Monitor Brain Activity Research Articles

Enhancing Deep Sleep Induction Through a Wireless In-Ear EEG Device Delivering Binaural Beats and ASMR: A Proof-of-Concept Study

This study presents the development of a wireless in-ear EEG device designed to monitor brain activity during sleep and deliver auditory stimuli aimed at enhancing deep sleep. The device records EEG signals and plays a combined auditory stimulus consisting of autonomous sensory meridian response (ASMR) and 3 Hz binaural beats at a 60:30 dB ratio, intended to promote delta wave activity and non-rapid eye movement (NREM) stage 3 sleep. Fifteen participants completed this study, which included two consecutive nights: a baseline night and a testing night. Participants were divided into an experimental group, which received the combined ASMR and binaural beat stimulus, and a control group, which received only ASMR. The combined stimulus was delivered upon entering NREM stage 2 and replaced by ASMR when NREM stage 3 was reached. Results showed that the experimental group experienced an increase in NREM 3 sleep, a decrease in NREM 2 sleep, and a slight increase in NREM 3 latency compared to the baseline night. Although the findings are promising, further testing with a larger sample size is required to confirm the device’s potential to enhance sleep quality and promote delta activity in the brain.

The Stigma Machine: A Study of the Prosocial Impact of Immersive VR Narratives on Youth in Spain and Canada

Virtual reality (VR) is increasingly employed to create immersive, interactive audiovisual narratives that accentuate emotion, storytelling, and user engagement. By harnessing the potential of VR, these avant-garde narratives aim to instill values of equity, justice, and fairness. This article critically examines the largely unsubstantiated assertion that VR is the ultimate tool for fostering empathy by means of a qualitative evaluation of the influence of prosocial VR audiovisual narratives. The study involved the production of the first episode of <em>The Stigma Machine</em>, a VR short film series in both traditional 2D and immersive VR formats, in a two-pronged production approach designed to examine the effects of the film on a sample of 44 university students from Spain (<em>n</em> = 22) and Canada (<em>n</em> = 22). The participants were segregated into two groups: Group 1 (1st VR Condition) viewed the VR experience first, followed by the traditional version, while Group 2 (1st Video Condition) viewed the two formats in reverse order. Data was collected before, during, and after viewing, using standardized questionnaires (interpersonal reactivity index, basic empathy scale, and Igroup presence questionnaire) and electroencephalogram devices to monitor brain activity. The dependent variables included: empathy, assessed using the interpersonal reactivity index and basic empathy scale surveys; electroencephalogram brain activity measures, indicating engagement, excitement, focus, interest, relaxation, and stress; presence, evaluated using the Igroup presence questionnaire; and various outcome variables. The results reveal no significant differences in presence and no significant changes to the empathy scores. The findings point to a need to focus more on narrative design and audiovisual content creation strategies than on VR technology itself.

Hybrid Functional Near-Infrared Spectroscopy System and Electromyography for Prosthetic Knee Control.

The increasing number of individuals with limb loss worldwide highlights the need for advancements in prosthetic knee technology. To improve control and quality of life, integrating brain-computer communication with motor imagery offers a promising solution. This study introduces a hybrid system that combines electromyography (EMG) and functional near-infrared spectroscopy (fNIRS) to address these limitations and enhance the control of knee movements for individuals with above-knee amputations. The study involved an experiment with nine healthy male participants, consisting of two sessions: real execution and imagined execution using motor imagery. The OpenBCI Cyton board collected EMG signals corresponding to the desired movements, while fNIRS monitored brain activity in the prefrontal and motor cortices. The analysis of the simultaneous measurement of the muscular and hemodynamic responses demonstrated that combining these data sources significantly improved the classification accuracy compared to using each dataset alone. The results showed that integrating both the EMG and fNIRS data consistently achieved a higher classification accuracy. More specifically, the Support Vector Machine performed the best during the motor imagery tasks, with an average accuracy of 49.61%, while the Linear Discriminant Analysis excelled in the real execution tasks, achieving an average accuracy of 89.67%. This research validates the feasibility of using a hybrid approach with EMG and fNIRS to enable prosthetic knee control through motor imagery, representing a significant advancement potential in prosthetic technology.

