Mindful Self-Compassion (MSC) programs have demonstrated benefits for psychological well-being, yet their neurophysiological mechanisms remain underexplored, particularly in naturalistic settings using portable electroencephalography (EEG). This study examined the effects of a six-week MSC program, Embracing Your Life (EYL), on stress response, brain activity, and self-compassion among Chinese university students. A quasi-experimental design was used with 62 participants (31 in the experimental group and 31 in the control group; mean age = 26.6years). The experimental group participated in six weekly 1.5-h EYL sessions, while the control group received no intervention. EEG data were collected using a portable headband (channels AF7, AF8) during baseline and post-intervention, involving three meditation tasks: resting state, mindfulness breathing, and a self-compassion break. Power spectral density (PSD) was calculated, and self-reported measures included self-compassion (SCS-SF), perceived stress (PSS), resilience (CD-RISC), mindfulness attention (MAAS), and well-being (SWEMWBS). Generalized estimating equations were used to analyze the data, controlling for baseline differences. The experimental group showed significant improvements in self-compassion (MD = + 0.28, p = 0.003), mental well-being (+ 1.75, p < 0.001), and perceived stress (- 3.48, p < 0.001), with a marginal increase in resilience (+ 2.88, p = 0.045). EEG analyses revealed condition-specific reductions in alpha and beta power during mindfulness and self-compassion tasks, with significant Group × Time interactions (e.g., alpha: Wald χ2 = 14.55, p < 0.001; Wald χ2 = 225.34, p < 0.001). Theta activity showed nuanced patterns, with suppression during mindfulness and partial recovery during self-compassion. While during the intervention, formal practice segments exhibited the lowest power values across all frequency bands, indicating deeper meditative engagement and enhanced neural efficiency. The EYL program improved psychological outcomes and induced distinct EEG changes. These findings support the feasibility of portable EEG devices for monitoring mindfulness-based interventions and suggest that self-compassion practices engage in unique neural mechanisms beyond traditional mindfulness meditation. Future research should employ larger samples and multi-channel EEG to validate these results.

Background/Objectives: Mindfulness-based interventions are widely used to reduce psychological distress. Their long-term neurophysiological correlates remain insufficiently characterized. Using a portable Muse InteraXon® EEG device, this study aimed to evaluate (i) the extent to which a 12-month combined mindfulness and gratitude-based intervention reduces anxiety, depression, and perceived stress, and (ii) whether these changes are accompanied by corresponding EEG-derived neurophysiological alterations, exploring longitudinal brain-behavior associations. Methods: Fifty participants completed psychological assessments at baseline, 6 months, and 12 months using validated scales (BDI-II, DASS-21, EMAS). A subcohort of 25 participants also underwent EEG recordings with a portable Muse device at the same time points. Longitudinal changes were analyzed using linear mixed-effect models, and exploratory brain-behavior associations were assessed with change-score analyses and Spearman's correlations with false discovery rate correction. Results: Across the full cohort (n = 50), psychological outcomes showed longitudinal improvements over 12 months, with reductions in BDI-21, DASS-21 depression, anxiety, and stress subscales, and EMAS-State scores (all p < 0.001; linear mixed-effect models). In the EEG subcohort (n = 25), longitudinal analyses showed increased alpha power and reduced beta and gamma power in frontal and temporoparietal regions (pFDR < 0.05), along with a modest decrease in delta power at 12 months, while theta power remained stable. Exploratory analyses showed non-significant trends in the hypothesized directions that did not remain statistically significant after correction for multiple comparisons (e.g., Δalpha vs. Δstate anxiety: ρ ≈ -0.44; Δbeta vs. Δdepression: ρ ≈ 0.43) or after FDR correction. Conclusions: The mindfulness- and gratitude-based intervention was associated with sustained improvements in psychological outcomes and suggests accompanying dynamic modulation of neurophysiology. EEG appears to reflect time-dependent neural adaptation rather than a static predictor of treatment response.

