Detecting Attention Levels in ADHD Children with a Video Game and the Measurement of Brain Activity with a Single-Channel BCI Headset

Enhancing spatial memory and pattern separation: Long-term effects of stimulant treatment in individuals with ADHD

This study explores the under-researched domain of long-term stimulant treatment in children and adolescents diagnosed with attention deficit hyperactivity disorder (ADHD). The necessity for extended treatment duration, often accompanied by safety concerns and side effects leading to treatment discontinuation, underscores the significance of this investigation. Concurrently, comparative studies have revealed adverse impacts on vulnerable regions within the hippocampal formation, accompanied by behavioral perturbations. We employed computerized tests and virtual reality to assess spatial memory, pattern separation, and object recognition memory in a cohort of children diagnosed with ADHD receiving stimulant treatment. We compared their performance to a group of neurotypical peers. Our findings indicate that the ADHD group exhibited a lower performance in spatial memory, pattern separation, and object recognition memory than ND group. Intriguingly, a positive relationship emerged between the duration of stimulant treatment and performance in these variables. Notably, this improvement was not immediate to MPH treatment but becomes significant after 24 months of treatment. In contrast to previous comparative investigations, our study did not reveal a detrimental impact on spatial navigation, object recognition memory, or pattern separation, despite the known interplay of these cognitive processes with the hippocampal formation. These results shed new light on the nuanced effects of stimulant treatment in ADHD, underscoring the need for a more comprehensive understanding of long-term treatment outcomes.

Effects of the Regular Use of Virtual Environments on Spatial Navigation and Memory

Introduction: The cognitive effects of video games have garnered increasing attention due to their potential applications in cognitive rehabilitation and evaluation. However, the underlying mechanisms driving these cognitive modifications remain poorly understood. Objectives: This study investigates the fundamental mnemonic processes of spatial navigation, pattern separation, and recognition memory, closely associated with the hippocampus. Our objective is to elucidate the interaction of these cognitive processes and shed light on rehabilitation mechanisms that could inform the design of video games aimed at stimulating the hippocampus. Method: In this study, we assessed 48 young adults, including both video game players and non-players. We utilized virtual reality and cognitive tasks such as the Lobato Virtual Water Maze and the Mnemonic Similarity Task to evaluate their cognitive abilities. Results: Our key findings highlight that gamers exhibit heightened pattern separation abilities and demonstrate quicker and more accurate spatial learning, attributed to the cognitive stimulation induced by video games. Additionally, we uncovered a significant relationship between spatial memory, guided by environmental cues, and pattern separation, which serves as the foundation for more efficient spatial navigation. Conclusions: These results provide valuable insights into the cognitive impact of video games and offer potential for monitoring changes in rehabilitation processes and early signs of cognitive decline through virtual reality-based assessments. Ultimately, we propose that examining the relationships between cognitive processes represents an effective method for evaluating neurodegenerative conditions, offering new possibilities for early diagnosis and intervention.

Advanced Electrode Technologies for Noninvasive Brain-Computer Interfaces

Brain-computer interfaces (BCIs) have garnered significant attention in recent years due to their potential applications in medical, assistive, and communication technologies. Building on this, noninvasive BCIs stand out as they provide a safe and user-friendly method for interacting with the human brain. In this work, we provide a comprehensive overview of the latest developments and advancements in material, design, and application of noninvasive BCIs electrode technology. We also explore the challenges and limitations currently faced by noninvasive BCI electrode technology and sketch out the technological roadmap from three dimensions: Materials and Design; Performances; Mode and Function. We aim to unite research efforts within the field of noninvasive BCI electrode technology, focusing on the consolidation of shared goals and fostering integrated development strategies among a diverse array of multidisciplinary researchers.

