Douglas J. Weber

Objective. High performance intracortical brain-computer interface (iBCI) control has been demonstrated in research settings, but performance can still vary within and between sessions. One potential source of this variability is the change in attentional load that comes from processing naturally occurring distractors such as thoughts, sounds, fatigue, or pain. To improve the consistency of iBCI performance in real-world environments where this sort of multi-tasking is inevitable, we must understand how shifts in attention can impact performance. Approach. Here we examined the effect of attentional load on iBCI performance and movement-related neural activity using a 2D cursor translation + click iBCI task paired with an N -Back working memory task to increase attentional load during dual-task performance. Two participants (P2 and P4) with tetraplegia completed the study while enrolled in a long-term clinical trial of an iBCI device. Common neural correlates of attention (theta and alpha band power) were measured with simultaneously recorded scalp electroencephalography (EEG). Main Results. While the EEG metrics and difficulty ratings suggested increased attentional load during dual-tasking, iBCI performance was quite robust across the various dual-tasking conditions. One participant, P2, experienced a small but significant increase in trial completion time and normalized path length during the mild attentional load condition compared to the moderate load condition. Signal quality differences between the two participants may have impacted the results, as P2 had lower signal quality and was therefore likely more vulnerable to attentional load. P4’s higher signal quality likely allowed him to accommodate increased attentional load without a drop in performance. Significance. Overall, iBCI performance appears to be robust to attentional load, but the complex trends observed here reflect a need for continued investigation of BCI use under different cognitive states to elucidate potential challenges and compensatory mechanisms across participants.

Fiber-type composition contributes to contractile behavior and fatigue resistance. Characterizing these physiological differences could help study and monitor disease-related changes. Direct fiber-type measurement requires biopsy, whereas animal findings may not fully translate to humans. High-density surface electromyography (HD-sEMG) may offer repeatable, noninvasive assessment. We recorded HD-sEMG from tibialis anterior (TA) and soleus, muscles with contrasting fast- and slow-twitch-like physiology, during sustained contractions at 30% and 50% maximum voluntary contraction in participants with amyotrophic lateral sclerosis (ALS; n = 7) and healthy controls ( n = 10). Given fast-fatigable motor unit vulnerability in ALS models, we compared TA-soleus profiles and fatigue-related changes. Amplitude, spectral, motor unit firing, and motor unit action potential (MUAP) features were quantified in the first 10 s of stable contraction, with firing and MUAP features derived from decomposition. Exploratory fatigue analyses assessed early-to-late changes and trajectories. Early-window root mean square (RMS), spectral power, firing rate, and MUAP amplitude were higher in TA than soleus in both groups. Group x Muscle interactions were nonsignificant. Despite higher early values, TA showed larger fatigue-related declines in high-frequency power, RMS, and firing rate than soleus, particularly in ALS-affected TA. These findings suggest HD-sEMG detects muscle-type-related electrophysiological differences, with ALS-related changes more evident during fatigue.

Objective. Stent-electrode arrays enable endovascular brain–computer interfaces (BCI) by recording cortical neural activity from within the superior sagittal sinus and have recently been evaluated in an early feasibility clinical trial in the United States (ClinicalTrials.gov: NCT05035823). For a BCI to be viable, the signals need to be high quality to enable accurate decoding of user intent. Compared to electrodes placed on the scalp for electroencephalography (EEG), stent-electrode arrays lie closer to the cortical surface and would presumably offer higher signal quality yet a direct comparison of intravascular and scalp-based neural recordings in humans has not yet been investigated. Approach. We directly compared the signal quality of vascular electrocorticography (vECoG) versus scalp EEG signals in one participant with severe upper limb paralysis due to ALS. During two experimental sessions, the participant underwent simultaneous recording with the stent-electrode array and a scalp EEG using a gel cap. The participant was visually cued to attempt motor tasks, such as repeated flexion and extension of the ankles. Signal quality was assessed by quantifying motor modulation strength, differentiation of movement effort, and spatial lateralization. Noise metrics evaluated the relative impact of artifacts including 60 Hz line noise, electrocardiogram contamination, eye blinks, jaw clenching, and vocalization. Main results. Both recording modalities exhibited significant modulation during attempted movement relative to rest, with vECoG generally demonstrating significantly stronger modulation per channel in some frequency bands and conditions. Motor modulation was significantly reduced during motor imagery compared to overt movement in both modalities. Spatial source localization between left and right ankle movement did not reach significance for either modality. Each modality was vulnerable to some artifacts while generally unaffected by others. Scalp EEG showed large susceptibility to ocular artifacts and cranial muscle activity due to its proximity to superficial physiological sources, whereas vECoG exhibited prominent cardiac activity. Significance. Within this participant, the large modulation during attempted movement recorded with vECoG, unaffected by the attenuating effects of the skull, coupled with fewer artifacts in the frequency bands of interest, provides preliminary evidence that the stent-electrode arrays can acquire high quality neural signals that could support BCI control.

