Journal of Neural Engineering

ObjectiveWe developed and validated a detection-guided artifact removal framework for clinical electroencephalography (EEG). It corrects only the contaminated segments and preserves artifact-free data. ApproachThe framework employs convolutional neural network (CNN) detectors trained on the Temple University Hospital (TUH) Artifact Corpus of 150 recordings from 105 patients. For eye movement artifacts (20 second windows), it uses independent component analysis (ICA) and canonical correlation an...

Restoring communication for people with dysarthria secondary to pontine stroke remains a critical challenge. Intracortical brain-computer interfaces (iBCIs) have demonstrated great potential for speech restoration in people with amyotrophic lateral sclerosis (ALS), with 1-24% word error rates (WERs) on a 125,000-word vocabulary. In pontine stroke, electrocorticography (ECoG) BCIs achieved 25.5% WERs with a smaller 1,024-word vocabulary. Whether intracortical BCI performance improvements extend t...

Sensory organization at the spinal segment level is commonly inferred from dermatomal maps that assume a fixed correspondence between cutaneous regions and spinal segments. However, based on the complexities of spinal neuroanatomy and neurophysiology, the distribution of sensory signals within the cord may be broader and less segment-specific than dermatomal maps suggest, leaving the segment-level localization of sensory-evoked activity in humans uncertain. Spinal cord functional magnetic resona...

The neural signature of rhythm and tempo remains difficult to study in both humans and non-human primates. Here we recorded from the motor cortex of human participants implanted with intracortical microelectrode arrays while they performed a series of rhythmic tapping tasks. We found that rhythmic tapping elicited low-dimensional rotational neural dynamics whose radii varied in a tempo-dependent manner and axes related to kinematic properties. Moreover, we observed a spectrum of kinematic and ne...

There are many alternative methods to joystick control for control of Electric Powered Wheelchairs for users with neuromuscular disabilities, such as muscular dystrophy, and spinal cord injuries, such as tetraplegia. However, these methods- which include the sip-and-puff method, head and neck movement, blinking, or tongue movement- hinder social interaction, and are therefore detrimental to user independence. In recent years, research has explored the use of Electromyography (EMG) signals from a...

Sleep arousals trigger rapid autonomic shifts, yet their specific sympathetic signatures remain poorly characterized due to the mixed sympathetic-parasympathetic nature of traditional cardiovascular markers. Electrodermal activity (EDA), driven exclusively by sympathetic sudomotor pathways, offers a more direct opportunity to characterize arousal-related autonomic responses during sleep. This study quantifies the evolution of EDA-based features associated with arousal events in 100 adults using ...

BackgroundOscillations underpin a large spectrum of brain function. Brain oscillations are altered by neuromodulation approaches including deep brain stimulation (DBS), but a mechanistic understanding of the brain oscillation - DBS interaction is missing. DBS is predominantly used in the treatment of Parkinsons disease. DBS can induce or alter pre-existing narrow frequency band gamma oscillations at half the stimulation frequency. Such half-harmonic responses have been interpreted as entrainmen...

BackgroundMotor threshold (MT) estimation is fundamental to transcranial magnetic stimulation (TMS), guiding individualized stimulation intensity in research and therapy. Conventional methods such as the 5-out-of-10 rule require many stimuli, while adaptive approaches like Parameter Estimation by Sequential Testing (PEST) improve efficiency but can exhibit poor convergence under certain conditions. ObjectiveThis study introduces the Bayesian Uncertainty Dynamic Algorithm for Parameter Estimatio...

Annotating seizure onset and spread in intracranial EEG is essential for epilepsy surgical planning, yet manual annotation is unreliable and cannot scale to large datasets. We introduce Neural Dynamic Divergence (NDD), an unsupervised framework that detects seizure activity by measuring deviation from patient-specific baseline neural dynamics using autoregressive models. NDD requires no labeled training data and adapts to individual patients, channels, and brain states. Validating against expert...

IntroductionSleep spindles are electroencephalographic elements characteristic of non-rapid eye movement sleep generated by thalamo-cortical interactions. Spindles have been linked to some of the cognitive benefits afforded by sleep and high spindle activity is associated with increased arousal threshold (deeper sleep). Here, we demonstrate that targeting the thalamus with Transcranial Electrical Stimulation with Temporal Interference (TES-TI) can enhance spindle activity. Methods24 participant...