Objective. Silent speech decoding (SSD) offers a potential communication alternative for individuals with impaired vocalization. However, conventional multi-electrode electroencephalography (EEG) or facial electromyography (EMG) systems require cumbersome preparation and are unsuitable for daily use. This study evaluates the practicality of SSD using a wearable around-ear EEG device, focusing on data scaling, cross-subject transfer, vocabulary extensibility, and online decoding performance.Approach. We collected 72 h of around-ear EEG from 24 healthy participants and one individual with incomplete locked-in syndrome (LIS) during silent, vocalized, and attempted speech, and integrated these around-ear EEG recordings with prior EMG + high-density EEG datasets, yielding 282.4 total h of training data. Using a 64-word classification task as the evaluation metric, we assessed: (1) whether larger datasets improve around-ear EEG-based SSD, (2) whether healthy-participant data supplement limited LIS-participant data despite articulatory differences, (3) transferability to unseen vocabulary, and (4) online user-interface performance.Main results. Large-scale EEG/EMG data improved SSD accuracy in both healthy participants and the LIS participant. Training on the heterogeneous dataset achieved 56.6% accuracy for healthy users and 47.3% for the LIS participant. Fine-tuning this decoder for new vocabulary increased the accuracy by 22 percentage points relative to training from scratch. Regression analysis showed that, for decoding in the LIS participant, data from the LIS participant contributed approximately four times the weight of healthy-participant data, quantifying data strategies for SSD. Online experiments achieved top-1/top-5 accuracies of 47.2%/76.0% for healthy users and 26.5%/49.1% for the LIS participant.Significance. The results indicate that lightweight, commercially feasible around-ear EEG can enable practical SSD when combined with large-scale healthy-participant data, supporting online operation. Moreover, models trained on a 64-word vocabulary facilitate decoding of a new vocabulary, providing a path toward SSD systems requiring minimal LIS-participant data. This study advances non-invasive SSD systems suitable for everyday communication.

Wearable electroencephalography (EEG) devices offer a promising solution for continuous brain monitoring outside laboratory settings. However, maintaining stable signal quality over extended periods remains challenging, as skin-mounted EEG systems are prone to contact-induced noise caused by skin deformation and body motion. Here, we present a wrinkle-adaptive kirigami structure and a soft, wearable EEG patch engineered to match subject-specific forehead wrinkle patterns, thereby stabilizing the electrode-skin interface. Our two-step kirigami architecture integrates global conformability with localized strain accommodation, delivering anisotropic deformability and maintaining mechanical stability during facial motion. Leveraging an automated image-based workflow, we enable scalable, individualized generation of kirigami patterns. When integrated into a soft, wireless wearable system, the personalized patch delivers consistently enhanced signal-to-noise ratios across multiple EEG frequency bands, even under diverse motion conditions in at-home sleep settings. This technology demonstrates broad applicability for sleep EEG monitoring and underscores the potential of morphology-aware structural design for next-generation wearable EEG devices.

The use of electroencephalogram (EEG) to gain insight into cognitive and metacognitive processing during task execution is being pioneered in natural learning contexts; an opportunity not without its challenges. Accordingly, a pilot study was conducted to explore the feasibility of this approach. The aims of this study were: (1) to demonstrate how raw data extracted from an EEG device may be processed; (2) to determine whether there were differences in pre-task cognitive load between senior university students (Group 1), novice university teachers (Group 2) and experienced university teachers (Group 3); (3) To determine whether the peak power (μV2) per brain band (Delta, Theta, Alpha, Beta and Gamma) recorded during task performance was different depending on the type of participant; (4) To determine whether there were un-labelled groupings (clusters), and whether they corresponded to the type of participant. The raw data were processed using the MNE-Python toolkit. No significant differences were found in the perception of cognitive load or in peak power with respect to participant type. However, different frequencies of maximum activation of brain channels in the Delta wave were found by participant type. The largest overlaps were found between Group 1 and Group 2. Future studies will address the influence of other variables such as age, gender, type of studies and cranial tomography. In addition, 3D analyses with integration of brain surfaces and sensors will be applied.

