Modulating Driver Alertness via Ambient Olfactory Stimulation: A Wearable Electroencephalography Study.

Poor alertness levels and related changes in cognitive efficiency are common when performing monotonous tasks such as extended driving. Recent studies have investigated driver alertness decrement and possible strategies for modulating alertness with the goal of improving reaction times to safety critical events. However, most studies rely on subjective measures in assessing alertness changes, while the use of olfactory stimuli, which are known to be strong modulators of cognitive states, has not been commensurately explored in driving alertness settings. To address this gap, in the present study we investigated the effectiveness of olfactory stimuli in modulating the alertness state of drivers and explored the utility of electroencephalography (EEG) in developing objective brain-based tools for assessing the resulting changes in cortical activity. Olfactory stimulation induced a significant differential effect on braking reaction time. The corresponding effect to the cortical activity was characterized using EEG-derived metrics and the devised machine learning framework yielded a high discriminating accuracy (92.1%). Furthermore, neural activity in the alpha frequency band was found to be significantly associated with the observed drivers' behavioral changes. Overall, our results demonstrate the potential of olfactory stimuli to modulate the alertness state and the efficiency of EEG in objectively assessing the resulting cognitive changes.

Advances in Portable Atom Interferometry-Based Gravity Sensing.

Gravity sensing is a valuable technique used for several applications, including fundamental physics, civil engineering, metrology, geology, and resource exploration. While classical gravimeters have proven useful, they face limitations, such as mechanical wear on the test masses, resulting in drift, and limited measurement speeds, hindering their use for long-term monitoring, as well as the need to average out microseismic vibrations, limiting their speed of data acquisition. Emerging sensors based on atom interferometry for gravity measurements could offer promising solutions to these limitations, and are currently advancing towards portable devices for real-world applications. This article provides a brief state-of-the-art review of portable atom interferometry-based quantum sensors and provides a perspective on routes towards improved sensors.

Cardiological Monitoring - A Cornerstone for Pediatric Inflammatory Multisystem Syndrome Temporally Associated with COVID-19 Outcome: A Case Report and a Review from the Literature.

Introduction: Pediatric inflammatory multisystem syndrome temporally associated with COVID-19 (PIMS-TS) is a rare life-threatening condition requiring a complex management and multidisciplinary approach, whose outcome depends on the early diagnosis. Case report: We report the case of a 2 years and-5-month-old boy admitted in our clinic for fever, abdominal pain and diarrhea. The clinical exam at the time of admission revealed influenced gen-eral status, bilateral palpebral edema and conjunctivitis, mucocutaneous signs of dehydration, and abdominal tenderness at palpation. The laboratory tests performed pointed out lymphopenia, thrombocytopenia, anemia, elevated C-reactive protein - CRP, erythrocyte sedimentation rate and ferritin levels, hyponatremia, hypopotassemia, hypertriglyceridemia, elevated D-dimer, in-creased troponin and NT-proBNP. The real-time polymerase chain reaction (RT-PCR) test for SARS-CoV-2 infection was negative, but the serology was positive. Thus, established the diagnosis of PIMS-TS. We initiated intravenous immunoglobulin, empirical antibiotic, anticoagulation therapy and symptomatic drugs. Nevertheless, the clinical course and laboratory parameters worsened, and the 2nd echocardiography pointed out minimal pericardial effusion, slight dilation of the left cavities, dyskinesia of the inferior and septal basal segments of the left ventricle (LV), and LV systolic dysfunction. Therefore, we associated intravenous methylprednisolone, angiotensin converting enzyme inhibitors, spironolactone and hydrochlorothiazide, with outstanding favorable evolution. Conclusions: Echocardiographic monitoring might be a lifesaving diagnostic tool in the management of PIMS-TS.

Sensory stimulation for upper limb amputations modulates adaptability of cortical large-scale systems and combination of somatosensory and visual inputs.

