Emotion regulation plays a vital role in human beings daily lives by helping them deal with social problems and protects mental and physical health. However, objective evaluation of the efficacy of emotion regulation and assessment of the improvement in emotion regulation ability at the individual level remain challenging. In this study, we leveraged neurofeedback training to design a real-time EEG-based brain-computer interface (BCI) system for users to effectively regulate their emotions. Twenty healthy subjects performed 10 BCI-based neurofeedback training sessions to regulate their emotion towards a specific emotional state (positive, negative, or neutral), while their EEG signals were analyzed in real time via machine learning to predict their emotional states. The prediction results were presented as feedback on the screen to inform the subjects of their immediate emotional state, based on which the subjects could update their strategies for emotion regulation. The experimental results indicated that the subjects improved their ability to regulate these emotions through our BCI neurofeedback training. Further EEG-based spectrum analysis revealed how each emotional state was related to specific EEG patterns, which were progressively enhanced through long-term training. These results together suggested that long-term EEG-based neurofeedback training could be a promising tool for helping people with emotional or mental disorders.

Wearables with photoplethysmography (PPG) sensors are being increasingly used in clinical research as a non-invasive, inexpensive method for remote monitoring of physiological health. Ensuring the accuracy and reliability of PPG-derived measurements is critical, as inaccuracies can impact research findings and clinical decisions. This paper systematically compares heart rate (HR) and heart rate variability (HRV) measures from PPG against an electrocardiogram (ECG) monitor in free-living settings. Two devices with PPG and one device with an ECG sensor were worn by 25 healthy volunteers for 10 days. PPG-derived HR and HRV showed reasonable accuracy and reliability, particularly during sleep, with mean absolute error < 1 beat for HR and 6–15 ms for HRV. The relative error of HRV estimated from PPG varied with activity type and was higher than during the resting state by 14–51%. The accuracy of HR/HRV was impacted by the proportion of usable data, body posture, and epoch length. The multi-scale peak and trough detection algorithm demonstrated superior performance in detecting beats from PPG signals, with an F1 score of 89% during sleep. The study demonstrates the trade-offs of utilizing PPG measurements for remote monitoring in daily life and identifies optimal use conditions by recommending enhancements.

Episodic memory (EM) deficit is the core cognitive dysfunction of amnestic mild cognitive impairment (aMCI). However, the episodic retrieval pattern detected by functional MRI (fMRI) appears preserved in aMCI subjects. To address this discrepancy, simultaneous electroencephalography (EEG)-fMRI recording was employed to determine whether temporal dynamics of brain episodic retrieval activity were disturbed in patients with aMCI. Twenty-six aMCI and 29 healthy control (HC) subjects completed a word-list memory retrieval task during simultaneous EEG-fMRI. The retrieval success activation pattern was detected with fMRI analysis, and the familiarity- and recollection-related components of episodic retrieval activity were identified using event-related potential (ERP) analysis. The fMRI-constrained ERP analysis explored the temporal dynamics of brain activity in the retrieval success pattern, and the ERP-informed fMRI analysis detected fMRI correlates of the ERP components related to familiarity and recollection processes. The two groups exhibited similar retrieval success patterns in the bilateral posteromedial parietal cortex, the left inferior parietal lobule (IPL), and the left lateral prefrontal cortex (LPFC). The fMRI-constrained ERP analysis showed that the aMCI group did not exhibit old/new effects in the IPL and LPFC that were observed in the HC group. In addition, the aMCI group showed disturbed fMRI correlate of ERP recollection component that was associated with inferior EM performance. Therefore, in this study, we identified disturbed temporal dynamics in episodic retrieval activity with a preserved spatial activity pattern in aMCI. Taken together, the simultaneous EEG-fMRI technique demonstrated the potential to identify individuals with a high risk of cognitive deterioration.

Background: The most common symptom of atopic dermatitis (AD) is pruritus, which is often exacerbated at night and leads to nocturnal scratching and sleep disturbance. The quantification of nocturnal scratching provides an objective measure, which could be used as a clinical trial endpoint tracking this AD-related behavior. However, it is not clear how digital health technologies (DHTs) intended to measure scratching perform in the real-world environment of patient homes. Objective: In this study, we present the analytical validation of 2 DHTs: the GENEActiv wristband with Philips sleep and scratch algorithms (“Philips”) and the Emerald radio frequency touchless sensor (“Emerald”) to measure nocturnal scratching in adults with AD. Methods: Thirty-one participants (15 with moderate AD, 11 with mild AD, and 5 healthy volunteers) were enrolled in the study. Nocturnal scratching was assessed by each DHT in the study participant’s home environment over a 4-week observation period. Infrared videos were recorded during sleep twice per week and manually annotated for the intended sleep window (total sleep opportunity [TSO]) and scratching events. Human annotations for sleep and scratch measures were used as a reference for comparison with DHTs (“Reference”). Estimated TSO was compared for each DHT to Reference using Bland-Altman analysis. Within-night agreement of DHT-predicted scratching events versus the Reference was assessed by sensitivity, precision, and F₁-score. Intraclass correlation was used to compare night-level scratch summaries (scratch duration per hour of TSO and scratch frequency per hour of TSO) between each DHT and the Reference. Results: Characterization of human-annotated scratching revealed a basal level of scratching in both healthy volunteers (13.1 seconds per hour) and in participants with AD on nights when no itch was reported (10.2 seconds per hour). The TSO window was quantified accurately with both DHTs having a mean bias compared with Reference of <30 minutes. The within-night agreement with reference to scratch detection performance resulted in F₁-scores at the disease group level ranging from 0.51 to 0.68 for the Emerald DHT and 0.47 to 0.56 for the Philips DHT. The night-level agreement of nocturnal scratch duration and frequency with human raters fell mostly in the moderate—good range of intraclass correlation coefficients (0.5‐0.9) in participants with AD and was not significantly lower than the level of agreement between any 2 human raters. Conclusions: These results support the analytical validity of both DHTs tested for continuous measurement of nocturnal scratching in individuals with AD in the home environment. Opportunities remain for improving the performance of the DHTs tested, especially in the precision of wrist-worn accelerometer scratch detection, to reach human-level performance. Additional data collection in diverse patient populations will be beneficial for practitioners intending to use or improve these tools for quantifying nocturnal scratching behavior.