Worker fatigue is one of the most significant risks in hazardous industries such as construction. Traditional fatigue detection methods, which rely on subjective measures, are prone to bias and can interrupt work. Electroencephalography (EEG) signals have increasingly been used for the objective detection of fatigue, as they offer informative and rich data. However, their use has primarily been limited to detection of mental fatigue. This study examines whether EEG signals can support a unified fatigue detection model trained on both physical-task and mental-task segments. The study also further explores the performance of a fatigue detection model with a reduced number of EEG channels. To achieve these two objectives, a dataset consisting of objective EEG recordings combined with subjective fatigue surveys collected from 12 participants was used. The most important features were extracted using a systematic feature-selection approach. A Support Vector Machine (SVM) was then employed for the classification of physical and mental fatigue. To reduce the number of EEG channels, fifteen models representing different channel combinations were trained and tested using leave-one-subject-out cross-validation. The performance of each model was assessed using a custom performance score to determine the best combination of channels. The results showed that a two-channel model (TP9, AF7) achieved the best performance, with a score of 84. These findings provide important evidence for the development of purpose-built EEG devices with fewer channels capable of detecting both physical and mental fatigue.

IntroductionSleep quality is often thought to be a key determinant of cognitive performance, particularly in older adults who experience age-related changes in sleep architecture. However, the extent to which nightly variations in sleep quality impact next-day cognitive performance remains unclear—in part because it has only recently become practical to measure sleep over multiple nights.MethodsIn this study, we used an in-home wearable electroencephalography (EEG) device to monitor sleep patterns over ~10 nights in 17 healthy older adults, assessing metrics of sleep quality such as wake after sleep onset and the density of slow oscillations and sleep spindles. Next-day cognitive performance was evaluated using two computerized neuropsychological tasks measuring executive functions (inhibition and cognitive flexibility), and their relationships to sleep metrics were explored.ResultsAlthough participants placed the EEG device themselves, a high proportion of sleep data was usable (~71%), and clear nightly variations in sleep quality were captured. Sleep recordings showed considerable variability in sleep quality metrics across nights, with large inter-individual differences. However, we found no effects of either macro- or microarchitectural sleep metrics on executive task outcomes the following day.DiscussionThese results do not rule out the possibility that some aspects of cognitive performance may be affected by daily fluctuations in sleep quality; however, they suggest that inhibition and cognitive flexibility, which underlie reasoning and problem solving, may be relatively resilient to nightly sleep variability in older adults. The findings also demonstrate the feasibility of using emerging portable devices to extend sleep studies at home and over multiple nights in older adults, while providing variance estimates and effect sizes to guide power and sample size planning for future studies.

An open electroencephalography (EEG) dataset is presented for evaluating consumer-grade EEG devices in comparison with a research-grade EEG device. Datasets were collected from 30 participants using four consumer-grade EEG devices (BrainLink Pro, NeuroNicle FX2, MindWave Mobile 2, Muse 2), including two single-channel headsets (BrainLink Pro and MindWave Mobile 2), and one research-grade EEG device (DSI-24). Each participant completed four experimental paradigms: eye blinks, jaw clenching, head movements with eyes open, and head movements with eyes closed, with the resting-state EEG data recorded before and after each task. The dataset enables the validation of device signal quality, the assessment of neural features such as alpha-band activity, and the examination of robustness to movement-induced artifacts. The dataset is publicly available on Figshare.

Classification of Autism Spectrum Disorder in Children Using Electroencephalography Power Ratios Obtained During a Naturalistic Mentalizing Task.

Rapid or point-of-care (POC) EEG devices, bolstered by advancements in portability, ease of use, wireless technology, and artificial intelligence, are transforming the EEG field. Increasing demand for immediate neurophysiologic diagnosis, previously limited by the operational complexities and specialized personnel required for traditional EEG, has driven these critical shifts. These innovations extend EEG's reach beyond traditional neurophysiology labs to diverse clinical settings, including emergency departments, intensive care units, remote locations, and homes. POC EEG is particularly valuable for diagnosing acute neurologic emergencies such as nonconvulsive status epilepticus and nonconvulsive seizures, traumatic brain injury, and stroke, enabling faster seizure detection, improved triage, and timely treatment. POC EEG systems facilitate rapid acquisition of clinically acceptable EEG by nonexperts, including physicians and other health care providers, emergency personnel, nurses, and in some cases, remote caregivers and patients. Bedside interpretation is augmented by real-time artificial intelligence algorithms. POC EEG hardware, including its sensors, headsets, amplifiers, connectivity, form factor, and power, diverges significantly from conventional EEG systems. These modifications are explicitly engineered to optimize rapid deployment, patient comfort, and operational simplicity in resource-constrained or time-sensitive scenarios. The adaptations, however, may necessitate trade-offs in signal quality, flexibility, channel count, reliability, and cost compared with laboratory-grade systems. Understanding these inherent differences and how hardware designs address them is critical for selecting the optimal POC EEG technology for a specific use.

