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BACKGROUND: Automated insulin delivery (AID) devices have shown to be a promising treatment to improve glycemic control in patients with type 1 diabetes mellitus (T1DM). However, its efficacy in pregnant women with T1DM remains uncertain. METHODS: PubMed, Scopus, Cochrane Central and ClinicalTrials.gov were systematically searched for randomized controlled trials (RCTs) comparing AID to standard care (SC), defined as use of sensor-augmented pump and multiple daily insulin injections. Outcomes included time in range (TIR), nocturnal TIR, time in hypoglycemic and hyperglycemic ranges, among others. Sensitivity and trial sequential analyses (TSA) were performed. PROSPERO ID: CRD42023474398. RESULTS: We included five RCTs with a total of 236 pregnant women, of whom 117 (50.6%) received AID. There was a significant increase in nocturnal TIR (mean difference [MD] 12.69%; 95% CI 8.74-16.64; p < 0.01; I2 = 0%) and a decrease in glucose variability (standard deviation of glucose; MD -2.91; 95% CI -5.13 to -0.69; p = 0.01; I2 = 0%). No significant differences were observed for TIR, HBGI, LGBI, mean glucose and time spent in hyperglycemia and hypoglycemia. Regarding TSA, the statistical significance obtained in nocturnal TIR was conclusive and with minimal risk of a type I error. CONCLUSION: Our findings suggest that AID systems can significantly improve nocturnal glycemic control and potentially reduce glycemic variability in pregnant women with T1DM, with no effect in the risk of hypoglycemia and hyperglycemia compared with current insulin treatments.

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The aim of this study was to assess the efficacy and safety of hybrid closed-loop (HCL) systems for insulin delivery in children and adolescents with type 1 diabetes (T1D). We searched Embase, PubMed, and Cochrane Library for randomized controlled trials (RCTs) published until March 2023 comparing the HCL therapy with control therapies for children and adolescents with T1D. We computed weighted mean differences (WMDs) for continuous outcomes and risk ratios (RRs) with 95% confidence intervals (CIs) for binary endpoints. Four RCTs and 501 patients were included, of whom 323 were randomized to HCL therapy. Compared with control therapies, HCL significantly improved the period during which glucose level was 70-180 mg/dL (WMD 10.89%, 95% CI 8.22-13.56%) and the number of participants with glycated hemoglobin (HbA1c) level < 7% (RR 2.61, 95% CI 1.29-5.28). Also, HCL significantly reduced the time during which glucoselevel was > 180 mg/dL (WMD-10.46%, 95% CI-13.99 to-6.93%) and the mean levels of glucose (WMD-16.67 mg/dL, 95% CI-22.25 to-11.09 mg/dL) and HbA1c (WMD-0.50%, 95% CI-0.68 to-0.31). There were no significant differences between therapies regarding time during which glucose level was < 70 mg/dL or <54 mg/dL or number of episodes of ketoacidosis, hyperglycemia, and hypoglycemia. In this meta-analysis, HCL compared with control therapies was associated with improved time in range and HbA1c control in children and adolescents with T1D and a similar profile of side effects. These findings support the efficacy of HCL in the treatment of T1D in this population.

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Objective.This paper aims to bridge the gap between neurophysiology and automatic control methodologies by redefining the Wilson-Cowan (WC) model as a control-oriented linear parameter-varying (LPV) system. A novel approach is presented that allows for the application of a control strategy to modulate and track neural activity.Approach.The WC model is redefined as a control-oriented LPV system in this study. The LPV modelling framework is leveraged to design an LPV controller, which is used to regulate and manipulate neural dynamics.Main results.Promising outcomes, in understanding and controlling neural processes through the synergistic combination of control-oriented modelling and estimation, are obtained in this study. An LPV controller demonstrates to be effective in regulating neural activity.Significance.The presented methodology effectively induces neural patterns, taking into account optogenetic actuation. The combination of control strategies with neurophysiology provides valuable insights into neural dynamics. The proposed approach opens up new possibilities for using control techniques to study and influence brain functions, which can have key implications in neuroscience and medicine. By means of a model-based controller which accounts for non-linearities, noise and uncertainty, neural signals can be induced on brain structures.