State-of-the-Art on Brain-Computer Interface Technology

This paper provides a comprehensive overview of the state-of-the-art in brain-computer interfaces (BCI). It begins by providing an introduction to BCIs, describing their main operation principles and most widely used platforms. The paper then examines the various components of a BCI system, such as hardware, software, and signal processing algorithms. Finally, it looks at current trends in research related to BCI use for medical, educational, and other purposes, as well as potential future applications of this technology. The paper concludes by highlighting some key challenges that still need to be addressed before widespread adoption can occur. By presenting an up-to-date assessment of the state-of-the-art in BCI technology, this paper will provide valuable insight into where this field is heading in terms of progress and innovation.

Study on the Psychological States of Olfactory Stimuli Using Electroencephalography and Heart Rate Variability

In the modern information society, people are constantly exposed to stress due to complex work environments and various interpersonal relationships. Aromatherapy is attracting attention as one of the methods for relieving stress using aroma. A method to quantitatively evaluate such an effect is necessary to clarify the effect of aroma on the human psychological state. In this study, we propose a method of using two biological indexes, electroencephalogram (EEG) and heart rate variability (HRV), to evaluate human psychological states during the inhalation of aroma. The purpose is to investigate the relationship between biological indexes and the psychological effect of aromas. First, we conducted an aroma presentation experiment using seven different olfactory stimuli while collecting data from EEG and pulse sensors. Next, we extracted the EEG and HRV indexes from the experimental data and analyzed them with respect to the olfactory stimuli. Our study found that olfactory stimuli have a strong effect on psychological states during aroma stimuli and that the human response to olfactory stimuli is immediate but gradually adapts to a more neutral state. The EEG and HRV indexes showed significant differences between aromas and unpleasant odors especially for male participants in their 20-30s, while the delta wave and RMSSD indexes showed potential for generalizing the method to evaluate psychological states influenced by olfactory stimuli across genders and generations. The results suggest the possibility of using EEG and HRV indexes to evaluate psychological states toward olfactory stimuli such as aroma. In addition, we visualized the psychological states affected by the olfactory stimuli on an emotion map, suggesting an appropriate range of EEG frequency bands for evaluating psychological states applied to the olfactory stimuli. The novelty of this research lies in our proposed method to provide a more detailed picture of the psychological responses to olfactory stimuli using the integration of biological indexes and emotion map, which contributes to the areas such as marketing and product design by providing insights into the emotional responses of consumers to different olfactory products.

Exploration of Brain-Computer Interaction for Supporting Children's Attention Training: A Multimodal Design Based on Attention Network and Gamification Design

Recent developments in brain-computer interface (BCI) technology have shown great potential in terms of estimating users' mental state and supporting children's attention training. However, existing training tasks are relatively simple and lack a reliable task-generation process. Moreover, the training experience has not been deeply studied, and the empirical validation of the training effect is still insufficient. This study thusly proposed a BCI training system for children's attention improvement. In particular, to achieve a systematic training process, the attention network was referred to generate the training games for alerting, orienting and executive attentions, and to improve the training experience and adherence, the gamification design theory was introduced to derive attractive training tasks. A preliminary experiment was conducted to set and modify the training parameters. Subsequently, a series of contrasting user experiments were organized to examine the impact of BCI training. To test the training effect of the proposed system, a hypothesis-testing approach was adopted. The results revealed that the proposed BCI gamification attention training system can significantly improve the participants' attention behaviors and concentration ability. Moreover, an immersive, inspiring and smooth training process can be created, and a pleasant user experience can be achieved. Generally, this work is promising in terms of providing a valuable reference for related practices, especially for how to generate BCI attention training tasks using attention networks and how to improve training adherence by integrating multimodal gamification elements.