Depression detection using Electroencephalogram (EEG) signals obtained from wearable medical-assisted diagnostic systems has become a well-established approach in the field of affective disorders. However, despite recent advancements, on-board Artificial Intelligence (AI) models still demand substantial computational resources, presenting significant challenges for deployment on resource-constrained wearable medical devices. Embedded Multi-core Processors (MPs) offer a promising solution for accelerating these models. However, the limited computational capabilities of embedded MPs, combined with the structural diversity of AI models, complicate resource allocation and increase associated costs. To address these challenges, we propose a Memory-Aware Multi-Objective Iterative Local Search (MAMILS) algorithm to optimize task scheduling, thereby improving the efficiency of AI model deployment on wearable EEG devices. Experimental results across seven AI models demonstrate that, the MAMILS approach yields substantial improvements in key performance indicators: Total Energy Consumption (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\bm {TEC}$</tex-math></inline-formula>) with an average reduction of 47.57%, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\bm {Makespan}$</tex-math></inline-formula> with an average reduction of 48.75%, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\bm {Throughput}$</tex-math></inline-formula> with an average increase of 198.37%, all while maintaining satisfactory classification performance for both Machine Learning (ML) and Deep Learning (DL) models. Especially, on-board deployment of EEGNeX achieves an accuracy of 93.4%, sensitivity of 91.6%, and specificity of 95.8%. Further analysis indicates that, when integrated with wearable EEG sensors and executable on-board AI models, the proposed MAMILS optimization strategy shows significant promise in facilitating the widespread adoption of low-power, real-time diagnostic systems for depression detection.

In online learning environments, the need to monitor student engagement to achieve desired learning outcomes is foundational to any pedagogy for effective online teaching. With the absence of in-person supervision, keeping track of student attention in remote classes is quite arduous, if not impossible. This paper proposes an IoT-based EEG device for the real-time assessment of students' brain activity to measure and possibly improve students' attention levels in virtual classes. An ensemble machine learning (ML) study, specifically an artificial neural network (ANN) approach, is employed to investigate the relationship between student performance and EEG data. The study shows the performance of students is negatively correlated to the delta power and the theta/alpha ratio, the common EEG metrics for mental fatigue and drowsiness, respectively, for the student. An IoT-enabled EEG device provides teachers with real-time, precise, and unbiased data regarding the student's cognitive attention levels, via a reporting system. Thus, this research demonstrates that the proposed system, utilizing EEG and ANN ensemble-ML methods, can predict attention levels in real-time, enabling timely intervention for students who are disconnected. The study opens the door to the use of advanced BCI systems in teaching to maximize student attention.