Touch-like phantom limb sensations can be elicited through targeted transcutaneous electrical nerve stimulation (tTENS) in individuals with upper limb amputation. The corresponding impact of sensory stimulation on cortical activity remains an open question. Brain network research shows that sensorimotor cortical activity is supported by dynamic changes in functional connections between relevant brain regions. These groups of interconnected regions are functional modules whose architecture enables specialized function and related neural processing supporting individual task needs. Using electroencephalographic (EEG) signals to analyze modular functional connectivity, we investigated changes in the modular architecture of cortical large-scale systems when participants with upper limb amputations performed phantom hand movements before, during, and after they received tTENS. We discovered that tTENS substantially decreased the flexibility of the default mode network (DMN). Furthermore, we found increased interconnectivity (measured by a graph theoretic integration metric) between the DMN, the somatomotor network (SMN) and the visual network (VN) in the individual with extensive tTENS experience. While for individuals with less tTENS experience, we found increased integration between DMN and the attention network. Our results provide insights into how sensory stimulation promotes cortical processing of combined somatosensory and visual inputs and help develop future tools to evaluate sensory combination for individuals with amputations.

Odor Pleasantness Modulates Functional Connectivity in the Olfactory Hedonic Processing Network.

Olfactory hedonic evaluation is the primary dimension of olfactory perception and thus central to our sense of smell. It involves complex interactions between brain regions associated with sensory, affective and reward processing. Despite a recent increase in interest, several aspects of olfactory hedonic evaluation remain ambiguous: uncertainty surrounds the communication between, and interaction among, brain areas during hedonic evaluation of olfactory stimuli with different levels of pleasantness, as well as the corresponding supporting oscillatory mechanisms. In our study we investigated changes in functional interactions among brain areas in response to odor stimuli using electroencephalography (EEG). To this goal, functional connectivity networks were estimated based on phase synchronization between EEG signals using the weighted phase lag index (wPLI). Graph theoretic metrics were subsequently used to quantify the resulting changes in functional connectivity of relevant brain regions involved in olfactory hedonic evaluation. Our results indicate that odor stimuli of different hedonic values evoke significantly different interaction patterns among brain regions within the olfactory cortex, as well as in the anterior cingulate and orbitofrontal cortices. Furthermore, significant hemispheric laterality effects have been observed in the prefrontal and anterior cingulate cortices, specifically in the beta ((13-30) Hz) and gamma ((30-40) Hz) frequency bands.

An EEG study of human trust in autonomous vehicles based on graphic theoretical analysis.

With the development of autonomous vehicle technology, human-centered transport research will likely shift to the interaction between humans and vehicles. This study focuses on the human trust variation in autonomous vehicles (AVs) as the technology becomes increasingly intelligent. This study uses electroencephalogram data to analyze human trust in AVs during simulated driving conditions. Two driving conditions, the semi-autonomous and the autonomous, which correspond to the two highest levels of automatic driving, are used for the simulation, accompanied by various driving and car conditions. The graph theoretical analysis (GTA) is the primary method for data analysis. In semi-autonomous driving mode, the local efficiency and cluster coefficient are lower in car-normal conditions than in car-malfunction conditions with the car approaching. This finding suggests that the human brain has a strong information processing ability while facing predictable potential hazards. However, when it comes to a traffic light with a car malfunctioning under the semi-autonomous driving mode, the characteristic path length is higher for the car malfunction manifesting a weak information processing ability while facing unpredictable potential hazards. Furthermore, in fully automatic driving conditions, participants cannot do anything and need low-level brain function to take emergency actions as lower local efficiency and small worldness for car malfunction. Our results shed light on the design of the human-machine interaction and human factor engineering on the high level of an autonomous vehicle.

Multilayer Network Framework Reveals Cross-Frequency Coupling Hubs in Cortical Olfactory Perception.

Olfactory perception is shaped by dynamic in-teractions among networks of widely distributed brain regions involved in several neurocognitive processes. However, the neural mechanisms that enable effective coordination and integrative processing across these brain regions, which have different functions and operating characteristics, are not yet fully understood. In this study we use electroencephalography (EEG) signals and a multilayer network formalism to model cross-frequency coupling across the brain and identify brain regions that operate as connecting hubs, thus facilitating inte-grative function. To this goal, we investigate α-γ coupling and θ-γ coupling during exposure to olfactory stimuli of different pleasantness levels. We found that a wider distributed network of hubs emerges in the higher pleasantness condition and that significant differences in the hub connectivity are located in the middle frontal and central regions. Our results indicate the consistent functional role that γ band activity plays in information integration in olfactory perception.