Characteristic activation pattern and network connectivity of prefrontal cortex in patients with type 2 diabetes mellitus and major depressive disorder during a verbal fluency task: A functional near-infrared spectroscopy study based on network-based statistic prediction.

Type 2 diabetes (T2DM) and major depressive disorder (MDD) together occur frequently among the elderly population. However, the inconsistency in assessments and limited medical resources in the community make it challenging to identify depression in patients with T2DM. This cross-sectional study aimed to investigate the activation pattern and network connectivity of prefrontal cortex (PFC) during a verbal fluency task (VFT) in patients with T2DM and MDD using functional near-infrared spectroscopy (fNIRS). Three parallel groups (T2DM with MDD group, T2DM group, and healthy group) with 100 participants in each group were included in the study. Recruitment took place from August 1, 2020, to December 31, 2023. Due to the close association between the PFC and depressive emotions, fNIRS was used to monitor brain activation and network connectivity of PFC in all participants during a task of Chinese-language phonological VFT. Network-based statistic prediction (NBS-predict) was adopted as data analysis method. Patients in the T2DM with MDD group showed characteristic activation pattern and network connectivity in contrast with patients with T2MD and healthy controls, including decreased activation in PFC, and decreased network connectivity of right dorsolateral prefrontal cortex (DLPFC). Furthermore, the network connectivity of the right DLPFC in patients with T2DM and MDD was negatively correlated with scores of Hamilton Depression Scale-24 (HAMD-24). There was a distinctive activation pattern and network connectivity of the prefrontal cortex in patients with T2DM and MDD. The right DLPFC could serve as a potential target for the diagnosis and intervention of MDD in patients with T2DM.

MRI and CT compatible asymmetric bilayer hydrogel electrodes for EEG-based brain activity monitoring

The exploration of multi-dimensional brain activity with high temporal and spatial resolution is of great significance in the diagnosis of neurological disease and the study of brain science. Although the integration of electroencephalogram (EEG) with magnetic resonance imaging (MRI) and computed tomography (CT) provides a potential solution to achieve a brain-functional image with high spatiotemporal resolution, the critical issues of interface stability and magnetic compatibility remain challenging. Therefore, in this research, we proposed a conductive hydrogel EEG electrode with an asymmetrical bilayer structure, which shows the potential to overcome the challenges. Benefiting from the bilayer structure with different moduli, the hydrogel electrode exhibits high biological and mechanical compatibility with the heterogeneous brain-electrode interface. As a result, the impedance can be reduced compared with conventional metal electrodes. In addition, the hydrogel-based ionic conductive electrodes, which are free from metal conductors, are compatible with MRI and CT. Therefore, they can obtain high spatiotemporal resolution multi-dimensional brain information in clinical settings. The research outcome provides a new approach for establishing a platform for early diagnosis of brain diseases and the study of brain science.

Comparative exploration on EEG signal filtering using window control methods

Electroencephalography (EEG) is used to monitor brain activity. The brain signals consist of different frequency band signals delta, theta, alpha, beta, and gamma waves. The signals are affected by external noise which reduces the quality of the EEG signal due to which it becomes difficult to do further processing of EEG signals like feature extraction or extraction of meaningful features from EEG signal. Therefore, it becomes important to filter the noise from the EEG signal before feature extraction or classification of the EEG signal. The research article presents an overview of different types of windowing filter techniques like Rectangular, Bartlett, Hamming, Hanning, and Kaiser windows applied for finite impulse response (FIR) behavior which is used for EEG signal processing for different brain waves processed in different frequency bands. The comparative analysis is carried out in terms of the response time of brain frequency bands for different windowing filter techniques using the MATLAB 2023 signal processing simulation tool. The novelty of the work lies in estimating minimum latency and appropriate filter selection for various typical EEG waves, since the EEG signals are pre-supposed in the hardware chip design, noise elimination is the first step in high-performance computing applications. The Bartlett window band stop has an optimal response time of 12.666 s for delta waves, a highpass filter with a response time of 16.187 s for theta waves, a bandpass with a response time of 13.122 s for alpha waves, a highpass filter with a response time of 17.866 s for beta waves, and a highpass filter with a response time of 13.797 s for gamma waves. The Barlett window FIR filter is well-suited for EEG applications.