Animal welfare is increasingly assessed through the "Five Domains" framework, where monitoring brain activity via electroencephalography (EEG) is essential for objectively evaluating sleep-wake cycles and neurological health. However, traditional EEG studies in animals often require invasive procedures, anesthesia, and movement restriction, which compromise both animal welfare and natural behavior. To overcome these limitations, we developed a miniaturized wireless EEG device (24.8 × 24.8 × 8.2 mm; 5.2 g) with Bluetooth transmission, surface electrodes, and biocompatible adhesives. This system allows 10-12 h of recording without restricting movement while remaining compatible with animal welfare standards. We validated the methodology in three amniote species representing major vertebrate classes: Aldabra giant tortoise (Aldabrachelys gigantea), gentoo penguin (Pygoscelis papua), and aardvark (Orycteropus afer). Recordings were conducted on conscious, freely moving animals in zoological facilities, and signals were analyzed using spectral frequency analysis. Three distinct EEG patterns were consistently identified across all species: active wakefulness, characterized by desynchronized high-frequency waves (0-30 Hz) and locomotor activity; NREM sleep or a homologous state, marked by synchronized high-amplitude, low-frequency waves (0.5-4 Hz); and REM sleep or a homologous state, defined by desynchronized high-frequency waves without locomotor activity. Fundamental brainwave frequencies (delta, theta, alpha, beta and gamma) were consistent and conserved across species, while amplitude varied according to anatomical differences. Interestingly, we observed specific patterns of EEG frequencies distribution in the three species, reflecting unique evolutionary spectral profiles, such as alpha dominance during aardvark wakefulness, theta profusion in penguin wakefulness and REM sleep, and delta massiveness in tortoise NREM sleep. This non-invasive methodology successfully distinguishes and records sleep-wake patterns in reptiles, birds, and mammals without surgical procedures, demonstrating that high-quality neurophysiological data can be obtained while adhering to animal welfare principles. The system maintained signal integrity within a 15-meter range, allowing for naturalistic behaviors in home enclosures. The technique opens new possibilities for longitudinal behavioral studies, detection of neurological disorders, and comparative sleep research in captive animals, representing a significant advance toward more ethical practices in animal neuroscience.

The widespread adoption of consumer-grade electroencephalogram (EEG) devices has introduced new opportunities for applications beyond traditional clinical and laboratory settings. However, the lack of standardized evaluation methodologies for these devices raises concerns regarding their signal validity and usability. This study proposes a comprehensive evaluation paradigm for consumer-grade EEG devices, encompassing three levels of signal assessment: the detection of non-neural physiological artefacts signals, the validation of brain waves, and robustness to noise. Using a participant pool of 30 individuals, we assessed four popular consumer-grade EEG devices—BrainLink Pro, NeuroNicle FX2, Mindwave Mobile2, and Muse2—against a research-grade reference device, DSI-24 (Wearable Sensing Inc.). Experimental paradigms involved tasks such as eye blinking, jaw clenching, eyes-open/closed conditions for brain wave detection, and controlled head movements. The results indicate that all tested devices successfully detected both non-neural physiological artefacts and brain wave signals, with consumer-grade devices displaying comparable alpha rhythm characteristics and noise robustness to the research-grade device. User experience was evaluated through a structured questionnaire, revealing significantly higher usability scores for consumer-grade devices, particularly Mindwave Mobile2. The findings highlight the feasibility of using consumer-grade EEG devices for practical applications, provided that validation is performed using structured evaluation protocols as proposed.

Sleep is essential for human health and well-being. Although the traditional sleep polysomnography (PSG) monitoring is the clinical gold standard for sleep assessment, its operation is complex and costly, so it is not suitable for long-term home use. Wearable electroencephalography (EEG) is a promising alternative, providing a new way to facilitate continuous sleep monitoring. This study focuses on the latest advances in wearable EEG devices for sleep monitoring, with a particular focus on headband, in-ear, and flexible forehead patch systems, including evaluating their design principles, balance between user comfort and signal quality, and performance in sleep staging technology compared to PSG. At the same time, not only the ongoing challenges of reducing signal artifacts and accurately detecting light sleep layers were discussed, but also the important advantages of wearable EEG in supporting personalized health tracking and longitudinal sleep studies. Finally, the study envisions the revolutionary potential of wearable EEG in popularizing sleep health management, including the use of artificial intelligence and cloud computing to optimize data processing to improve accuracy, and the positive shift from passive observation to active sleep intervention.