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INTRODUCTION: For patients with drug-resistant epilepsy (DRE) who are not suitable for surgical resection, neuromodulation with vagus nerve stimulation (VNS) is an established approach. However, there is limited evidence of seizure reduction when replacing traditional VNS (tVNS) device with a cardiac-based one (cbVNS). This meta-analysis compares the seizure reduction achieved by replacing tVNS with cbVNS in a population with DRE. METHODS: We systematically searched PubMed, Embase, and Cochrane Central following PRISMA guidelines. The main outcomes were number of patients experiencing a ≥ 50 % and ≥80 % reduction in seizures, as defined by the McHugh scale. Additionally, we assessed the number of patients achieving freedom from seizures. RESULTS: We included 178 patients with DRE from 7 studies who were initially treated with tVNS and subsequently had it replaced by cbVNS. The follow-up for cbVNS ranged from 6 to 37.5 months. There was a statistically significant reduction in seizure frequency with the replacement of tVNS by cbVNS, using a ≥ 50 % (OR 1.79; 95 % CI 1.07 to 2.97; I²=0 %; p = 0.03) and a ≥ 80 % (OR 2.06; 95 % CI 1.17 to 3.62; I²=0 %; p = 0.01) reduction threshold. Nineteen (13 %) participants achieved freedom from seizures after switching to cbVNS. There was no difference in the rate of freedom from seizures between groups (OR 1.85; 95 % CI 0.81 to 4.21; I²=0 %; p = 0.14). CONCLUSION: In patients with DRE undergoing battery replacement, cbVNS might be associated with seizure reduction (≥50 % and ≥80 % threshold) after switching from tVNS. Randomised controlled trials are necessary to validate these findings.

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BACKGROUND: This randomized and controlled prospective study tested the hypothesis that closed-loop Target-Controlled Infusion (TCI) of propofol would be associated with better system performance when compared with open-loop controlled delivery of propofol. METHODS: Patients scheduled for elective breast surgery were randomly assigned to two groups: a closed-loop group, in which propofol infusion was performed by a closed-loop TCI system that used the Bispectral Index (BIS) as a feedback parameter to titrate the rate of propofol infusion, and an open-loop group, in which propofol infusion was performed manually and guided by the bispectral index. RESULTS: A total of 156 patients were recruited for this study (closed-loop group n = 79; open-loop group n = 77). The Global Score (GS) of the closed-loop group was lower than that of the open-loop group (34.3 and 42.2) (p = 0.044). The proportions of time with a BIS value between 40 and 60 were almost identical in the closed-loop group and the open-loop group (68.7 ± 10.6% and 66.7 ± 13.3%) (p = 0.318). The individuals in the closed-loop group consumed more propofol compared with those in the open-loop group (7.20 ± 1.65 mg.kg-1.h-1 vs. 6.03 ± 1.31 mg.kg-1.h-1, p < 0.001). No intraoperative recall, somatic events or adverse events occurred. No significant difference in heart rate was observed between the two groups (p = 0.169). CONCLUSION: The closed-loop protocol was associated with lower BIS variability and lower out-of-range BIS values, at the cost of a greater consumption of propofol when compared to the open loop group. REGISTER NUMBER: ChiCTR-INR-17010399.

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ABSTRACT The aim of this study was to assess the efficacy and safety of hybrid closed-loop (HCL) systems for insulin delivery in children and adolescents with type 1 diabetes (T1D). We searched Embase, PubMed, and Cochrane Library for randomized controlled trials (RCTs) published until March 2023 comparing the HCL therapy with control therapies for children and adolescents with T1D. We computed weighted mean differences (WMDs) for continuous outcomes and risk ratios (RRs) with 95% confidence intervals (CIs) for binary endpoints. Four RCTs and 501 patients were included, of whom 323 were randomized to HCL therapy. Compared with control therapies, HCL significantly improved the period during which glucose level was 70-180 mg/dL (WMD 10.89%, 95% CI 8.22-13.56%) and the number of participants with glycated hemoglobin (HbA1c) level < 7% (RR 2.61, 95% CI 1.29-5.28). Also, HCL significantly reduced the time during which glucose level was > 180 mg/dL (WMD -10.46%, 95% CI -13.99 to -6.93%) and the mean levels of glucose (WMD -16.67 mg/dL, 95% CI -22.25 to -11.09 mg/dL) and HbA1c (WMD -0.50%, 95% CI -0.68 to -0.31). There were no significant differences between therapies regarding time during which glucose level was < 70 mg/dL or <54 mg/dL or number of episodes of ketoacidosis, hyperglycemia, and hypoglycemia. In this meta-analysis, HCL compared with control therapies was associated with improved time in range and HbA1c control in children and adolescents with T1D and a similar profile of side effects. These findings support the efficacy of HCL in the treatment of T1D in this population.