Objective assessment of trait attentional control predicts driver response to emergency failures of vehicular automation.

With the advent of autonomous driving, the issue of human intervention during safety-critical events is an urgent topic of research. Supervisory monitoring, taking over vehicle control during automation failures and then bringing the vehicle to safety under time pressure are cognitively demanding tasks that pose varying difficulties across the driving population. This underpins a need to investigate individual differences (i.e., how people differ in their dispositional traits) in driver responses to automation system limits, so that autonomous vehicle design can be tailored to meet the safety-critical needs of higher-risk drivers. However, few studies thus far have examined individual differences, with self-report measures showing limited ability to predict driver takeover performance. To address this gap, the present study explored the utility of an established brain activity-based objective index of trait attentional control (frontal theta/beta ratio; TBR) in predicting driver interactions with conditional automation. Frontal TBR predicted drivers' average takeover reaction time, as well as the likelihood of accident after takeover. Moving towards practical applications, this study also demonstrated the utility of streamlined estimates of frontal TBR measured from the forehead electrodes and from a single crown electrode, with the latter showing better fidelity and predictive value. Overall, TBR is behaviourally relevant, measurable with minimal sensors and easily computable, rendering it a promising candidate for practical and objective assessment of drivers' neurocognitive traits that contribute to their AV driving readiness.

Distinct spatio-temporal and spectral brain patterns for different thermal stimuli perception.

Understanding the human brain's perception of different thermal sensations has sparked the interest of many neuroscientists. The identification of distinct brain patterns when processing thermal stimuli has several clinical applications, such as phantom-limb pain prediction, as well as increasing the sense of embodiment when interacting with neurorehabilitation devices. Notwithstanding the remarkable number of studies that have touched upon this research topic, understanding how the human brain processes different thermal stimuli has remained elusive. More importantly, very intense thermal stimuli perception dynamics, their related cortical activations, as well as their decoding using effective features are still not fully understood. In this study, using electroencephalography (EEG) recorded from three healthy human subjects, we identified spatial, temporal, and spectral patterns of brain responses to different thermal stimulations ranging from extremely cold and hot stimuli (very intense), moderately cold and hot stimuli (intense), to a warm stimulus (innocuous). Our results show that very intense thermal stimuli elicit a decrease in alpha power compared to intense and innocuous stimulations. Spatio-temporal analysis reveals that in the first 400 ms post-stimulus, brain activity increases in the prefrontal and central brain areas for very intense stimulations, whereas for intense stimulation, high activity of the parietal area was observed post-500 ms. Based on these identified EEG patterns, we successfully classified the different thermal stimulations with an average test accuracy of 84% across all subjects. En route to understanding the underlying cortical activity, we source localized the EEG signal for each of the five thermal stimuli conditions. Our findings reveal that very intense stimuli were anticipated and induced early activation (before 400 ms) of the anterior cingulate cortex (ACC). Moreover, activation of the pre-frontal cortex, somatosensory, central, and parietal areas, was observed in the first 400 ms post-stimulation for very intense conditions and starting 500 ms post-stimuli for intense conditions. Overall, despite the small sample size, this work presents novel findings and a first comprehensive approach to explore, analyze, and classify EEG-brain activity changes evoked by five different thermal stimuli, which could lead to a better understanding of thermal stimuli processing in the brain and could, therefore, pave the way for developing a real-time withdrawal reaction system when interacting with prosthetic limbs. We underpin this last point by benchmarking our EEG results with a demonstration of a real-time withdrawal reaction of a robotic prosthesis using a human-like artificial skin.

Reconstructing EOG From EEG Timeseries: A Spatial Filtering Approach.