Adjuvant Therapy for Attention in Children with ADHD Using Game-Type Digital Therapy

Children diagnosed with attention deficit hyperactivity disorder (ADHD) require early intervention and sustained treatment. This study used a game-based digital treatment planning NeuroWorld DTx to analyze the impact on attention and cognitive function in children with ADHD. Thirty children diagnosed with ADHD were recruited and subjected to a four-week NeuroWorld DTx digital treatment. To assess the impact of the digital therapeutic therapy on children's attention, we used the comprehension attention test (CAT) and Korean ADHD Rating Scale (K-ARS). Clinical global impression (CGI) and the Korean-child behavior checklist (K-CBCL 6-18) were used to examine the degree of improvement in ADHD. After four weeks, significant differences in the sensitivity and response style indices were noted, as compared with the baseline in the CAT test; in the case of K-ARS and CGI, a moderate decrease in ADHD was confirmed. The study achieved better results for the "total behavior problems" belonging to the K-CBCL assessment. Game-based digital therapy intervention can be a treatment method that elicits interest and satisfaction in children with ADHD and can be used as an adjunct to drug therapy to improve the quality of life and strengthen attention in children with symptoms of ADHD.

A Videogame as a Tool for Clinical Screening of Possible Vulnerability to Impulsivity and Attention Disturbances in Children

An attention disturbance is a problem that affects many school-aged children. The assessment in children is usually report-based, and as a result, controversy surrounds the diagnosis. To solve this issue, the aim of this study was to develop a new tool to detect possible attention-related problems and impulsive behavior in 4- and 5-year-old children. This tool was developed as an Android app and could be used to provide an early indicator of possible future development problems. A sample of 103 children (48 girls and 55 boys) was randomly selected from primary schools and assessed by Pinky-Piggy, a videogame application based on a classical paradigm in experimental psychology. Data from this app were compared with a Child Neuropsychological Maturity Questionnaire. The subjects displayed different patterns of response to play a very simple game called Pinky-Piggy. The application discriminated between high-responders and low responders. The results showed a relationship between these two profiles and the levels of attention and neurodevelopment in each group. The tool could identify different types of profiles and demonstrated its potential to evaluate endophenotypes to predict attentional problems related to impulsive behavior. Additionally, it required less time and fewer tests to identify possible at-risk populations, thus assisting in clinical diagnosis.

TMP19: A Novel Ternary Motif Pattern-Based ADHD Detection Model Using EEG Signals

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental condition worldwide. In this research, we used an ADHD electroencephalography (EEG) dataset containing more than 4000 EEG signals. Moreover, these EEGs are noisy signals. A new hand-modeled EEG classification model has been proposed to separate healthy versus ADHD individuals using the EEG signals. In this model, a new ternary motif pattern (TMP) has been incorporated. We have mimicked deep learning networks to create this hand-modeled classification method. The Tunable Q Wavelet Transform (TQWT) has been utilized to generate wavelet subbands. We applied the proposed TMP and statistics to construct informative features from both raw EEG signals and wavelet bands by generating TQWT. Herein, features have been generated by 18 subbands and the original EEG signal. Thus, this model is named TMP19. The most informative features have been chosen by deploying neighborhood component analysis (NCA), and the selected features have been classified using the k-nearest neighbor (kNN) classifier. The used ADHD EEG dataset has 14 channels. Thus, these three phases—(i) feature extraction with TQWT, TMP, and statistics; (ii) feature selection by deploying NCA; and (iii) classification with kNN—have been applied to each channel. Iterative hard majority voting (IHMV) has been applied to obtain a higher and more general classification response. Our model attained 95.57% and 77.93% classification accuracies by deploying 10-fold and leave one subject out (LOSO) cross-validations, respectively.

A multimodal approach for the ecological investigation of sustained attention: A pilot study

Natural fluctuations in sustained attention can lead to attentional failures in everyday tasks and even dangerous incidences. These fluctuations depend on personal factors, as well as task characteristics. So far, our understanding of sustained attention is partly due to the common usage of laboratory setups and tasks, and the complex interplay between behavior and brain activity. The focus of the current study was thus to test the feasibility of applying a single-channel wireless EEG to monitor patterns of sustained attention during a set of ecological tasks. An EEG marker of attention (BEI—Brain Engagement Index) was continuously recorded from 42 healthy volunteers during auditory and visual tasks from the Test of Everyday Attention (TEA) and Trail Making Test (TMT). We found a descending pattern of both performance and BEI in the auditory tasks as task complexity increases, while the increase in performance and decrease in BEI on the visual task. In addition, patterns of BEI in the complex tasks were used to detect outliers and the optimal range of attention through exploratory models. The current study supports the feasibility of combined electrophysiological and neurocognitive investigation of sustained attention in ecological tasks yielding unique insights on patterns of sustained attention as a function of task modality and task complexity.