Wearable electroencephalography (EEG) devices are miniaturized, portable, and wireless systems for long-term brain monitoring, demonstrating significant potential as accessible mild cognitive impairment (MCI) screening tools based on objective neurophysiological biomarkers. However, their performance in MCI detection remains unclear, and their translation to real-world applications faces several challenges. This study aimed to comprehensively evaluate wearable EEG for MCI detection, identify key characteristics that optimize classification performance and usability, and address gaps in effective design implementation. We conducted a systematic search across seven databases, screening 1562 records and analyzing 21 studies that examined 16 distinct wearable EEG devices for MCI detection. The results revealed considerable variation in classification accuracy (range: 46-95%). A system-level analysis of the entire wearable EEG system and data flow identified seven critical factors that optimize the trade-off between diagnostic performance, portability, and affordability: (1) moderate channel density; (2) frontal and parietal electrode placement; (3) elderly-friendly multi-domain cognitive tasks; (4) adaptive signal preprocessing; (5) multi-domain feature extraction; (6) ensemble classifiers; and (7) multimodal integration. Additionally, methodological considerations for future wearable EEG-based MCI detection research include: (1) standardize MCI diagnostic frameworks; (2) increase sample diversity; (3) optimizing device usability and technical specifications; (4) standardize recording protocols; (5) harmonizing data processing pipelines; (6) validate in real-world settings; (7) assess cost-effectiveness; and (8) implement comprehensive reporting guidelines. These insights enable further translational applications of wearable EEG-based MCI detection and provide a foundation for developing user-friendly systems that could transform early cognitive impairment screening in community and primary care settings.

In-ear electroencephalography (EEG) systems offer several practical advantages over scalp-based EEG systems for non-invasive brain-computer interface (BCI) applications. However, the difficulty in fabricating in-ear EEG systems can limit their accessibility for BCI use cases. In this study, we developed a portable, low-cost wireless in-ear EEG device using commercially available components. In-ear EEG signals (referenced to left mastoid) from 5 adolescent participants were compared to scalp-EEG collected simultaneously during an alpha modulation task, various artifact induction tasks, and an auditory word-streaming BCI paradigm. Spectral analysis confirmed that the proposed in-ear EEG system could capture significantly increased alpha activity during eyes-closed relaxation in 3 of 5 participants, with a signal-to-noise ratio of 2.34 across all participants. In-ear EEG signals were most susceptible to horizontal head movement, coughing and vocalization artifacts but were relatively insensitive to ocular artifacts such as blinking. For the auditory streaming paradigm, the classifier decoded the presented stimuli from in-ear EEG signals only in 1 of 5 participants. Classification of the attended stream did not exceed chance levels. Contrast plots showing the difference between attended and unattended streams revealed reduced amplitudes of in-ear EEG responses relative to scalp-EEG responses. Hardware modifications are needed to amplify in-ear signals and measure electrode-skin impedances to improve the viability of in-ear EEG for BCI applications.

Mild cognitive impairment (MCI) is a cognitive decline syndrome in the elderly, often a precursor to dementia. It is a heterogeneous condition that can signal degenerative disorders like Alzheimer's or non-degenerative conditions such as vascular issues, depression, or poorly managed diabetes. Early detection of MCI is crucial for timely intervention, and differentiating its phenotypes helps in understanding its causes, progression, and treatment. EEG, which records brain electrical activity, consists of rhythmic and arrhythmic components. Examining these inherently overlapping EEG components calls for quantification, ensuring that an appropriate physiological mechanism is attributed to a given neural response. This study explores the interaction between APOE ε4 (APOE4) and cognitive impairment on non-oscillatory EEG activity. We examined aperiodic EEG activity using a parameterized spectral estimation approach in a sample comprising 751, 142, and 279 cognitively normal (CN), non-amnestic (naMCI), and amnestic (aMCI) MCI patients, respectively. The 5-min EEG was recorded using a prefrontal two-channel EEG device in a resting state, eyes closed. Cognitive decline was assessed using the Seoul Neuropsychological Screening Battery (SNSB) and the Mini-Mental State Examination (MMSE). The analyses were performed using various statistical methods, including independent t-tests and generalized linear models (GLM) with an identity link function. These analyses investigated the main and interaction effects of the APOE4 status and participants' cognitive states. We found interactions between APOE4 and cognitive states in the aperiodic EEG exponent and the spectral power ratio (SPR). Distinct patterns were observed in the exponent, offset, and SPR between APOE4 non-carriers and carriers across the CN, naMCI, and aMCI. Among the APOE4 carriers, the aMCI individuals exhibited heightened aperiodic activity and a reduced SPR than the naMCI. Furthermore, the CN had a lower SPR compared to the naMCI. However, no differences in the aperiodic component and SPR were observed in the APOE4 non-carriers across the cognitive states. The higher aperiodic component and a reduced SPR observed in aMCI relative to naMCI in APOE4 carriers may indicate an interplay between genetic predisposition, neuropathological changes, and cognitive decline. These aperiodic components, combined with APOE4 status, represent promising neurophysiological markers that may help identify individuals at elevated risk for cognitive decline or progression toward AD.