Unobtrusive mental state monitoring based on neurosphysiological signals has seen thriving developments over the past decade, with a wide area of applications, from rehabilitation to neuroergonomics and neuromarketing. Particularly, electroencephalography (EEG) and electrooculography (EOG) have been popular techniques to obtain cognitive-relevant biosignals. However, current wearable systems may still pose practical inconvenience, motivating further interest to integrate EOG+EEG recording into streamlined frontal-only sensor montages with sufficient signal fidelity. We propose, here, a spatial filtering approach to reliably extract EOG signals from a reduced set of frontal EEG electrodes, placed on non-hair-bearing (NHB) areas. Within a common signal analytic framework, two distinct schemes are examined. The one is based on standard linear least squares (LLS) and the other on Least Absolute Shrinkage and Selection Operator (LASSO). Both schemes are data-driven techniques, require a small amount of training data, and lead to reliable estimators of EOG activity from EEG signals. The LASSO-based technique, in addition, provides guidelines that generalize well across subjects. Using experimental data, we provide some empirical evidence that our estimators can replace the actual EOG signals in algorithmic pipelines that automatically detect oculographic events, like blinks and saccades.

Community Analysis of Brain Functional Networks Reveals Systems-Level Integration in Olfactory Hedonic Perception.

Olfactory hedonic perception involves complex interplay among an ensemble of neurocognitive systems implicated in sensory, affective and reward processing. However, the mechanisms of these inter-system interactions have yet to be well-characterized. Here, we employ directed functional connectivity networks estimated from source-localized EEG to uncover how brain regions across the olfactory, emotion and reward systems integrate organically into cross-system communities. Using the integration coefficient, a graph theoretic measure, we quantified the effect of exposure to fragrance stimuli of different hedonic values (high vs low pleasantness levels) on inter-systems interactions. Our analysis focused on beta band activity (13-30 Hz), which is known to facilitate integration of cortical areas involved in sensory perception. Higher-pleasantness stimuli induced elevated integration for the reward system, but not for the emotion and olfactory systems. Furthermore, the nodes of reward system showed more outward connections to the emotion and olfactory systems than inward connections from the respective systems. These results suggest the centrality of the reward system-supported by beta oscillations-in actively coordinating multi-system interactivity to give rise to hedonic experiences during olfactory perception.

Wearable EEG Entropy and Spectral Measures for Classification of Consumer Reward-based Evaluation of Odor Stimuli.

Consumer neuroscience is a rapidly emerging field, with the ability to detect consumer attitudes and states via real-time passive technologies being highly valuable. While many studies have attempted to classify consumer emotions and perceived pleasantness of olfactory products, no known machine learning approach has yet been developed to directly predict consumer reward-based decision-making, which has greater behavioral relevance. In this proof-of-concept study, participants indicated their decision to have fragrance products repeated after fixed exposures to them. Single-trial power spectral density (PSD) and approximate entropy (ApEn) features were extracted from EEG signals recorded using a wearable device during fragrance exposures, and served as subject-independent inputs for 4 supervised learning algorithms (kNN, Linear-SVM, RBF- SVM, XGBoost). Using a cross-validation procedure, kNN yielded the best classification accuracy (77.6%) using both PSD and ApEn features. Acknowledging the challenging prospects of single-trial classification of high-order cognitive states especially with wearable EEG devices, this study is the first to demonstrate the viability of using sensor-level features towards practical objective prediction of consumer reward experience.

Exploring Inflammatory Status in Febrile Seizures Associated with Urinary Tract Infections: A Two-Step Cluster Approach.

BACKGROUND: Urinary tract infections (UTIs) are considered common facilitating factors, along with other infections, in triggering febrile seizures (FS). The main purpose of our study was to identify specific inflammatory patterns of UTI cases from other infections in a specific cluster, using a combination of inflammatory biomarkers to differentiate UTIs from other bacterial diseases triggering FS. METHOD: This prospective study included a number of 136 patients with 197 distinct FS events, from patients hospitalized in the Pediatric Clinical Hospital Sibiu, among which 10.2% were diagnosed with UTIs. RESULTS: In one-third of the patients with UTIs (20 cases), the symptoms were limited to fever and FS. Using two-step cluster analysis, a distinct UTI inflammatory pattern has emerged: highest platelet values (PLT), median value 331 × 103/mm3 and intermediate C-reactive protein (CRP), median value 15 mg/dL, platelet distribution width (PDW), median value 9.65%, platelet-large cell ratio (P-LCR), median value 14.45%, mean platelet volume (MPV), median value 8.60 fL and neutrophil-to-lymphocyte values (NLR), median value 3.64. Furthermore, higher PDW (median value 12.25%), P-LCR (median value 28.55%), MPV (median value 10.40 fL), CRP (median value 74.00 mg/dL) and NLR values (median value 4.11) were associated mainly (85.7%) with bacterial lower respiratory infections. UTIs were highly unlikely in these patients with significantly increased CRP values and normal values of platelet indices. CONCLUSIONS: Considering the nonspecific clinical picture of UTIs at an early age, to optimize the management of FS, a fast diagnosis of UTI is mandatory. The analysis of the inflammatory biomarker clusters (rather than individual parameters) correlated with urine leukocyte and nitrite stick evaluation for specific age groups could help in identifying even oligosymptomatic UTIs patients. The study limitation (20 UTI cases) recommends future multicentric trials on larger datasets to validate the model.

