time, the participant reported when he perceived vibration or ICMS. In the TOJ task, vibration and ICMS were sequentially presented and the participant reported which stimulus occurred first. To verify that the participant could distinguish between stimuli, he also performed a modality discrimination task, in which he indicated if he felt vibration, ICMS, or both. RESULTS: When vibration was matched in perceived intensity to high-amplitude ICMS, vibration was perceived, on average, 48 ms faster than ICMS. However, in the TOJ task, both sensations arose at comparable latencies, with points of subjective simultaneity not significantly different from zero. The participant could discriminate between tactile modalities above chance level but was more inclined to report feeling vibration than ICMS. CONCLUSIONS: The latencies of ICMS-evoked percepts are slower than their mechanical counterparts. However, differences in latencies are small, particularly when stimuli are matched for intensity, implying that ICMS-based somatosensory feedback is rapid enough to be effective in neuroprosthetic applications.

Characteristics and stability of sensorimotor activity driven by isolated-muscle group activation in a human with tetraplegia.

Understanding the cortical representations of movements and their stability can shed light on improved brain-machine interface (BMI) approaches to decode these representations without frequent recalibration. Here, we characterize the spatial organization (somatotopy) and stability of the bilateral sensorimotor map of forearm muscles in an incomplete-high spinal-cord injury study participant implanted bilaterally in the primary motor and sensory cortices with Utah microelectrode arrays (MEAs). We built representation maps by recording bilateral multiunit activity (MUA) and surface electromyography (EMG) as the participant executed voluntary contractions of the extensor carpi radialis (ECR), and attempted motions in the flexor carpi radialis (FCR), which was paralytic. To assess stability, we repeatedly mapped and compared left- and right-wrist-extensor-related activity throughout several sessions, comparing somatotopy of active electrodes, as well as neural signals both at the within-electrode (multiunit) and cross-electrode (network) levels. Wrist motions showed significant activation in motor and sensory cortical electrodes. Within electrodes, firing strength stability diminished as the time increased between consecutive measurements (hours within a session, or days across sessions), with higher stability observed in sensory cortex than in motor, and in the contralateral hemisphere than in the ipsilateral. However, we observed no differences at network level, and no evidence of decoding instabilities for wrist EMG, either across timespans of hours or days, or across recording area. While map stability differs between brain area and hemisphere at multiunit/electrode level, these differences are nullified at ensemble level.

Monitoring at-home prosthesis control improvements through real-time data logging.

Objective.Validating the ability for advanced prostheses to improve function beyond the laboratory remains a critical step in enabling long-term benefits for prosthetic limb users.Approach.A nine week take-home case study was completed with a single participant with upper limb amputation and osseointegration to better understand how an advanced prosthesis is used during daily activities. The participant was already an expert prosthesis user and used the Modular Prosthetic Limb (MPL) at home during the study. The MPL was controlled using wireless electromyography (EMG) pattern recognition-based movement decoding. Clinical assessments were performed before and after the take-home portion of the study. Data was recorded using an onboard data log in order to measure daily prosthesis usage, sensor data, and EMG data.Main results.The participant's continuous prosthesis usage steadily increased (p= 0.04, max = 5.5 h) over time and over 30% of the total time was spent actively controlling the prosthesis. The duration of prosthesis usage after each pattern recognition training session also increased over time (p= 0.04), resulting in up to 5.4 h of usage before retraining the movement decoding algorithm. Pattern recognition control accuracy improved (1.2% per week,p< 0.001) with a maximum number of ten classes trained at once and the transitions between different degrees of freedom increased as the study progressed, indicating smooth and efficient control of the advanced prosthesis. Variability of decoding accuracy also decreased with prosthesis usage (p< 0.001) and 30% of the time was spent performing a prosthesis movement. During clinical evaluations, Box and Blocks and the Assessment of the Capacity for Myoelectric Control scores increased by 43% and 6.2%, respectively, demonstrating prosthesis functionality and the NASA Task Load Index scores decreased, on average, by 25% across assessments, indicating reduced cognitive workload while using the MPL, over the nine week study.Significance. In this case study, we demonstrate that an onboard system to monitor prosthesis usage enables better understanding of how prostheses are incorporated into daily life. That knowledge can support the long-term goal of completely restoring independence and quality of life to individuals living with upper limb amputation.

Intracortical Somatosensory Stimulation to Elicit Fingertip Sensations in an Individual With Spinal Cord Injury.

BACKGROUND AND OBJECTIVES: The restoration of touch to fingers and fingertips is critical to achieving dexterous neuroprosthetic control for individuals with sensorimotor dysfunction. However, localized fingertip sensations have not been evoked via intracortical microstimulation (ICMS). METHODS: Using a novel intraoperative mapping approach, we implanted electrode arrays in the finger areas of left and right somatosensory cortex and delivered ICMS over a 2-year period in a human participant with spinal cord injury. RESULTS: Stimulation evoked tactile sensations in 8 fingers, including fingertips, spanning both hands. Evoked percepts followed expected somatotopic arrangements. The subject was able to reliably identify up to 7 finger-specific sites spanning both hands in a finger discrimination task. The size of the evoked percepts was on average 33% larger than a finger pad, as assessed via manual markings of a hand image. The size of the evoked percepts increased modestly with increased stimulation intensity, growing 21% as pulse amplitude increased from 20 to 80 µA. Detection thresholds were estimated on a subset of electrodes, with estimates of 9.2 to 35 µA observed, roughly consistent with prior studies. DISCUSSION: These results suggest that ICMS can enable the delivery of consistent and localized fingertip sensations during object manipulation by neuroprostheses for individuals with somatosensory deficits. CLINICALTRIALSGOV IDENTIFIER: NCT03161067.