Vascular electrocorticography (vECoG) has shown great promise as a less‐invasive alternative to penetrating electrode arrays for neural signal acquisition. This study investigates the signal quality of the Stentrode device, a leading vECoG platform, by contrasting artifact persistence with the preservation of low frequency motor cortical activity. Typical (re‐)referencing schemes, including a monopolar stent‐mounted reference, a common average reference, and a Laplacian reference, are shown to significantly reduce the presence of electrocardiogram (ECG) artifacts compared to a distal monopolar reference. However, resting state beta activity is also significantly diminished when employing these techniques. By using Band‐Limited Independent Component Analysis (BL‐ICA), a type of spatial filter that allows weighting of the noise on each electrode differently, ECG artifacts are easily separated from vECoG recordings. With this cleaning methodology, signals are reconstructed without the ECG component and the reduction in cross‐channel correlation is evaluated, as well as the increase in relative entropy between rest and go distributions. To ensure that low‐frequency motor features are preserved, beta bursts features are evaluated in both the source space and reconstructed signal space. BL‐ICA is an effective technique to remove widespread ECG artifacts while maintaining typical motor‐related features in beta band activity.

Amyotrophic lateral sclerosis (ALS) causes motor neuron loss, denervation, collateral reinnervation, and altered motor unit organization. Clinical assessments track functional decline but provide limited information about underlying muscle remodeling. We recorded high-density surface electromyography (HD-sEMG) from the biceps brachii and tibialis anterior in participants with ALS and healthy controls during sustained contractions at 30% and 50% maximum voluntary contraction. We examined features reflecting spatial activation, motor unit morphology, fatigue-related signal behavior, and propagation. Principal component analysis was applied to early steady-state features and fatigue-related changes. At baseline, HD-sEMG structure was highly similar between groups and dominated by spatial features, which contributed 91.1% of loading weight in healthy observations and 89.9% in ALS observations. Spatial features alone did not separate groups, but a multifeature baseline composite combining morphology and spatial organization did. During fatigue, ALS observations showed a larger shift in loading structure and greater score variability, with stronger contributions from amplitude and spectral features. The fatigue-change composite did not significantly distinguish groups or scale linearly with limb function. These findings suggest that HD-sEMG may be most useful for capturing neuromuscular organization and heterogeneous physiological responses in ALS rather than serving as a single uniform marker of disease status.

Whether proprioception is necessary for upper-limb motor control has been debated for decades. Classic studies in deafferented animals and humans suggested that proprioception may be dispensable for rapid, goal-directed movements. However, chronic sensory loss conflates the absence of proprioceptive input with years of compensatory adaptation. As a result, the field has lacked a strong causal test of proprioception's contribution to motor control. Here, we leveraged a clinical trial of cervical spinal cord stimulation (SCS) in individuals with chronic post-stroke hemiparesis to study if electrical stimulation of sensory afferents causally perturbs proprioception and affects arm reaching. We found that turning SCS ON causally impaired proprioceptive perception and postural stabilization in response to force perturbations, and enhanced adaptation to visual errors during implicit learning. Yet visually and non-visually rapid, goal-directed reaching improved in smoothness, straightness and spatial accuracy. These findings provide strong causal evidence that proprioception is not required for rapid, goal-directed action, helping resolve a decades-long debate regarding its necessity for effective movement.

High performance intracortical brain-computer interface (iBCI) control has been demonstrated in research settings, but performance can still vary within and between sessions. One potential source of this variability is the change in attentional load that comes from processing naturally occurring distractors such as thoughts, sounds, fatigue, or pain. To improve the consistency of iBCI performance in real-world environments where this sort of multi-tasking is inevitable, we must understand how shifts in attention can impact performance. Here we examined the effect of attentional load on iBCI performance and movement-related neural activity using a 2D cursor translation + click iBCI task paired with an N-Back working memory task to increase attentional load during dual-task performance. Two participants (P2 and P4) with tetraplegia completed the study while enrolled in a long-term clinical trial of an iBCI device (NCT1894802). Common neural correlates of attention (theta and alpha band power) were measured with simultaneously recorded scalp electroencephalography (EEG). While the EEG recordings and difficulty ratings suggested increased attentional load during dual tasking, iBCI performance was quite robust across the various dual tasking conditions. One participant, P2, experienced a small but significant increase in trial completion time and normalized path length during the mild attentional load condition. Signal quality differences between the two participants may have impacted the results, as P2 had lower signal quality and was therefore likely more vulnerable to attentional load. P4’s higher signal quality likely allowed him to accommodate increased attentional load without a drop in performance. Overall, iBCI performance appears to be robust to attentional load, but the complex trends observed here reflect a need for continued investigation of BCI use under different cognitive states to elucidate potential challenges and compensatory mechanisms across participants.