Event-related pupillary response-based authentication system using eye-tracker add-on augmented reality glasses for individual identification.

This study aimed at developing a noncontact authentication system using event-related pupillary response (ErPR) epochs in an augmented reality (AR) environment. Thirty participants were shown in a rapid serial visual presentation consisting of familiar and unknown human photographs. ErPR was compared with event-related potential (ERP).

Electroencephalography-based classification of Alzheimer's disease spectrum during computer-based cognitive testing.

Alzheimer's disease (AD) is a progressive disease leading to cognitive decline, and to prevent it, researchers seek to diagnose mild cognitive impairment (MCI) early. Particularly, non-amnestic MCI (naMCI) is often mistaken for normal aging as the representative symptom of AD, memory decline, is absent. Subjective cognitive decline (SCD), an intermediate step between normal aging and MCI, is crucial for prediction or early detection of MCI, which determines the presence of AD spectrum pathology.

A comprehensive dataset for home appliance control using ERP-based BCIs with the application of inter-subject transfer learning.

Brain-computer interfaces (BCIs) have a potential to revolutionize human-computer interaction by enabling direct links between the brain and computer systems. Recent studies are increasingly focusing on practical applications of BCIs-e.g.

Electroencephalography characteristics related to risk of sudden unexpected death in epilepsy in patients with Dravet syndrome.

Objective: To investigate the quantitative electroencephalography (EEG) features associated with a high risk of sudden unexpected death in epilepsy (SUDEP) in patients with Dravet syndrome (DS).

Methods: Patients with DS and healthy controls (HCs) who underwent EEG were included in the study. EEG signals were recorded using a 21 channel digital EEG system, and pre-processed data were analyzed to identify quantitative EEG features associated with a high SUDEP risk.

Measurement of craving among gamers with internet gaming disorder using repeated presentations of game videos: a resting-state electroencephalography study.

Background: Internet gaming disorder (IGD) is receiving increasing attention owing to its effects on daily living and psychological function.

Methods: In this study, electroencephalography was used to compare neural activity triggered by repeated presentation of a stimulus in healthy controls (HCs) and those with IGD. A total of 42 adult men were categorized into two groups (IGD, n = 21) based on Y-IAT-K scores.

Improving Performance of Motor Imagery-based Brain-computer Interface in Poorly Performing Subjects Using a Hybrid-imagery Method utilizing Combined Motor and Somatosensory Activity.

The phenomena of brain-computer interface-inefficiency in transfer rates and reliability can hinder development and use of brain-computer interface technology. This study aimed to enhance the classification performance of motor imagery-based brain-computer interface (three-class: left hand, right hand, and right foot) of poor performers using a hybrid-imagery approach that combined motor and somatosensory activity. Twenty healthy subjects participated in these experiments involving the following three paradigms: (1) Control-condition: motor imagery only, (2) Hybrid-condition I: combined motor and somatosensory stimuli (same stimulus: rough ball), and (3) Hybrid-condition II: combined motor and somatosensory stimuli (different stimulus: hard and rough, soft and smooth, and hard and rough ball).

Effect of Visually Induced Motion Sickness from Head-Mounted Display on Cardiac Activity.

Head-mounted display (HMD) virtual reality devices can facilitate positive experiences such as co-presence and deep immersion; however, motion sickness (MS) due to these experiences hinders the development of the VR industry. This paper proposes a method for assessing MS caused by watching VR content on an HMD using cardiac features. Twenty-eight undergraduate volunteers participated in the experiment by watching VR content on a 2D screen and HMD for 12 min each, and their electrocardiogram signals were measured.

Early Prediction of Sepsis Onset Using Neural Architecture Search Based on Genetic Algorithms.

Sepsis is a life-threatening condition with a high mortality rate. Early prediction and treatment are the most effective strategies for increasing survival rates. This paper proposes a neural architecture search (NAS) model to predict the onset of sepsis with a low computational cost and high search performance by applying a genetic algorithm (GA).

Improving Motor Imagery-Based Brain-Computer Interface Performance Based on Sensory Stimulation Training: An Approach Focused on Poorly Performing Users.

The motor imagery (MI)-based brain-computer interface (BCI) is an intuitive interface that provides control over computer applications directly from brain activity. However, it has shown poor performance compared to other BCI systems such as P300 and SSVEP BCI. Thus, this study aimed to improve MI-BCI performance by training participants in MI with the help of sensory inputs from tangible objects (i.

Anti-Heartbeat-Evoked Potentials Performance in Event-Related Potentials-Based Mental Workload Assessment.

The aim of this study was to determine the effect of heartbeat-evoked potentials (HEPs) on the performance of an event-related potential (ERP)-based classification of mental workload (MWL). We produced low- and high-MWLs using a mental arithmetic task and measured the ERP response of 14 participants. ERP trials were divided into three conditions based on the effect of HEPs on ERPs: ERP, containing the heartbeat in a period of 280-700ms in ERP epochs after the target; ERP, not including the heartbeat within the same period; and ERP, all trials including ERP and ERP.

Classification of Gamers Using Multiple Physiological Signals: Distinguishing Features of Internet Gaming Disorder.

The proliferating and excessive use of internet games has caused various comorbid diseases, such as game addiction, which is now a major social problem. Recently, the American Psychiatry Association classified "Internet gaming disorder (IGD)" as an addiction/mental disorder. Although many studies have been conducted on the diagnosis, treatment, and prevention of IGD, screening studies for IGD are still scarce.