Obstructive sleep apnoea (OSA) is common and burdensome, yet current diagnostic pathways remain costly and often misclassify patients. Single-night polysomnography (PSG), the diagnostic gold standard, fails to capture night-to-night variability in severity, is difficult to access and costly. Emerging technologies enable multi-night in-home evaluation, but robust comparative evidence regarding accuracy and cost-effectiveness is lacking. This study will evaluate (a) the diagnostic accuracy of three multi-night in-home devices versus conventional single night PSG; (b) the patient and signal characteristics that optimize diagnostic performance; and (c) the cost-effectiveness of these pathways for clinical implementation. We will conduct a three-arm randomized diagnostic strategy trial (n = 500) enrolling adults referred for suspected OSA over a recruitment period of several years from July 2025. All participants will undergo both conventional single-night PSG and multi-night assessment with an under-mattress sensor, oximetry ring, and forehead EEG device. Participants will be randomized to one of three diagnostic pathways, determining the order in which sleep physicians interpret test results. Sleep physicians will provide sequential diagnostic decisions at each stage, and a blinded expert panel will determine consensus diagnoses. The primary outcome is diagnostic accuracy of each pathway compared with consensus diagnosis. Secondary outcomes include cost-effectiveness, patient-reported acceptability, clinical confidence, self-reported long-term symptoms, and health and quality of life outcomes over 12 months follow-up. Findings will provide definitive evidence for whether simplified and accessible multi-night testing can improve accuracy and cost-effectiveness of OSA diagnosis in routine care.

BackgroundRecent advances in equipment miniaturization have led to low-cost, portable electroencephalography (EEG) systems that facilitate data collection in real-world settings and with larger samples. Although wireless EEG systems were originally developed for non-research applications, recent studies have provided valuable information to help researchers make informed choices, particularly about participant comfort, mobility during recordings, and data validity. This study aimed to assess the impact of portability by comparing the performance of portable consumer- and research-grade systems (EPOC Saline Flex, EM; LiveAmp, LA) with fixed research-grade systems (BrainAmp, BA).MethodContinuous EEG was recorded with each system in healthy adults performing five benchmark tasks in fundamental and clinical cognitive neuroscience. Mental states (alpha power variations in open/closed eyes) and unconscious perception (steady-state visual evoked potential, SSVEP) were analyzed through time/frequency methods, while active (N200 and P300 components during active listening and N170 component during face recognition) and passive cognitive processes (Mismatch negativity, MMN component during passive listening) were examined using time/amplitude analyses (event-related potential, ERPs). Our analyses compared system efficiency at native and equalized sampling rates and examined 100%, 75%, and 50% of the datasets to determine the required trial number for satisfactory signal quality.ResultsDespite the smaller amount of signal retained for EM, all systems recorded the expected resting state alpha power decrease and SSVEP responses, with EM showing lower spectral effects ([EM < (LA≈BA)]). ERPs for active (N170, N200, P300) and passive (MMN) processes emerged across all systems, with EM and LA showing lower amplitudes only for N170 compared to BA. Furthermore, the dataset reduction resulted in a decreased N170 at P7 only for EM ([EM < LA < BA]). EM also exhibited shorter latencies for all ERPs except for MMN.ConclusionThis study provides concrete guidance for designing EEG experiments in real-world settings, with significant potential for investigating children and vulnerable populations. The efficiency of the three EEG systems is more influenced by task duration than sampling rates. A wireless EEG device, such as the EM, can effectively support both time/frequency and time/amplitude analyses in cognitive science, provided that the number of trials is sufficient and latencies are controlled.