Transparent MXene Microelectrode Arrays for Multimodal Mapping of Neural Dynamics.

Transparent microelectrode arrays have proven useful in neural sensing, offering a clear interface for monitoring brain activity without compromising high spatial and temporal resolution. The current landscape of transparent electrode technology faces challenges in developing durable, highly transparent electrodes while maintaining low interface impedance and prioritizing scalable processing and fabrication methods. To address these limitations, we introduce artifact-resistant transparent MXene microelectrode arrays optimized for high spatiotemporal resolution recording of neural activity. With 60% transmittance at 550 nm, these arrays enable simultaneous imaging and electrophysiology for multimodal neural mapping. Electrochemical characterization shows low impedance of 563 ± 99 kΩ at 1 kHz and a charge storage capacity of 58 mC cm⁻² without chemical doping. In vivo experiments in rodent models demonstrate the transparent arrays' functionality and performance. In a rodent model of chemically-induced epileptiform activity, we tracked ictal wavefronts via calcium imaging while simultaneously recording seizure onset. In the rat barrel cortex, we recorded multi-unit activity across cortical depths, showing the feasibility of recording high-frequency electrophysiological activity. The transparency and optical absorption properties of Ti₃C₂Tx MXene microelectrodes enable high-quality recordings and simultaneous light-based stimulation and imaging without contamination from light-induced artifacts.

Region of interest determination algorithm of lensless calcium imaging datasets.

Advances in fluorescence imaging technology have been crucial to the progress of neuroscience. Whether it was specific expression of indicator proteins, detection of neurotransmitters, or miniaturization of fluorescence microscopes, fluorescence imaging has improved upon electrophysiology, the gold standard for monitoring brain activity, and enabled novel methods to sense activity in the brain. Hence, we developed a lightweight and compact implantable CMOS-based lensless Ca2+ imaging device for freely moving transgenic G-CaMP mouse experiments. However, without a lens system, determination of regions of interest (ROI) has proven challenging. Localization of fluorescence activity and separation of signal from noise are difficult. In this study, we report an ROI selection method using a series of adaptive binarizations with a gaussian method and morphological image processing. The parameters for each operation such as the kernel size, sigma and footprint size were optimized. We then validated the utility of the algorithm with simulated data and freely moving nociception experiments using the lensless devices. The device was implanted in the dorsal raphe nucleus to observe pain-related brain activity following a formalin test to stimulate pain. We observed significant increases in fluorescence activity after formalin injection compared to the control group when using the ROI determination algorithm.

Signal quality evaluation of an in-ear EEG device in comparison to a conventional cap system.