Non-Contact Measurement of Motion Sickness Using Pupillary Rhythms from an Infrared Camera.

Both physiological and neurological mechanisms are reflected in pupillary rhythms via neural pathways between the brain and pupil nerves. This study aims to interpret the phenomenon of motion sickness such as fatigue, anxiety, nausea and disorientation using these mechanisms and to develop an advanced non-contact measurement method from an infrared webcam. Twenty-four volunteers (12 females) experienced virtual reality content through both two-dimensional and head-mounted device interpretations.

A Hybrid Brain-Computer Interface for Real-Life Meal-Assist Robot Control.

Assistant devices such as meal-assist robots aid individuals with disabilities and support the elderly in performing daily activities. However, existing meal-assist robots are inconvenient to operate due to non-intuitive user interfaces, requiring additional time and effort. Thus, we developed a hybrid brain-computer interface-based meal-assist robot system following three features that can be measured using scalp electrodes for electroencephalography.

Artificial intelligence-based nomogram for small-incision lenticule extraction.

Background: Small-incision lenticule extraction (SMILE) is a surgical procedure for the refractive correction of myopia and astigmatism, which has been reported as safe and effective. However, over- and under-correction still occur after SMILE. The necessity of nomograms is emphasized to achieve optimal refractive results.

Developing a Motor Imagery-Based Real-Time Asynchronous Hybrid BCI Controller for a Lower-Limb Exoskeleton.

This study aimed to develop an intuitive gait-related motor imagery (MI)-based hybrid brain-computer interface (BCI) controller for a lower-limb exoskeleton and investigate the feasibility of the controller under a practical scenario including stand-up, gait-forward, and sit-down. A filter bank common spatial pattern (FBCSP) and mutual information-based best individual feature (MIBIF) selection were used in the study to decode MI electroencephalogram (EEG) signals and extract a feature matrix as an input to the support vector machine (SVM) classifier. A successive eye-blink switch was sequentially combined with the EEG decoder in operating the lower-limb exoskeleton.

EEG response to game-craving according to personal preference for games.

Recently, the World Health Organization included 'gaming disorder' in its latest revision of the international classification of diseases (ICD-11). Despite extensive research on internet gaming disorder (IGD), few studies have addressed game-related stimuli eliciting craving, which plays an important role in addiction. Particularly, most previous studies did not consider personal preferences in games presented to subjects as stimuli.

Resilience in the Surgical Scheduling to Support Adaptive Scheduling System.

Operating Room (OR) managers frequently encounter uncertainties related to real-time scheduling, especially on the day of surgery. It is necessary to enable earlier identification of uncertainties occurring in the perioperative environment. This study aims to propose a framework for resilient surgical scheduling by identifying uncertainty factors affecting the real-time surgical scheduling through a mixed-methods study.

Designing a mandibular advancement device with topology optimization for a partially edentulous patient.

Statement Of Problem: Patients with partial tooth loss treated with implant-supported fixed partial dentures (FPDs) have difficulty using conventional mandibular advancement devices (MADs) because of the risk of side effects. Also, which design factors affect biomechanical stability when designing MADs with better stability is unclear.

Purpose: The purpose of this finite element (FE) analysis study was to analyze the effect of the MAD design on biomechanical behavior and to propose a new design process for improving the stability of MADs.

Machine-Learning-Based Detection of Craving for Gaming Using Multimodal Physiological Signals: Validation of Test-Retest Reliability for Practical Use.

Internet gaming disorder in adolescents and young adults has become an increasing public concern because of its high prevalence rate and potential risk of alteration of brain functions and organizations. Cue exposure therapy is designed for reducing or maintaining craving, a core factor of relapse of addiction, and is extensively employed in addiction treatment. In a previous study, we proposed a machine-learning-based method to detect craving for gaming using multimodal physiological signals including photoplethysmogram, galvanic skin response, and electrooculogram.

Non-invasive transmission of sensorimotor information in humans using an EEG/focused ultrasound brain-to-brain interface.

We present non-invasive means that detect unilateral hand motor brain activity from one individual and subsequently stimulate the somatosensory area of another individual, thus, enabling the remote hemispheric link between each brain hemisphere in humans. Healthy participants were paired as a sender and a receiver. A sender performed a motor imagery task of either right or left hand, and associated changes in the electroencephalogram (EEG) mu rhythm (8-10 Hz) originating from either hemisphere were programmed to move a computer cursor to a target that appeared in either left or right of the computer screen.

A wearable device for emotional recognition using facial expression and physiological response.

This paper introduces a glasses-typed wearable system to detect user's emotions using facial expression and physiological responses. The system is designed to acquire facial expression through a built-in camera and physiological responses such as photoplethysmogram (PPG) and electrodermal activity (EDA) in unobtrusive way. We used video clips for induced emotions to test the system suitability in the experiment.

Automatic segmentation and user-friendly software techniques for virtual surgical planning of mandibular reconstruction.

Compared to conventional mandibular reconstruction surgery, a recent approach of virtual planning can reduce surgical time providing reliable surgical outcomes. We present our virtual surgical planning software using automatic segmentation and user-friendly techniques. Evaluation experiments compared with a commercial software prove that the proposed software can achieve successful surgical planning in reduced time with reliable accuracy.