Intracortical microstimulation of somatosensory cortex enables object identification through perceived sensations.

Advances in brain-machine interfaces have helped restore function and independence for individuals with sensorimotor deficits; however, providing efficient and effective sensory feedback remains challenging. Intracortical microstimulation (ICMS) of sensorimotor brain regions is a promising technique for providing bioinspired sensory feedback. In a human participant with chronically-implanted microelectrode arrays, we provided ICMS to the primary somatosensory cortex to generate tactile percepts in his hand. In a 3-choice object identification task, the participant identified virtual objects using tactile sensory feedback and no visual information. We evaluated three different stimulation paradigms, each with a different weighting of the grip force and its derivative, to explore the potential benefits of a more bioinspired stimulation strategy. In all paradigms, the participant's ability to identify the objects was above-chance, with object identification accuracy reaching 80% correct when using only sustained grip force feedback and 76.7% when using equal weighting of both sustained grip force and its derivative. These results demonstrate that bioinspired ICMS can provide sensory feedback that is functionally beneficial in sensorimotor tasks. Designing more efficient stimulation paradigms is important because it will allow us to 1) provide safer stimulation delivery methods that reduce overall injected charge without sacrificing function and 2) more effectively transmit sensory information to promote intuitive integration and usage by the human body.

Novel intraoperative online functional mapping of somatosensory finger representations for targeted stimulating electrode placement: technical note.

Defining eloquent cortex intraoperatively, traditionally performed by neurosurgeons to preserve patient function, can now help target electrode implantation for restoring function. Brain-machine interfaces (BMIs) have the potential to restore upper-limb motor control to paralyzed patients but require accurate placement of recording and stimulating electrodes to enable functional control of a prosthetic limb. Beyond motor decoding from recording arrays, precise placement of stimulating electrodes in cortical areas associated with finger and fingertip sensations allows for the delivery of sensory feedback that could improve dexterous control of prosthetic hands. In this study, the authors demonstrated the use of a novel intraoperative online functional mapping (OFM) technique with high-density electrocorticography to localize finger representations in human primary somatosensory cortex. In conjunction with traditional pre- and intraoperative targeting approaches, this technique enabled accurate implantation of stimulating microelectrodes, which was confirmed by postimplantation intracortical stimulation of finger and fingertip sensations. This work demonstrates the utility of intraoperative OFM and will inform future studies of closed-loop BMIs in humans.

Extended home use of an advanced osseointegrated prosthetic arm improves function, performance, and control efficiency.

Objective.Full restoration of arm function using a prosthesis remains a grand challenge; however, advances in robotic hardware, surgical interventions, and machine learning are bringing seamless human-machine interfacing closer to reality.Approach.Through extensive data logging over 1 year, we monitored at-home use of the dexterous Modular Prosthetic Limb controlled through pattern recognition of electromyography (EMG) by an individual with a transhumeral amputation, targeted muscle reinnervation, and osseointegration (OI).Main results.Throughout the study, continuous prosthesis usage increased (1% per week,p< 0.001) and functional metrics improved up to 26% on control assessments and 76% on perceived workload evaluations. We observed increases in torque loading on the OI implant (up to 12.5% every month,p< 0.001) and prosthesis control performance (0.5% every month,p< 0.005), indicating enhanced user integration, acceptance, and proficiency. More importantly, the EMG signal magnitude necessary for prosthesis control decreased, up to 34.7% (p< 0.001), over time without degrading performance, demonstrating improved control efficiency with a machine learning-based myoelectric pattern recognition algorithm. The participant controlled the prosthesis up to one month without updating the pattern recognition algorithm. The participant customized prosthesis movements to perform specific tasks, such as individual finger control for piano playing and hand gestures for communication, which likely contributed to continued usage.Significance.This work demonstrates, in a single participant, the functional benefit of unconstrained use of a highly anthropomorphic prosthetic limb over an extended period. While hurdles remain for widespread use, including device reliability, results replication, and technical maturity beyond a prototype, this study offers insight as an example of the impact of advanced prosthesis technology for rehabilitation outside the laboratory.

Sensory stimulation enhances phantom limb perception and movement decoding.