Wearable in-ear electroencephalographic (EEG) devices hold significant promise for integrating brain monitoring technologies into real-life applications. However, despite the introduction of various in-ear EEG systems, there remains a necessity for validating these technologies against gold-standard, clinical-grade devices. This study aims to evaluate the signal quality of a newly developed mobile in-ear EEG device compared to a standard scalp EEG system among healthy volunteers during wakefulness and sleep. The study evaluated an in-ear EEG device equipped with dry electrodes in a laboratory setting, recording a single bipolar EEG channel using a cross-ear electrode configuration. Thirty healthy participants were recorded simultaneously using the in-ear EEG device and a conventional EEG cap system with 64 wet electrodes. Based on two recording protocols, one during a resting state condition involving alternating eye opening and closure with a low degree of artifact contamination and another consisting of a daytime nap, several quality measures were used for a quantitative comparison including root mean square (RMS) analysis, artifact quantification, similarities of relative spectral power (RSP), signal-to-noise ratio (SNR) based on alpha peak criteria, and cross-signal correlations of alpha activity during eyes-closed conditions and sleep activities. The statistical significance of our results was assessed through nonparametric permutation tests with False Discovery Rate (FDR) control. During the resting state, in-ear and scalp EEG signals exhibited similar fluctuations, characterized by comparable RMS values. However, intermittent signal alterations were noticed in the in-ear recordings during nap sessions, attributed to movements of the head and facial muscles. Spectral analysis indicated similar patterns between in-ear and scalp EEG, showing prominent peaks in the alpha range (8-12 Hz) during rest and in the low-frequency range during naps (particularly in the theta range of 4-7 Hz). Analysis of alpha wave characteristics during eye closures revealed smaller alpha wave amplitudes and slightly lower signal-to-noise ratio (SNR) values in the in-ear EEG compared to scalp EEG. In around 80% of cases, cross-correlation analysis between in-ear and scalp signals, using a contralateral bipolar montage of 64 scalp electrodes, revealed significant correlations with scalp EEG (p < 0.01), particularly evident in the FT11-FT12 and T7-T8 electrode derivations. Our findings support the feasibility of using in-ear EEG devices with dry-contact electrodes for brain activity monitoring, compared to a standard scalp EEG, notably for wakefulness and sleep uses. Although marginal signal degradation is associated with head and facial muscle contractions, the in-ear device offers promising applications for long-term EEG recordings, particularly in scenarios requiring enhanced comfort and user-friendliness.

Continuous Electroencephalographic Monitoring in Adult ICU Challenges and Potential Solutions: A Scoping Review

Background: Continuous electroencephalographic (cEEG) monitoring is continuous monitoring of brain activity that is often used in the intensive care unit (ICU). Recently, the usage of cEEG has grown in many institutions as it detects non-convulsive seizures better than routine EEG (rEEG). However, due to the aggressive environment of ICU, the application and the prolonged recording is challenging. The objectives of this scoping review are to evaluate the common challenges faced during cEEG in adult ICU and proper solutions for optimal implementation. Methods: A scoping review was conducted using Arksey and O’Malley framework. The included articles contain “continuous EEG in ICU settings” in their titles, written in English, and published between 2010 and 2022. That was by searching the databases: PubMed, Google Scholar, and Summon. Results: 14 articles were included as they meet the criteria of this review. (28.57 %) of the included studies mentioned the challenges regarding staff, (42.85 %) addressed the challenges of electrode application and the alternative techniques in ICU, and (42.85%) discussed the difficulties in interpretation and duration of the recording. Conclusion: Continuous EEG is increasingly recognized as valuable mean of monitoring cerebral function, but it is faced with numerous challenges: the difficult environment of ICU and the type of critical patient admitted requiring the optimal electrode application and maintenance, minimizing artifacts and adequate duration of recording to detect abnormalities, and sufficiently trained personnel including ICU staff, technologist and neurophysiologist to provide the optimal patient care as it is the main goal.

Is deep brain imaging on the brink of transformation with a bioluminescence molecule?

AbstractCephalofurimazine (CFz), when paired with Antares luciferase, shows superior blood‐brain barrier permeability and enhanced imaging depth and clarity for deep brain imaging. This bioluminescence provides a less invasive method for real‐time monitoring of deep brain activity, with the potential to advance targeted therapies and deepen our understanding of brain functions. Further molecular engineering and localized delivery can reduce the potential toxicity of CFz and enhance its efficacy for clinical deep brain imaging.

Immediate and Transient Perturbances in EEG Within Seconds Following Controlled Soccer Head Impact.