A Decision-Tree Approach to Assist in Forecasting the Outcomes of the Neonatal Brain Injury.

Neonatal brain injury or neonatal encephalopathy (NE) is a significant morbidity and mortality factor in preterm and full-term newborns. NE has an incidence in the range of 2.5 to 3.5 per 1000 live births carrying a considerable burden for neurological outcomes such as epilepsy, cerebral palsy, cognitive impairments, and hydrocephaly. Many scoring systems based on different risk factor combinations in regression models have been proposed to predict abnormal outcomes. Birthweight, gestational age, Apgar scores, pH, ultrasound and MRI biomarkers, seizures onset, EEG pattern, and seizure duration were the most referred predictors in the literature. Our study proposes a decision-tree approach based on clinical risk factors for abnormal outcomes in newborns with the neurological syndrome to assist in neonatal encephalopathy prognosis as a complementary tool to the acknowledged scoring systems. We retrospectively studied 188 newborns with associated encephalopathy and seizures in the perinatal period. Etiology and abnormal outcomes were assessed through correlations with the risk factors. We computed mean, median, odds ratios values for birth weight, gestational age, 1-min Apgar Score, 5-min Apgar score, seizures onset, and seizures duration monitoring, applying standard statistical methods first. Subsequently, CART (classification and regression trees) and cluster analysis were employed, further adjusting the medians. Out of 188 cases, 84 were associated to abnormal outcomes. The hierarchy on etiology frequencies was dominated by cerebrovascular impairments, metabolic anomalies, and infections. Both preterms and full-terms at risk were bundled in specific categories defined as high-risk 75-100%, intermediate risk 52.9%, and low risk 0-25% after CART algorithm implementation. Cluster analysis illustrated the median values, profiling at a glance the preterm model in high-risk groups and a full-term model in the inter-mediate-risk category. Our study illustrates that, in addition to standard statistics methodologies, decision-tree approaches could provide a first-step tool for the prognosis of the abnormal outcome in newborns with encephalopathy.

The Design of an Intelligent Robotic Wheelchair Supporting People with Special Needs, Including for Their Visual System.

The paper aims to study the applicability and limitations of the solution resulting from a design process for an intelligent system supporting people with special needs who are not physically able to control a wheelchair using classical systems. The intelligent system uses information from smart sensors and offers a control system that replaces the use of a joystick. The necessary movements of the chair in the environment can be determined by an intelligent vision system analyzing the direction of the patient's gaze and point of view, as well as the actions of the head. In this approach, an important task is to detect the destination target in the 3D workspace. This solution has been evaluated, outdoor and indoor, under different lighting conditions. In order to design the intelligent wheelchair, and because sometimes people with special needs also have specific problems with their optical system (e.g., strabismus, Nystagmus) the system was tested on different subjects, some of them wearing eyeglasses. During the design process of the intelligent system, all the tests involving human subjects were performed in accordance with specific rules of medical security and ethics. In this sense, the process was supervised by a company specialized in health activities that involve people with special needs. The main results and findings are as follows: validation of the proposed solution for all indoor lightning conditions; methodology to create personal profiles, used to improve the HMI efficiency and to adapt it to each subject needs; a primary evaluation and validation for the use of personal profiles in real life, indoor conditions. The conclusion is that the proposed solution can be used for persons who are not physically able to control a wheelchair using classical systems, having with minor vision deficiencies or major vision impairment affecting one of the eyes.