Executive dysfunction affects nearly 50% of individuals with traumatic brain injuries (TBI), yet interventions targeting the underlying neural mechanisms remain limited. This study examined whether aerobic exercise modulates functional connectivity to improve executive function in individuals with mild TBI and identified the neural pathways mediating these improvements. In this secondary analysis of a 12-week pilot randomized controlled trial, participants with mild TBI (n=24) were randomized to...

Gait impairment (GI) and freezing of gait (FOG) affect 80% of patients with advanced Parkinsons disease. Continuous deep brain stimulation (cDBS) provides limited adaptability to address the episodic nature of FOG due to fixed parameters. Neural biomarkers for adaptive DBS are limited by signal artifacts and poor FOG classification. Wearable inertial measurement units (IMUs) offer a promising alternative by directly measuring signatures of GI&FOG. We developed Kinematic adaptive DBS (KaDBS), the...

INTRODUCTIONConnected speech analyses can help characterize linguistic impairments in primary progressive aphasia (PPA) and classify variants, however, manual transcription of speech samples is time-consuming and expensive. Automated speech recognition (ASR) may be efficacious for transcribing PPA speech. METHODSTranscripts of picture descriptions (109 PPA, 32 healthy controls (HC)) were generated using a manual, automated (Whisper) or semi-automated approach including a quality control (QC) st...

BackgroundArtificial Intelligence (AI) based approaches to speech analysis have the potential to assist with objective speech error analysis in aphasia but off-the shelf tools often fail to detect speech errors due to prioritizing "fluent transcription." Speech production errors (dysfluencies) are hallmark diagnostic features of the nonfluent (nfvPPA) and logopenic (lvPPA) variants of primary progressive aphasia, yet they can be challenging to detect and characterize even by expert clinicians. T...

BackgroundConventional evaluations of digital health interventions typically assess mean treatment effects, potentially masking heterogeneous impacts across the functional recovery distribution. Patients at the lower and upper tails of recovery trajectories may respond differently to AI-enhanced telerehabilitation, yet standard regression approaches cannot capture these distributional nuances. ObjectiveThis study applied Recentered Influence Function (RIF) quantile regression with Oaxaca-Blinde...

About half of patients who undergo epilepsy surgery for drug-resistant epilepsy have seizure recurrence, supporting the need for approaches that more accurately identify the epileptogenic zone, defined as the brain areas whose removal causes cessation of seizures. Altered network connectivity has emerged as a candidate biomarker of the epileptogenic zone, but how connectivity is altered in the epileptogenic zone remains uncertain, with prior studies reporting inconsistent results. We hypothesize...

ImportanceTracking and predicting seizure frequency in patients with epilepsy is important for prognostication and therapy management. Interictal spikes have been proposed as a biomarker of seizure burden, but their association with seizure frequency has not been well quantified across epilepsy subtypes. ObjectiveTo measure the association between spike rate and seizure frequency and how this varies by epilepsy subtype. Design, Setting and ParticipantsWe studied 3,614 consecutive routine outpa...

Visual Snow Syndrome (VSS) is a neurological condition characterized by continuous visual disturbances resembling television static across the visual field. Despite its significant impact on quality of life, objective assessment methods remain limited, with diagnosis relying primarily on subjective patient reports. Current understanding of VSS pathophysiology suggests cortical hyperexcitability, but precise mechanisms remain unclear. Here we developed an integrated protocol combining transcrania...

ObjectiveQuantitative assessment of extent of tissue resection following epilepsy surgery requires accurate delineation of the resection cavity on postoperative MRI. Current methods for resection cavity masking are time-consuming and labour-intensive, while existing automated approaches exhibit variable segmentation accuracy, particularly on extra-temporal resections. We developed MELD-PostOp, a deep learning tool trained and evaluated on a large, international, heterogeneous cohort to automatic...

Effective connectivity of the human insula, mainly assessed at rest using cortico-cortical evoked potentials (CCEPs), is not yet fully characterized at high-resolution. Here, we significantly extend prior CCEP studies of the insula by leveraging an extensive multicenter CCEP database and fine-grained anatomical atlases of the insula. We analyzed CCEP datasets from 897 patients with refractory focal epilepsy (459 females, age: 26{+/-}14 years) explored by stereo electroencephalography and with a...