Athletes in contact and collision sports can sustain frequent subconcussive head impacts. Although most impacts exhibit low kinematics around or below 10g of head linear acceleration, there is growing concern regarding the cumulative effects of repetitive sports head impacts. Even mild impacts can lead to brain deformations as shown through neuroimaging and finite element modeling, and thus may result in mild and transient effects on the brain, prompting further investigations of the biomechanical dose-brain response relationship. Here we report findings from a novel laboratory study with continuous monitoring of brain activity through electroencephalography (EEG) during controlled soccer head impacts. Eight healthy participants performed simulated soccer headers at 2 mild levels (6g, 4rad/s and 10g, 8rad/s) and three directions (frontal, oblique left, oblique right). Participants were instrumented with an inertial measurement unit (IMU) bite bar and EEG electrodes for synchronized head kinematics and brain activity measurements throughout the experiment. After an impact, EEG exhibited statistically significant elevation of relative and absolute delta power that recovered within two seconds from the impact moment. These changes were statistically significantly higher for 10g impacts compared with 6g impacts in some topographical regions, and oblique impacts resulted in contralateral delta power increases. Post-session resting state measurements did not indicate any cumulative effects. Our findings suggest that even mild soccer head impacts could lead to immediate, transient neurophysiological changes. This study paves the way for further dose-response studies to investigate the cumulative effects of mild sports head impacts, with implications for long-term athlete brain health.

Utility of Neonatal Pain, Agitation and Sedation Scale and Amplitude Integrated Electroencephalogram for Detection of Different Levels of Sedation in NICUs

Abstract Background Sedatives are essential drugs in every intensive care unit used to ensure the patient's optimal level of comfort. Avoiding conditions of over and under sedation is a challenge in a neonatal intensive care setting. To date, there are no definitions of optimal levels of sedation in neonates. Testing the hypothesis that if a combined use of Neonatal Pain, Agitation and Sedation Scale (N- PASS) and amplitude integrated electroencephalography (aEEG) score might be helpful to distinguish different levels of neonatal sedation and therefore useful in titration of the drugs used. Aim of the Work The primary aim of the present study is to assess Neonatal Pain, Agitation and Sedation Scale (N-PASS) and amplitude integrated electroencephalography (aEEG) in the detection of different levels of neonatal sedation. Patients and Methods This was prospective observational study conducted on 30 full term neonates on mechanical ventilation reciving sedation (midazolam,fentanyl). we divided our patients into two groups (lightly sedated and deeply sedated patients) according to dose of sedation which determined by the expert opinion according to the clinical condition of the patients. all patients were subjected to the following:(demographic data, clinical signs &amp; symptoms, laboratory investigations, N-PASS and aEEG before and after sedation from march 2021 to August 2022 to evaluate N-PASS and aEEG in the detection of different levels of neonatal sedation. Results In our study which was conducted on 30 patients admitted to NICU at Ain shams university hospitals, our patients were of mean gestational age 38.34, all patients presented with respiratory distress, were mechanically ventilated and received sedation (midazolam, fentanyl). we divided our patients into two groups (lightly sedated patients who were 11 patients (36.7%), deeply sedated patients who were 19 patients ( 63.3% ) according to dose of sedation. There was a significant decrease of co2 level in deeply sedated patients, mean co2 after deep sedation 43.36 mmHg while mean co2 after light sedation 51.52 mmHg (p 0.034),and regarding N-PASS significant decrease in N-PASS total score after sedation (p &amp;lt; 0.001 ),and regarding aEEG significant decrease of total burdjalov score after sedation (p &amp;lt; 0.001 ) Conclusion We recommend combining a clinical scoring system such as the N-PASS, with a continuous brain activity monitor such as the aEEG for assessing the level of sedation in neonates.