Different Educational Interventions on Individual Cognition of Garbage Classification Based on EEG Monitoring

Improvement in an individuals’ cognition is the key to promote garbage classification. This study takes university students as the research subjects, through three educational interventions, including the self-learning, heuristic learning, and interactive learning ways, to seek the most effective intervention based upon event-related potentials (ERPs) that is beneficial to enhance cognition of garbage classification. The results show that the experimental subjects induced P300 and LPP components, representing attentional changes and cognitive conflicts in classification judgments. There are differences in the amplitudes and peak latency of the two components corresponding to different interventions, indicating that the three educational interventions are able to improve the individual’s cognition level of garbage classification within a certain period of time. The interactive-learning intervention triggers the largest amplitudes of P300 and LPP, as well as the smallest peak latency, indicating its effect is the best. Such results provide insight into the design for an appropriate strategy in garbage classification education. The study also shows that an EEG signal can be used as the endogenous neural indicator to measure the performance of garbage classification under different educational interventions.

An Information System for Symptom Diagnosis and Improvement of Attention Deficit Hyperactivity Disorder: The ADHD360 Project (Preprint)

Background

Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders during childhood; however, the diagnosis procedure remains challenging, as it is nonstandardized, multiparametric, and highly dependent on subjective evaluation of the perceived behavior.

Objective

To address the challenges of existing procedures for ADHD diagnosis, the ADHD360 project aims to develop a platform for (1) early detection of ADHD by assessing the user’s likelihood of having ADHD characteristics and (2) providing complementary training for ADHD management.

Methods

A 2-phase nonrandomized controlled pilot study was designed to evaluate the ADHD360 platform, including ADHD and non-ADHD participants aged 7 to 16 years. At the first stage, an initial neuropsychological evaluation along with an interaction with the serious game developed (“Pizza on Time”) for approximately 30-45 minutes is performed. Subsequently, a 2-week behavior monitoring of the participants through the mADHD360 app is planned after a telephone conversation between the participants’ parents and the psychologist, where the existence of any behaviors characteristic of ADHD that affect daily functioning is assessed. Once behavior monitoring is complete, the research team invites the participants to the second stage, where they play the game for a mean duration of 10 weeks (2 times per week). Once the serious game is finished, a second round of behavior monitoring is performed following the same procedures as the initial one. During the study, gameplay data were collected and preprocessed. The protocol of the pilot trials was initially designed for in-person participation, but after the COVID-19 outbreak, it was adjusted for remote participation. State-of-the-art machine learning (ML) algorithms were used to analyze labeled gameplay data aiming to detect discriminative gameplay patterns among the 2 groups (ADHD and non-ADHD) and estimate a player’s likelihood of having ADHD characteristics. A schema including a train-test splitting with a 75:25 split ratio, k-fold cross-validation with k=3, an ML pipeline, and data evaluation were designed.

Results

A total of 43 participants were recruited for this study, where 18 were diagnosed with ADHD and the remaining 25 were controls. Initial neuropsychological assessment confirmed that the participants in the ADHD group showed a deviation from the participants without ADHD characteristics. A preliminary analysis of collected data consisting of 30 gameplay sessions showed that the trained ML models achieve high performance (ie, accuracy up to 0.85) in correctly predicting the users’ labels (ADHD or non-ADHD) from their gameplay session on the ADHD360 platform.