OBJECTIVE: A major challenge for controlling a prosthetic arm is communication between the device and the user's phantom limb. We show the ability to enhance phantom limb perception and improve movement decoding through targeted transcutaneous electrical nerve stimulation in individuals with an arm amputation. APPROACH: Transcutaneous nerve stimulation experiments were performed with four participants with arm amputation to map phantom limb perception. We measured myoelectric signals during phantom hand movements before and after participants received sensory stimulation. Using electroencephalogram (EEG) monitoring, we measured the neural activity in sensorimotor regions during phantom movements and stimulation. In one participant, we also tracked sensory mapping over 2 years and movement decoding performance over 1 year. MAIN RESULTS: Results show improvements in the participants' ability to perceive and move the phantom hand as a result of sensory stimulation, which leads to improved movement decoding. In the extended study with one participant, we found that sensory mapping remains stable over 2 years. Sensory stimulation improves within-day movement decoding while performance remains stable over 1 year. From the EEG, we observed cortical correlates of sensorimotor integration and increased motor-related neural activity as a result of enhanced phantom limb perception. SIGNIFICANCE: This work demonstrates that phantom limb perception influences prosthesis control and can benefit from targeted nerve stimulation. These findings have implications for improving prosthesis usability and function due to a heightened sense of the phantom hand.

Changes in human brain dynamics during behavioral priming and repetition suppression.

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.

Stable Responsive EMG Sequence Prediction and Adaptive Reinforcement With Temporal Convolutional Networks.

Prediction of movement intentions from electromyographic (EMG) signals is typically performed with a pattern recognition approach, wherein a short dataframe of raw EMG is compressed into an instantaneous feature-encoding that is meaningful for classification. However, EMG signals are time-varying, implying that a frame-wise approach may not sufficiently incorporate temporal context into predictions, leading to erratic and unstable prediction behavior. OBJECTIVE: We demonstrate that sequential prediction models and, specifically, temporal convolutional networks are able to leverage useful temporal information from EMG to achieve superior predictive performance. METHODS: We compare this approach to other sequential and frame-wise models predicting 3 simultaneous hand and wrist degrees-of-freedom from 2 amputee and 13 non-amputee human subjects in a minimally constrained experiment. We also compare these models on the publicly available Ninapro and CapgMyo amputee and non-amputee datasets. RESULTS: Temporal convolutional networks yield predictions that are more accurate and stable than frame-wise models, especially during inter-class transitions, with an average response delay of 4.6 ms and simpler feature-encoding. Their performance can be further improved with adaptive reinforcement training. SIGNIFICANCE: Sequential models that incorporate temporal information from EMG achieve superior movement prediction performance and these models allow for novel types of interactive training. CONCLUSIONS: Addressing EMG decoding as a sequential modeling problem will lead to enhancements in the reliability, responsiveness, and movement complexity available from prosthesis control systems.

Decoding Native Cortical Representations for Flexion and Extension at Upper Limb Joints Using Electrocorticography.

Brain-machine interface (BMI) researchers have traditionally focused on modeling endpoint reaching tasks to provide the control of neurally driven prosthetic arms. Most previous research has focused on achieving an endpoint control through a Cartesian-coordinate-centered approach. However, a joint-centered approach could potentially be used to intuitively control a wide range of limb movements. We systematically investigated the feasibility of discriminating between flexion and extension of different upper limb joints using electrocorticography(ECoG) recordings from sensorimotor cortex. Four subjects implanted with macro-ECoG (10-mm spacing), high-density ECoG (5-mm spacing), and/or micro-ECoG arrays (0.9-mm spacing and 4 mm × 4 mm coverage), performed randomly cued flexions or extensions of the fingers, wrist, or elbow contralateral to the implanted hemisphere. We trained a linear model to classify six movements using averaged high-gamma power (70-110 Hz) modulations at different latencies with respect to movement onset, and within a time interval restricted to flexion or extension at each joint. Offline decoding models for each subject classified these movements with accuracies of 62%-83%. Our results suggest that the widespread ECoG coverage of sensorimotor cortex could allow a whole limb BMI to sample native cortical representations in order to control flexion and extension at multiple joints.

Cortical subnetwork dynamics during human language tasks.

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.

Spatial-temporal functional mapping of language at the bedside with electrocorticography.

OBJECTIVE: To investigate the feasibility and clinical utility of using passive electrocorticography (ECoG) for online spatial-temporal functional mapping (STFM) of language cortex in patients being monitored for epilepsy surgery. METHODS: We developed and tested an online system that exploits ECoG's temporal resolution to display the evolution of statistically significant high gamma (70-110 Hz) responses across all recording sites activated by a discrete cognitive task. We illustrate how this spatial-temporal evolution can be used to study the function of individual recording sites engaged during different language tasks, and how this approach can be particularly useful for mapping eloquent cortex. RESULTS: Using electrocortical stimulation mapping (ESM) as the clinical gold standard for localizing language cortex, the average sensitivity and specificity of online STFM across 7 patients were 69.9% and 83.5%, respectively. Moreover, relative to regions of interest where discrete cortical lesions have most reliably caused language impairments in the literature, the sensitivity of STFM was significantly greater than that of ESM, while its specificity was also greater than that of ESM, though not significantly so. CONCLUSIONS: This study supports the feasibility and clinical utility of online STFM for mapping human language function, particularly under clinical circumstances in which time is limited and comprehensive ESM is impractical.