Manufacturing of an electrocardiographic sensor and its implantation technique in rats for long-term monitoring of brain activity

Manufacturing of an electrocardiographic sensor and its implantation technique in rats for long-term monitoring of brain activity

Mobile application-based phonetic training facilitates Chinese-English learners’ learning of L2

BackgroundNumerous studies have claimed that high variability phonetic training (HVPT) has significant efficacy in improving learners’ phonological decoding ability. Although mobile learning (ML) based HVPT program integrated with sound instructional design also demonstrated the effectiveness, its reliability and validity need to be further verified. AimsThis study evaluated the training's efficacy in improving Chinese learners' perception and production of English words by comparing learners' brain activation changes and network connectivity changes because of the training. SamplesParticipants were 40 undergraduate students. MethodsThis study developed a Wechat application to implement a self-regulated high-variability-phonetic-training (HVPT) for 20 university students; employed a modified phonics screening test (MPST) as a benchmark test to assess learners’ phonetic decoding ability before and after the training; designed a quasi-experiment (control/experimental design) for 40 participants; most importantly, this study used a functional near infrared spectroscopy (fNIRS) device to monitor brain activation and networks synchronisation for participants in both groups before and after training. ResultsThe results showed that learners' performance on 5-phoneme pseudowords was significantly improved. The training inhibited learners’ right fusiform gyrus. A significant correlation was observed between enhanced RMTG and increased testing scores. ConclusionThe transfer effect of Mobile MVPT on long pseudowords is significant, suggesting its efficacy in helping learners apply the phonetic patterns perceived from the training to segmentally analyse new difficult words.

Evaluating the Electroencephalographic Signal Quality of an In-Ear Wearable Device.

Wearable in-ear electroencephalographic (EEG) devices hold significant promise for advancing brain monitoring technologies into everyday applications. However, despite the current availability of several in-ear EEG devices in the market, there remains a critical need for robust validation against established clinical-grade systems. In this study, we carried out a detailed examination of the signal performance of a mobile in-ear EEG device from Naox Technologies. Our investigation had two main goals: firstly, evaluating the hardware circuit's reliability through simulated EEG signal experiments and, secondly, conducting a thorough comparison between the in-ear EEG device and gold-standard EEG monitoring equipment. This comparison assesses correlation coefficients with recognized physiological patterns during wakefulness and sleep, including alpha rhythms, eye artifacts, slow waves, spindles, and sleep stages. Our findings support the feasibility of using this in-ear EEG device for brain activity monitoring, particularly in scenarios requiring enhanced comfort and user-friendliness in various clinical and research settings.

Sleep Disorder Detection Using EEG Signals

Sleep disorders are prevalent health concerns affecting millions of individuals worldwide, with adverse impacts on overall well-being and cognitive function. Detecting and diagnosing these disorders accurately is crucial for effective treatment planning and management. This project focuses on utilizing Electroencephalogram (EEG) signals, a non-invasive method for monitoring brain activity, to detect sleeping disorders. By leveraging advanced signal processing techniques and machine learning algorithms, this research aims to develop a robust and accurate system capable of identifying various types of sleep disorders, such as insomnia, sleep apnea, and narcolepsy, based on EEG data. The proposed approach holds the potential to enhance early detection, personalized treatment strategies, and ultimately improve the quality of life for individuals affected by sleep disorders. Keywords-Ambulatory EEG, automatic scoring, deep learning, electroencephalography, sleep staging.

Short-term meditation training alters brain activity and sympathetic responses at rest, but not during meditation

