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...

PurposeVisual function testing in retinal prosthesis users relies on repetitive psychophysical tasks that are cognitively demanding and fatiguing. Gamification may increase engagement, but its effects on perceptual performance in implanted users remain unclear. MethodsThree Argus II users completed circle localization and motion direction discrimination in clinical and gamified versions. Visual stimuli, trial structure, and response requirements were matched within each participant; gamified ve...

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...

Spinal cord injury (SCI) results in profound motor impairment for approximately 20 million people worldwide. Regaining hand use is one of their highest priorities. Interestingly, even severely affected individuals can still generate some level of muscle activity (EMG) in their paralysed muscles when attempting to perform a movement. Here we asked whether participants with no hand function due to a cervical SCI could still use their spared inputs to volitionally control the activity of motor unit...

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...

Stroke can affect sensorimotor control, impairing balance and locomotion. These impairments increase the risk of falls, limit patient independence, and reduce their quality of life. In this study, we investigated how stroke affects the bilateral coordination of soleus motor units during standing, in individuals undergoing subacute rehabilitation. Fourteen participants (n=7 females; time since stroke=19{+/-}8 days; age=60.2{+/-}15.9 years) were recruited after admission for inpatient rehabilitat...

Temporal interference stimulation (TIS) promises deeper and more selective neuromodulation, yet predictions remain sensitive to uncertainties in electrode setup and head modeling. We investigate the impact of coregistration error (CE) of the volume conductor and the head, electrode placement uncertainty (EP), and tissue conductivity uncertainty (CU) on the electric field generated by TIS. The stochastic model aggregates CE, EP, and CU into nineteen random variables and is evaluated for a deep ta...

We previously documented and released a benchmark dataset for machine learning research on sleep stage classification [1]. Subsequently, it was pointed out in a preprint [2] that some recordings in the National Sleep Research Resource [3] include only binary wake-sleep annotations, instead of full sleep stage scoring using the Rechtschaffen and Kales (R&K) [4] or American Academy of Sleep Medicine (AASM) [5] standards. Because wake-sleep labels are an ontological mismatch and not just label nois...

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...

PurposeNearly all amyotrophic lateral sclerosis (ALS) patients develop dysarthria, with many progressing to anarthria and global expressive communication failure despite preserved consciousness. Despite the severity of this communication loss, available augmentative communication technologies remain limited. Brain-computer interface (BCI) technology provides a theoretically compelling approach for decoding speech directly from neural activity. Current BCI technologies are predominantly invasive,...

As implantable brain-computer interfaces (iBCIs) for communication and movement transition from cutting-edge research to clinical practice, a standardized approach will be required to reliably plan neurosurgeries involving complex microelectrode arrays and other neural sensors. Here, through our BrainGate study experiences, we present a replicable methodology, using open-source tools, to create interactive, personalized, 3-dimensional, virtual and physical, functional mapping models to guide iBC...

BackgroundMarkerless motion analysis using deep learning is attracting attention in the field of rehabilitation; however, the three-dimensional measurement accuracy in finger joints, which are prone to self-occlusion, has not been sufficiently validated. This study aimed to validate the accuracy of finger joint angle measurements obtained using a marker-less system based on DeepLabCut (DLC) and Anipose by comparing it with the clinical standard of goniometric measurements. MethodsForty-one heal...

BackgroundCommercially-available microprocessor-controlled prosthetic knees are unable to fully replicate the biomechanical function of the missing biological limb. While powered prostheses have the capacity to restore joint level kinetics, current systems rely on intrinsic control schemes that do not allow the user to volitionally modulate movement under neural commands. This limitation may compromise functional performance and hinder prosthetic embodiment, the sense that the device is part of ...

One-third of the worlds 70 million people with epilepsy have seizures that are not controlled by medication; and implantable devices are an exciting option for treatment. These devices improve seizure control and can detect impending attacks, missed medication, and impaired cognition. Unfortunately, they have no way to share this information with their hosts in real-time - a limitation common to most medical devices. This is a missed opportunity for implants and wearables to learn from patients,...

BackgroundDifferentiating between motor functional dissociative seizures (FDS) and motor epileptic seizures (ES) is a common diagnostic challenge, requiring video electroencephalography (vEEG) as gold standard. However, vEEG requires specialized technicians and clinical experts to set up and interpret and oftentimes fails to capture events. We sought to develop machine-learning (ML) tools to carry out this diagnostic task independently of vEEG or human review by a neurologist. MethodsIn this re...

ObjectiveDetecting epileptic seizures in real-world environments remains challenging, as electroencephalography (EEG) is often impractical in chronic ambulatory monitoring. Heart rate and accelerometry, measurable from wearable devices, provide a less obtrusive alternative. Although some studies explored multimodal wearable-based seizure detection, few have been validated on long-term ambulatory datasets reflecting real-world variability. This study investigated the added value of accelerometry ...

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...

BackgroundThe ability to elicit a rapid, reactive step to recover balance after a postural destabilization is paramount to fall prevention. In response to a given balance perturbation magnitude, people after stroke display impaired spatiotemporal stepping kinematics. Yet, spatiotemporal stepping kinematics at individualized perturbation magnitudes after stroke and the underlying neural correlates remain unknown. Here, we tested whether stepping kinematics differ in people after stroke at individ...