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Abstract Background: This randomized and controlled prospective study tested the hypothesis that closed-loop Target-Controlled Infusion (TCI) of propofol would be associated with better system performance when compared with open-loop controlled delivery of propofol. Methods: Patients scheduled for elective breast surgery were randomly assigned to two groups: a closed-loop group, in which propofol infusion was performed by a closed-loop TCI system that used the Bispectral Index (BIS) as a feedback parameter to titrate the rate of propofol infusion, and an open-loop group, in which propofol infusion was performed manually and guided by the bispectral index. Results: A total of 156 patients were recruited for this study (closed-loop group n = 79; open-loop group n = 77). The Global Score (GS) of the closed-loop group was lower than that of the open-loop group (34.3 and 42.2) (p = 0.044). The proportions of time with a BIS value between 40 and 60 were almost identical in the closed-loop group and the open-loop group (68.7 ± 10.6% and 66.7 ± 13.3%) (p = 0.318). The individuals in the closed-loop group consumed more propofol compared with those in the open-loop group (7.20 ± 1.65 mg.kg−1.h−1 vs. 6.03 ± 1.31 mg.kg−1.h−1, p < 0.001). No intraoperative recall, somatic events or adverse events occurred. No significant difference in heart rate was observed between the two groups (p = 0.169). Conclusion: The closed-loop protocol was associated with lower BIS variability and lower out-of-range BIS values, at the cost of a greater consumption of propofol when compared to the open loop group. Register number:ChiCTR-INR-17010399.

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Objectives: To describe the trends and characteristics of the worldwide scientific production on the use of artificial pancreas (AP) in the management of diabetes mellitus (DM). Materials and Methods: Scientific papers published between 2017 and 2022 were retrieved from the Scopus database using relevant keywords. Only original articles, reviews, and short surveys were included. The metadata were exported to the SciVal software for retrieving quantitative data and the main characteristics such as journals, authors, institutions, journal metrics by quartiles, subcategories, and collaborative networks were extracted. Results: A total of 642 articles were included after applying the inclusion/exclusion criteria: original articles, 489; reviews, 151; and short surveys, 2. The most common type of collaboration was at the national level (38.3%; citations per publication: 22.3; field-weighted citation index [FWCI]: 2.2) followed by international collaboration (29.4%; citations per publication: 19.6; FWCI: 1.94). More than 70% of articles in each year were published in journals listed in Q1. Two journals, Diabetes Technology and Therapeutics and Journal of Diabetes Science and Technology, accounted for about 22% of all publications. Six of the top 10 universities were from the United States, with The University of Virginia having the most publications (n = 54; 59 authors; citations per publication: 38.4; FWCI: 3.73). Conclusions: The findings of this study highlight that most research on this topic is published in high-quality journals and has a good citation impact. Notably, most research has been conducted in developed countries, thereby indicating the need for research efforts in this field from developing countries.

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While external stimulation can reliably trigger neuronal activity, cerebral processes can operate independently from the environment. In this study, we conceptualize autogenous cerebral processes (ACPs) as intrinsic operations of the brain that exist on multiple scales and can influence or shape stimulus responses, behavior, homeostasis, and the physiological state of an organism. We further propose that the field should consider exploring to what extent perception, arousal, behavior, or movement, as well as other cognitive functions previously investigated mainly regarding their stimulus-response dynamics, are ACP-driven.

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BACKGROUND: Evidence regarding the implementation of medium-term strategies in advanced hybrid closed-loop (AHCL) system users is limited. Therefore, this study aimed to describe the efficacy and safety of the AHCL system in patients with type 1 diabetes (T1D) on a six-month follow-up in a virtual diabetes clinic (VDC). METHOD: A prospective cohort of adult patients with T1D treated using the AHCL system (Mini Med 780G; Medtronic, Northridge, California) in a VDC follow-up. Standardized training and follow-up were conducted virtually. Clinical data and metabolic control outcomes were reported at baseline, and at three and six months. RESULTS: Sixty-four patients (mean age = 42 ± 14.6 years, 65% men, 54% with graduate education) were included. Percentage time in range (%TIR) increased significantly regardless of prior therapy with intermittently scanned continuous glucose monitoring + multiple daily injections and sensor-augmented pump therapy with predictive low-glucose management after starting AHCL and persisted during the follow-up period with no hypoglycemic events. The %TIR 70 to 180 mg/dL according to socioeconomic strata was 73.4% ± 5.3%, 78.1% ± 8.1%, and 84.2% ± 7.5% for the lower, middle, and upper strata, respectively. The sensor was used more frequently in the population with a higher education level. Adherence to sensor use and SmartGuard retention were higher in patients who underwent the VDC follow-up. CONCLUSIONS: Medium-term follow-up of users of AHCL systems in a VDC contributes to safely achieving %TIR goals. Virtual diabetes clinic follow-up favored adherence to sensor use and continuous SmartGuard use. Socioeconomic strata were associated with a better glycemic profile and education level was associated with better adherence to sensor use.