Although more people are engaging in meditation practices that require specialized training, few studies address the issues associated with nervous activity pattern changes brought about by such training. For beginners, it remains unclear how much practice is needed before objective physiological changes can be detected, whether or not they are similar across the novices and what are the optimal strategies to track these changes. To clarify these questions we recruited individuals with no prior meditation experience. The experimental group underwent an eight-week Taoist meditation course administered by a professional, while the control group listened to audiobooks. Both groups participated in audio-guided, 34-min long meditation sessions before and after the 8-week long intervention. Their EEG, photoplethysmogram, respiration, and skin conductance were recorded during the mediation and resting state periods. Compared to the control group, the experimental group exhibited band-specific topically organized changes of the resting state brain activity and heart rate variability associated with sympathetic system activation. Importantly, no significant changes were found during the meditation process prior and post the 8-week training in either of the groups. The absence of notable changes in CNS and ANS activity indicators during meditation sessions, for both the experimental and control groups, casts doubt on the effectiveness of wearable biofeedback devices in meditation practice. This finding redirects focus to the importance of monitoring resting state activity to evaluate progress in beginner meditators. Also, 16 h of training is not enough for forming individual objectively different strategies manifested during the meditation sessions. Our results contributed to the development of tools to objectively monitor the progress in novice meditators and the choice of the relevant monitoring strategies. According to our findings, in order to track early changes brought about by the meditation practice it is preferable to monitor brain activity outside the actual meditation sessions.

An electroencephalogram microdisplay to visualize neuronal activity on the brain surface.

Functional mapping during brain surgery is applied to define brain areas that control critical functions and cannot be removed. Currently, these procedures rely on verbal interactions between the neurosurgeon and electrophysiologist, which can be time-consuming. In addition, the electrode grids that are used to measure brain activity and to identify the boundaries of pathological versus functional brain regions have low resolution and limited conformity to the brain surface. Here, we present the development of an intracranial electroencephalogram (iEEG)-microdisplay that consists of freestanding arrays of 2048 GaN light-emitting diodes laminated on the back of micro-electrocorticography electrode grids. With a series of proof-of-concept experiments in rats and pigs, we demonstrate that these iEEG-microdisplays allowed us to perform real-time iEEG recordings and display cortical activities by spatially corresponding light patterns on the surface of the brain in the surgical field. Furthermore, iEEG-microdisplays allowed us to identify and display cortical landmarks and pathological activities from rat and pig models. Using a dual-color iEEG-microdisplay, we demonstrated coregistration of the functional cortical boundaries with one color and displayed the evolution of electrical potentials associated with epileptiform activity with another color. The iEEG-microdisplay holds promise to facilitate monitoring of pathological brain activity in clinical settings.

Noninvasive monitoring technologies to identify discomfort and distressing symptoms in persons with limited communication at the end of life: a scoping review

BackgroundDiscomfort and distressing symptoms are common at the end of life, while people in this stage are often no longer able to express themselves. Technologies may aid clinicians in detecting and treating these symptoms to improve end-of-life care. This review provides an overview of noninvasive monitoring technologies that may be applied to persons with limited communication at the end of life to identify discomfort.MethodsA systematic search was performed in nine databases, and experts were consulted. Manuscripts were included if they were written in English, Dutch, German, French, Japanese or Chinese, if the monitoring technology measured discomfort or distressing symptoms, was noninvasive, could be continuously administered for 4 hours and was potentially applicable for bed-ridden people. The screening was performed by two researchers independently. Information about the technology, its clinimetrics (validity, reliability, sensitivity, specificity, responsiveness), acceptability, and feasibility were extracted.ResultsOf the 3,414 identified manuscripts, 229 met the eligibility criteria. A variety of monitoring technologies were identified, including actigraphy, brain activity monitoring, electrocardiography, electrodermal activity monitoring, surface electromyography, incontinence sensors, multimodal systems, and noncontact monitoring systems. The main indicators of discomfort monitored by these technologies were sleep, level of consciousness, risk of pressure ulcers, urinary incontinence, agitation, and pain. For the end-of-life phase, brain activity monitors could be helpful and acceptable to monitor the level of consciousness during palliative sedation. However, no manuscripts have reported on the clinimetrics, feasibility, and acceptability of the other technologies for the end-of-life phase.ConclusionsNoninvasive monitoring technologies are available to measure common symptoms at the end of life. Future research should evaluate the quality of evidence provided by existing studies and investigate the feasibility, acceptability, and usefulness of these technologies in the end-of-life setting. Guidelines for studies on healthcare technologies should be better implemented and further developed.