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Abstract Introduction Ocular artifact has long been viewed as an impediment to the interpretation of electroencephalogram (EEG) signals in basic and applied research. Today, the use of blind source separation (BSS) methods, including independent component analysis (ICA) and second‐order blind identification (SOBI), is considered an essential step in improving the quality of neural signals. Recently, we introduced a method consisting of SOBI and a discriminant and similarity (DANS)‐based identification method, capable of identifying and extracting eye movement–related components. These recovered components can be localized within ocular structures with a high goodness of fit (>95%). This raised the possibility that such EEG‐derived SOBI components may be used to build predictive models for tracking gaze position. Methods As proof of this new concept, we designed an EEG‐based virtual eye‐tracker (EEG‐VET) for tracking eye movement from EEG alone. The EEG‐VET is composed of a SOBI algorithm for separating EEG signals into different components, a DANS algorithm for automatically identifying ocular components, and a linear model to transfer ocular components into gaze positions. Results The prototype of EEG‐VET achieved an accuracy of 0.920° and precision of 1.510° of a visual angle in the best participant, whereas an average accuracy of 1.008° ± 0.357° and a precision of 2.348° ± 0.580° of a visual angle across all participants (N = 18). Conclusion This work offers a novel approach that readily co‐registers eye movement and neural signals from a single‐EEG recording, thus increasing the ease of studying neural mechanisms underlying natural cognition in the context of free eye movement.

Background: Computerized cognitive tests may serve as a preliminary, low-cost method to identify individuals with suspected cognitive impairment in the community.Objective: To develop a self-administered computerized test, namely the "Computerized Cognitive Screen (CoCoSc), Hong Kong version", for screening of individuals with cognitive impairment (CI) in community settings.Methods: The CoCoSc is a 15-min computerized cognitive screen covering memory, executive functions, orientation, attention and working memory, and prospective memory administered on a touchscreen computer. Individuals with CI and cognitively normal controls were administered the CoCoSc and the Montreal Cognitive Assessment (MoCA). Validity of the CoCoSc was assessed based on the relationship with the MoCA using Pearson correlation. Receiver operating characteristic curve (ROC) was used to examine the ability of the CoCoSc to differentiate CI from controls.Results: Fifty-nine individuals with CI and 101 controls were recruited. Seventy-five (46.9%) participants had ≤6 years of education. Performance on the CoCoSc differed between normal and CI groups in both low and high education subgroups. Total scores of the CoCoSc and MoCA were significantly correlated (r = 0.71, p < 0.001). The area under ROC was 0.78, p < 0.001 for the CoCoSc total score in differentiating the CI group from the cognitively normal group. A cut-off of ≤30 on the CoCoSc was associated with a sensitivity of 0.78 and specificity of 0.69. The CoCoSc was well accepted by attendees of community social centers.Conclusion: The CoCoSc is a promising computerized cognitive screen for self-administration in community social centers. It is feasible for testing individuals with high or low education levels. [ABSTRACT FROM AUTHOR]

Objective This article describes a protocol for a randomized controlled trial to evaluate the effects of a three-level Health App for Post-Pandemic Years (HAPPY) on alleviating post-pandemic physiological and psychosocial distress. Methods Convenience and snowball sampling methods will be used to recruit 814 people aged 18+ with physiological and/or psychosocial distress. The experimental group will receive a 24-week intervention consisting of an 8-week regular supervision phase and a 16-week self-help phase. Based on their assessment results, they will be assigned to receive interventions on mindfulness, energy conservation techniques, or physical activity training. The waitlist control group will receive the same intervention in Week 25. The primary outcome will be changes in psychosocial distress, measured using the Kessler Psychological Distress Scale (K10). Secondary outcomes will include changes in levels of fatigue (Chinese version of the Brief Fatigue Inventory), sleep quality (Chinese version of the Pittsburgh Sleep Quality Index), pain intensity (Numeric Rating Scale), positive appraisal (Short version of the 18-item Cognitive Emotion Regulation Questionnaire), self-efficacy (Chinese version of the General Self-efficacy Scale), depression and anxiety (Chinese version of the 21-item Depression Anxiety Stress Scale), and event impact (Chinese version of the 22-item Impact of Event Scale–Revised). All measures will be administered at baseline (T0), Week 8 after the supervision phase (T1), and 24 weeks post-intervention (T2). A generalized estimating equations model will be used to examine the group, time, and interaction (Time × Group) effect of the interventions on the outcome assessments (intention-to-treat analysis) across the three time points, and to compute a within-group comparison of objective physiological parameters and adherence to the assigned interventions in the experimental group. Conclusions The innovative, three-level mobile HAPPY app will promote beneficial behavioral strategies to alleviate post-pandemic physiological and psychosocial distress. Trial registration ClinicalTrials.gov, NCT05459896. Registered on 15 July 2022.