Evoked Brain Responses in Odor Stimuli Evaluation - an EEG Event Related Potential Study.

Decoding olfactory cognition has been generating significant interest in recent years due to a wide range of applications, from diagnosing neurodegenerative disorders to consumer research and traditional medicine. In this study, we have investigated whether changes in odor stimuli evaluation across repeated stimuli presentation can be attributed to changes in brain perception of the stimuli. Epoch intervals representing olfactory sensory perception were extracted from electroencephalography (EEG) signals using minimum variance distortionless response (MVDR)-based single trial event related potential (ERP) approach to understand the evoked response to high pleasantness and low pleasantness stimuli. We found statistically significant changes in self reported stimuli evaluation between initial and final trials (p < 0.05) for both stimuli categories. However, the changes in ERP amplitude were found to be statistically significant only for the high pleasantness stimuli. This implies that olfactory stimuli of higher hedonic value recruit high-order cognitive processing that may be responsible for initial increased ERP response, as well as for rapid subsequent adaptation in processing the stimuli.

Olfactory-induced Positive Affect and Autonomic Response as a Function of Hedonic and Intensity Attributes of Fragrances.

Olfactory perception is intrinsically tied to emotional processing, in both behavior and neurophysiology. Despite advances in olfactory-affective neuroscience, it is unclear how separate attributes of odor stimuli contribute to olfactoryinduced emotions, especially within the positive segment of the hedonic dimension to avoid potential cross-valence confounds. In this study, we examined how pleasantness and intensity of fragrances relate to different grades of positive affect. Our results show that greater odor pleasantness and intensity are independently associated with stronger positive affect. Pleasantness has a greater influence than intensity in evoking a positive vs. neutral affect, whereas intensity is more impactful than pleasantness in evoking an extreme positive vs. positive response. Autonomic response, as assessed by the galvanic skin response (GSR) was found to decrease with increasing pleasantness but not intensity. This clarifies how olfactory and affective processing induce significant downstream effects in peripheral physiology and self-reported affective experience, pertinent to the thriving field of olfactory neuromarkerting.

Sensory Feedback in Upper Limb Amputees Impacts Cortical Activity as Revealed by Multiscale Connectivity Analysis.

Sensory feedback in upper limb amputees is crucial for improving movement decoding and also to enhance embodiment of the prosthetic limb. Recently, an increasing number of invasive and noninvasive solutions for sensory stimulation have demonstrated the capability of providing a range of sensations to upper limb amputees. However, the cortical impact of restored sensation is not clearly understood. Particularly, understanding the cortical connectivity changes at multiple scales (nodal and modular) in response to sensory stimulation, can reveal crucial information on how amputees brain process the sensory stimuli. Using Electroencephalography (EEG) signals, we compared the cortical connectivity network in response to sensory feedback provided by targeted transcutaneous electrical nerve stimulation (tTENS) in an upper limb amputee during phantom upper limb movements. We focused our cortical connectivity analysis on four functional modules comprising of 20 brain regions that are primarily associated with a visually guided motor task (visual, motor, somatosensory and multisensory integration (MI)) used in this study. At the modular level, we observed that the hubness (a graph theoretic measure quantifying the importance of brain regions in integrating brain function) of the motor module decreases whereas that of the somatosensory module increases in presence of tTENS feedback. At the nodal level, similar observations were made for the visual and MI regions. This is the first work to reveal the impact of sensory feedback at multiple scales in the cortex of amputees in response to sensory stimulation.

Identification of differentially expressed subpathways via a bilevel consensus scoring of network topology and gene expression.

Identifying differentially expressed subpathways connected to the emergence of a disease that can be considered as candidates for pharmacological intervention, with minimal off-target effects, is a daunting task. In this direction, we present a bilevel subpathway analysis method to identify differentially expressed subpathways that are connected with an experimental condition, while taking into account potential crosstalks between subpathways which arise due to their connectivity in a combined multi-pathway network. The efficacy of the method is demonstrated on a hematopoietic stem cell aging dataset, with findings corroborated using recent literature.

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.