Conclusions

ADHD360 is characterized by its notable capacity to discriminate player gameplay behavior as either ADHD or non-ADHD. Therefore, the ADHD360 platform could be a valuable complementary tool for early ADHD detection.

Trial Registration

ClinicalTrials.gov NCT04362982; https://clinicaltrials.gov/ct2/show/NCT04362982

International Registered Report Identifier (IRRID)

RR1-10.2196/40189

Using Neural Networks to Uncover the Relationship between Highly Variable Behavior and EEG during a Working Memory Task with Distractors

Value-driven attention capture (VDAC) occurs when previously rewarded stimuli capture attention and impair goal-directed behavior. In a working memory (WM) task with VDAC-related distractors, we observe behavioral variability both within and across individuals. Individuals differ in their ability to maintain relevant information and ignore distractions. These cognitive components shift over time with changes in motivation and attention, making it difficult to identify underlying neural mechanisms of individual differences. In this study, we develop the first participant-specific feedforward neural network models of reaction time from neural data during a VDAC WM task. We used short epochs of electroencephalography (EEG) data from 16 participants to develop the feedforward neural network (NN) models of RT aimed at understanding both WM and VDAC. Using general linear models (GLM), we identified 20 EEG features to predict RT across participants (r=0.53±0.08). The linear model was compared to the NN model, which improved the predicted trial-by-trial RT for all participants (r=0.87±0.04). We found that right frontal gamma-band activity and fronto-posterior functional connectivity in the alpha, beta, and gamma bands explain individual differences. Our study shows that NN models can link neural activity to highly variable behavior and can identify potential new targets for neuromodulation interventions.

Artificial Intelligence-Driven Serious Games in Healthcare: A Scoping Review (Preprint).. [DOI: 10.2196/preprints.39840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

BACKGROUND

Artificial Intelligence (AI)-driven serious games have been used in healthcare to offer a customizable and immersive experience. Summarizing the features of the current AI-driven serious games is very important to explore how they have been developed and used and their current state in order to plan on how to leverage them in the current and future healthcare needs.

OBJECTIVE

The current study aimed to explore the features of AI-driven serious games in healthcare as reported by previous research.

METHODS

We carried out a scoping review to achieve the above-mentioned objective. The most popular databases in information technology and health fields (e.g., MEDLINE and IEEE Xplore) were searched using keywords related to serious games and AI. These terms were selected based on the target intervention (i.e., AI) and the target disease (i.e., COVID-19). Two reviewers independently performed the study selection process. Three reviewers independently used Microsoft Excel to extract data from the included studies. A narrative approach was used for data synthesis.

RESULTS

The search process returned 1470 records. Of these records, 46 met all eligibility criteria. 60 different serious games were found in the included studies. Motor impairment was the most common health condition targeted by these serious games. Serious games in most of the studies were used for rehabilitation. The serious games in the majority of the included studies can be played by only single player. Most serious games were played on standalone devices (offline games). The most common genres of serious games were role-playing games, puzzle games, and platformer games. Unity was the most prominent game engine used to develop serious games. Personal computers (PCs) were the most common platforms used to play serious games. The most common algorithms used in the included studies were Support Vector Machine (SVM), Convolutional Neural Network (CNN), Artificial Neural Networks (ANN), and Random Forest (RF). The most common purposes of AI were the detection of disease and the evaluation of user's performance. The dataset size ranged from 36 to 795,600, with an average of about 52,124. The most common validation techniques used in the included studies were K-fold cross-validation and training test split validation. Accuracy was the most commonly used metric to evaluate the performance of AI models.

CONCLUSIONS

The last decade witnessed an increase in the development of AI-driven serious games for healthcare purposes and targeting various health conditions and leveraging multiple AI algorithms; this rising trend is expected to continue for years to come. While the evidence uncovered in this study shows promising applications of AI-driven serious games, larger and more rigorous, diverse, and robust studies may be needed to examine the efficacy and effectiveness of AI-driven serious games in different populations with different health conditions.