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Several closed or hybrid loop controllers for Blood Glucose (BG) regulation, which are also known as Artificial Pancreas (AP) Systems or Automated Insulin Delivery systems (AIDs), are in development worldwide. Most AIDs are designed and evaluated for short-term performance, with a particular emphasis on the post-meal period. However, if controllers are not adapted properly to account for variations in physiology that affect Insulin Sensitivity (IS), the AIDs may perform inadequately. In this work, the performance of two Reinforcement Learning (RL) agents trained under both piecewise and continuous reward functions is evaluated in-silico for long-term adaptation of a Fully Automated Insulin Delivery (fAID) system. An automatic adaptive discretization scheme that expands the state space as needed is also implemented to avoid disproportionate state space exploration. The proposed agents are evaluated for long-term adaptation of the Automatic Regulation of Glucose (ARG) algorithm, considering variations in IS. Results show that both RL agents have improved performance compared to a rule-based decision-making approach and the baseline controller for the majority of the adult population. Moreover, the use of a continuous shaped reward function proves to enhance the performance of the agents further than a piecewise one.

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Closed-loop acoustic stimulation (CLAS) during sleep has shown to boost slow wave (SW) amplitude and spindle power. Moreover, sleep SW have been classified based on different processes of neuronal synchronization. Thus, different types of SW events may have distinct functional roles and be differentially affected by external stimuli. However, the SW synchronization processes affected by CLAS are not well understood. Here, we studied the effect of CLAS on the dissociation of SW events based on two features of neuronal synchronization in the electroencephalogram (topological spread and wave slope). We evaluated and classified individual SW events of 14 healthy subjects during a CLAS stimulated (STM) and a control night (CNT). Three main categories of SW events were found denoting (C1) steep slope SW with global spread, (C2) flat-slope waves with localized spread and homeostatic decline, and (C3) multipeaked flat-slope events with global spread. Comparing between conditions, we found a consistent increase of event proportion and trough amplitudes for C1 events during the time of stimulation. Furthermore, we found similar increases in post-stimulus spectral power in θ, ß, and σ frequencies for CNT vs STIM condition independently of sleep stage or SW categories. However, topological analysis showed differentiated spatial dynamics in N2 and N3 for SW categories and the co-occurrence with spindle events. Our findings support the existence of multiple types of SW with differential response to external stimuli and possible distinct neuronal mechanisms.

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BACKGROUND AND AIMS: Evidence supports the efficacy and safety of the Hybrid Close loop (HCL) system in patients with type 1 diabetes (T1D). However, limited data are available on the long-term outcomes of patients on HCL with telemedicine follow-up. METHODS: A prospective observational cohort study including T1D patients, who were upgrading to HCL system. Virtual training and follow-up were done through telemedicine. CGM data were analyzed to compare the baseline time in range (TIR), time below range (TBR), glycemic variability and auto mode (AM), with measurements performed at 3, 6 and 12 months. RESULTS: 134 patients were included with baseline A1c 7.6% ± 1.1. 40.5% had a severe hypoglycemia event in the last year. Baseline TIR, measured two weeks after starting AM was 78.6 ± 9.94%. No changes were evident at three (Mean difference - 0.15;CI-2.47,2.17;p = 0.96), six (MD-1.09;CI-3.42,1.24;p = 0.12) and 12 months (MD-1.30;CI-3.64,1.04;p = 0.08). No significant changes were found in TBR or glycemic variability throughout the follow-up. Use of AM was 85.6 ± 17.5% and percentage of use of sensor was 88.75 ± 9.5% at 12 months. No severe hypoglycemic (SH) events were reported. CONCLUSIONS: HCL systems allow to improve TIR, TBR and glycemic variability safely, early and sustained up to 1 year of follow-up in patients with T1D and high risk of hypoglycemia followed through telemedicine.