Learning First Aid with a Video Game

Any citizen can be involved in a situation that requires basic first aid knowledge. For this reason, it is important to be trained in this kind of activity. Serious games have been presented as a good option to integrate entertainment into the coaching process. This work presents a video game for mobile platforms which facilitate the formation and training in the PWA (Protect, Warn, Aid) first aid protocol. Users have to overcome a series of challenges to bring theoretical concepts closer to practice. To easily change the point of view of the game play, Augmented Reality technology has been used. In order to make each game looks different, neural networks have been implemented to perform the behavior of the Non-Playable Characters autonomous. Finally, in order to evaluate the quality and playability of the application, as well as the motivation and learning of content, several experiments were carried out with a sample of 50 people aged between 18 and 26. The obtained results confirm the playability and attractiveness of the video game, the increase of interest in learning first aid, as well as the greater fixation of the different concepts dealt with in the video game. The results support that this application facilitates and improves the learning of first aid protocols, making it more enjoyable, attractive, and practical.

The Implementation and Evaluation of Individual Preference in Robot Facial Expression Based on Emotion Estimation Using Biological Signals

Recently, robot services have been widely applied in many fields. To provide optimum service, it is essential to maintain good acceptance of the robot for more effective interaction with users. Previously, we attempted to implement facial expressions by synchronizing an estimated human emotion on the face of a robot. The results revealed that the robot could present different perceptions according to individual preferences. In this study, we considered individual differences to improve the acceptance of the robot by changing the robot’s expression according to the emotion of its interacting partner. The emotion was estimated using biological signals, and the robot changed its expression according to three conditions: synchronized with the estimated emotion, inversely synchronized, and a funny expression. During the experiment, the participants provided feedback regarding the robot’s expression by choosing whether they “like” or “dislike” the expression. We investigated individual differences in the acceptance of the robot expression using the Semantic Differential scale method. In addition, logistic regression was used to create a classification model by considering individual differences based on the biological data and feedback from each participant. We found that the robot expression based on inverse synchronization when the participants felt a negative emotion could result in impression differences among individuals. Then, the robot’s expression was determined based on the classification model, and the Semantic Differential scale on the impression of the robot was compared with the three conditions. Overall, we found that the participants were most accepting when the robot expression was calculated using the proposed personalized method.

Controlling a Mouse Pointer with a Single-Channel EEG Sensor

(1) Goals: The purpose of this study was to analyze the feasibility of using the information obtained from a one-channel electro-encephalography (EEG) signal to control a mouse pointer. We used a low-cost headset, with one dry sensor placed at the FP1 position, to steer a mouse pointer and make selections through a combination of the user’s attention level with the detection of voluntary blinks. There are two types of cursor movements: spinning and linear displacement. A sequence of blinks allows for switching between these movement types, while the attention level modulates the cursor’s speed. The influence of the attention level on performance was studied. Additionally, Fitts’ model and the evolution of the emotional states of participants, among other trajectory indicators, were analyzed. (2) Methods: Twenty participants distributed into two groups (Attention and No-Attention) performed three runs, on different days, in which 40 targets had to be reached and selected. Target positions and distances from the cursor’s initial position were chosen, providing eight different indices of difficulty (IDs). A self-assessment manikin (SAM) test and a final survey provided information about the system’s usability and the emotions of participants during the experiment. (3) Results: The performance was similar to some brain–computer interface (BCI) solutions found in the literature, with an averaged information transfer rate (ITR) of 7 bits/min. Concerning the cursor navigation, some trajectory indicators showed our proposed approach to be as good as common pointing devices, such as joysticks, trackballs, and so on. Only one of the 20 participants reported difficulty in managing the cursor and, according to the tests, most of them assessed the experience positively. Movement times and hit rates were significantly better for participants belonging to the attention group. (4) Conclusions: The proposed approach is a feasible low-cost solution to manage a mouse pointer.