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BACKGROUND: The first two studies of an artificial pancreas (AP) system carried out in Latin America took place in 2016 (phase 1) and 2017 (phase 2). They evaluated a hybrid algorithm from the University of Virginia (UVA) and the automatic regulation of glucose (ARG) algorithm in an inpatient setting using an AP platform developed by the UVA. The ARG algorithm does not require carbohydrate (CHO) counting and does not deliver meal priming insulin boluses. Here, the first outpatient trial of the ARG algorithm using an own AP platform and doubling the duration of previous phases is presented. METHOD: Phase 3 involved the evaluation of the ARG algorithm in five adult participants (n = 5) during 72 hours of closed-loop (CL) and 72 hours of open-loop (OL) control in an outpatient setting. This trial was performed with an own AP and remote monitoring platform developed from open-source resources, called InsuMate. The meals tested ranged its CHO content from 38 to 120 g and included challenging meals like pasta. Also, the participants performed mild exercise (3-5 km walks) daily. The clinical trial is registered in ClinicalTrials.gov with identifier: NCT04793165. RESULTS: The ARG algorithm showed an improvement in the time in hyperglycemia (52.2% [16.3%] OL vs 48.0% [15.4%] CL), time in range (46.9% [15.6%] OL vs 50.9% [14.4%] CL), and mean glucose (188.9 [25.5] mg/dl OL vs 186.2 [24.7] mg/dl CL) compared with the OL therapy. No severe hyperglycemia or hypoglycemia episodes occurred during the trial. The InsuMate platform achieved an average of more than 95% of the time in CL. CONCLUSION: The results obtained demonstrated the feasibility of outpatient full CL regulation of glucose levels involving the ARG algorithm and the InsuMate platform.

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Studies suggest non-invasive transcutaneous auricular vagus nerve stimulation (taVNS) as a potential therapeutic option for various pathological conditions, such as epilepsy and depression. Exhalation-controlled taVNS, which synchronizes stimulation with internal body rhythms, holds promise for enhanced neuromodulation, but there is no closed-loop system in the literature capable of performing such integration in real time. In this context, the objective was to develop real-time signal processing techniques and an integrated closed-loop device with sensors to acquire physiological data. After a conditioning stage, the signal is processed and delivers synchronized electrical stimulation during the patient's expiratory phase. Additional modules were designed for processing, software-controlled selectors, remote and autonomous operation, improved analysis, and graphical visualization. The signal processing method effectively extracted respiratory cycles and successfully attenuated signal noise. Heart rate variability was assessed in real time, using linear statistical evaluation. The prototype feedback stimulator device was physically constructed. Respiratory peak detection achieved an accuracy of 90%, and the real-time processing resulted in a small delay of up to 150 ms in the detection of the expiratory phase. Thus, preliminary results show promising accuracy, indicating the need for additional tests to optimize real-time processing and the application of the prototype in clinical studies.

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INTRODUCTION: Automated insulin delivery (AID) systems, known as artificial pancreas or closed-loop glucose control systems, have been developed to improve the glycemic outcomes of people with type 1 diabetes. These systems use a control algorithm that automatically modifies the amount of insulin infused into a patient based on real-time blood glucose measurements. This study presents a summary of key clinical and technical issues related to the development of the first commercial AID systems and their evolution into commercial biomedical devices. AREAS COVERED: Highlights of each AID system are summarized through timelines, ranging from the definition of the core strategy of the control algorithm to the practical application and subsequent commercial approval. Tabulated information regarding the conducted main clinical studies is also presented. EXPERT OPINION: Insulin therapy has evolved up to the current commercial AID systems available, which have provided patients access to a safer and more effective therapy owing to automatic adjustments to insulin. However, this technology is relatively new and can be significantly improved. Limitations include the resistance of healthcare providers, high costs, and the availability of this treatment. The future of this technology is directed toward obtaining fully automatic control systems.

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Objective. Sharp wave-ripples (SWRs, 100-250 Hz) are oscillatory events extracellularly recorded in the CA1 subfield of the hippocampus during sleep and quiet wakefulness. Many studies employed closed-loop strategies to either detect and abolish SWRs within the hippocampus or manipulate other relevant areas upon ripple detection. However, the code and schematics necessary to replicate the detection system are not always available, which hinders the reproducibility of experiments among different research groups. Furthermore, information about performance is not usually reported. Here, we sought to provide an open-source, validated ripple detector for the scientific community.Approach. We developed and validated a ripple detection plugin integrated into the Open Ephys graphical user's interface. It contains a built-in movement detector based on accelerometer or electromyogram data that prevents false ripple events (due to chewing, grooming, or moving, for instance) from triggering the stimulation/manipulation device.Main results. To determine the accuracy of the detection algorithm, we first carried out simulations in MATLAB with real ripple recordings. Using a specific combination of detection parameters (amplitude threshold of 5 standard deviations above the mean, time threshold of 10 ms, and root mean square block size of 7 samples), we obtained a 97% true positive rate and 2.48 false positives per minute. Next, an Open Ephys plugin based on the same detection algorithm was developed, and a closed-loop system was set up to evaluate the round trip (ripple onset-to-stimulation) latency over synthetic data. The lowest latency obtained was 34.5 ± 0.5 ms. The embedded movement monitoring was effective in reducing false positives and the plugin's flexibility to detect pathological events was also verified.Significance. Besides contributing to increased reproducibility, we anticipate that the developed ripple detector plugin will be helpful for many closed-loop applications in the field of systems neuroscience.

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INTRODUCTION: Finding a goal of time in range (%TIR) that defines good glycemic control is necessary. Previous retrospective studies suggest good concordance between HbA1c ≤7% with a TIR >70%; however, the studies that included the largest number of patients used blood glucose measurement data with a follow-up time of less than 90 days. This study defined the TIR value that best discriminates HbA1c ≤7%. METHODS: We performed a prospective study of diagnostic tests based on a cohort of patients with type 1 diabetes (T1D) treated with a hybrid closed loop (HCL) followed for three months. The ability of %TIR to distinguish patients with HbA1c ≤7% was evaluated through receiver operating characteristic curve analysis. We determined the %TIR cutoff point with the best operating characteristics. RESULTS: A total of 118 patients were included (58.1% women, 47% overweight or obese, and 33% with high glycemic variability). A moderate negative correlation (R = -.54, P < .001) was found between %TIR and HBA1c. The discrimination ability was moderate, with an area under the curve of 0.7485 (95% confidence interval = 0.6608-0.8363). The cutoff point that best predicted HbA1c ≤7% was %TIR ≥75.5 (sensitivity 70%, specificity 67%). The findings were similar among those with a coefficient of variation (CV%) ≥36%. CONCLUSIONS: Our data suggest that the %TIR adequately identifies patients with HbA1c ≤7%. A target of TIR ≥75%, rather than the currently recommended TIR ≥70%, may be a more suitable value for optimal glycemic control.

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The most used approach in the motor rehabilitation of spinal cord injury is functional electrical stimulation. However, current devices do not provide real-time feedback, work in the closed-loop, and became remotely operable. In this scenario, this paper presents the development of an open access 4-channel IoT electrostimulator device with an inertial sensor. The electrostimulator circuit was designed with four modules: Boost Converter, H-bridge, Inertial Measurement Unit, and Processing Module. The firmware was implemented in the processing module to manage the modules to perform closed-loop stimulation (using PID controller). To perform the proof of concept of the device, a closed loop test was performed to control the ankle joint, performing the movements of dorsiflexion, plantar flexion, inversion, and eversion. The designed hardware allowed one to freely change the boost converter voltage and modulate the signal with 200 µs of pulse duration and 50 Hz of period in a safe and stable way. Furthermore, the controller was able to move the ankle joint in all desired directions following the reference values and respecting the imposed constraints. In general, the developed hardware was able to safely control a closed-loop joint.

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Acoustic stimulation synchronized to slow oscillations in scalp electroencephalograms has been shown to enhance sleep features, which makes it promising in boosting cognitive functions as well as in the treatment of some sleep disturbances. Nevertheless, scalp electrode sensors are resource intensive and poorly tolerated by sleeping patients. The aim of this study was to investigate the potential usability of in-the-ear electroencephalography to implement auditory closed-loop stimulation during sleep. For this, we evaluated the agreement between slow oscillation recordings obtained through the in-ear sensor and those obtained simultaneously from standard scalp electrodes during naps of 13 healthy subjects. We found that in-ear activity provided enough information to automatically detect sleep slow oscillations in real-time. Based on this, we successfully enhanced scalp slow oscillations using auditory single-cycle closed-loop brain-state-dependent stimulation based on in-ear signals acquired in 11 further subjects. We conclude that in-ear sensors provide a feasible technology for the enhancement of sleep patterns, and could pave the way for new clinical applications in the near future.

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