Search Results (37)

Brain–computer interfaces (BCIs) can be used to monitor and provide real-time feedback on brain signals, directly influencing external systems, such as virtual environments (VE), to support self-regulation. We piloted a novel immersive, first-person shooting BCI-VE during which the avatars’ movement speed was directly influenced by neural activity in the supplementary motor area (SMA). Previous analyses revealed behavioral and localized neural effects for active versus reduced contingency neurofeedback in a randomized controlled trial design. However, the modeling of neural dynamics during such complex tasks challenges traditional event-related approaches. To overcome this limitation, we employed a data-driven framework utilizing group-level independent networks derived from BOLD-specific components of the multi-echo fMRI data obtained during the BCI regulation. Individual responses were estimated through dual regression. The spatial independent components corresponded to established cognitive networks and task-specific networks related to gaming actions. Compared to reduced contingency neurofeedback, active regulation induced significantly elevated fractional amplitude of low-frequency fluctuations (fALFF) in a frontoparietal control network, and spatial reweighting of a salience/ventral attention network, with stronger expression in SMA, prefrontal cortex, inferior parietal lobule, and occipital regions. These findings underscore the distributed network engagement of BCI regulation during a behavioral task in an immersive virtual environment. Full article

Attention and brain focus are essential in human activities that require learning. In higher education, a popular means of acquiring knowledge and information is through serious games. The need for integrating digital learning tools, including serious games, into university curricula has been demonstrated by the students’ preferences that are oriented more towards engaging and interactive alternatives than traditional education. This study examines real-time attention measurement in serious games using wearable brain–computer interfaces (BCIs). By capturing electroencephalography (EEG) signals non-invasively, the system continuously monitors players’ cognitive states to assess attention levels during gameplay. The novel approach proposes adaptive attention measurements to investigate the ability to maintain attention during cognitive tasks of different durations and intensities, using a single-channel EEG system—NeuroSky Mindwave Mobile 2. The measures have been achieved on ten volunteer master’s students in Computer Science. Attention levels during short and intense tasks were compared with those recorded during moderate and long-term activities like watching an educational lecture. The aim was to highlight differences in mental concentration and consistency depending on the type of cognitive task. The experiment was designed following a unique protocol applied to all ten students. Data were acquired using the NeuroExperimenter software 6.6, and analytics were performed in RStudio Desktop for Windows 11. Data is available at request for further investigations and analytics. Experimental results demonstrate that wearable BCIs can reliably detect attention fluctuations and that integrating this neuroadaptive feedback significantly enhances player focus and immersion. Thus, integrating real-time cognitive monitoring in serious game design is an efficient method to optimize cognitive load and create personalized, engaging, and effective learning or training experiences. Beta and attention brain waves, associated with concentration and mental processing, had higher values during the gameplay phase than in the lecture phase. At the same time, there are significant differences between participants—some react better to reading, while others react better to interactive games. The outcomes of this study contribute to the design of personalized learning experiences by customizing learning paths. Integrating NeuroSky or similar EEG tools can be a significant step toward more data-driven, learner-aware environments when designing or evaluating educational games. Full article

Emotion recognition using electroencephalogram (EEG) signals has gained significant attention due to its potential applications in human–computer interaction (HCI), brain computer interfaces (BCIs), mental health monitoring, etc. Although deep learning (DL) techniques have shown impressive performance in this domain, they often require large datasets and high computational resources and offer limited interpretability, limiting their practical deployment. To address these issues, this paper presents a novel frequency-driven ensemble framework for electroencephalogram-based emotion recognition (FREQ-EER), an ensemble of lightweight machine learning (ML) classifiers with a frequency-based data augmentation strategy tailored for effective emotion recognition in low-data EEG scenarios. Our work focuses on the targeted analysis of specific EEG frequency bands and brain regions, enabling a deeper understanding of how distinct neural components contribute to the emotional states. To validate the robustness of the proposed FREQ-EER, the widely recognized DEAP (database for emotion analysis using physiological signals) dataset, SEED (SJTU emotion EEG dataset), and GAMEEMO (database for an emotion recognition system based on EEG signals and various computer games) were considered for the experiment. On the DEAP dataset, classification accuracies of up to 96% for specific emotion classes were achieved, while on the SEED and GAMEEMO, it maintained 97.04% and 98.6% overall accuracies, respectively, with nearly perfect AUC values confirming the frameworks efficiency, interpretability, and generalizability. Full article

This paper presents the research outcomes of the Golden Seal project, which addresses the omnipresent issue of plastic pollution in coastal areas while enhancing their touristic value through the deployment of Internet of Things (IoT) technologies integrated into a gamified recycling framework. The developed system employs an IoT-enabled Wireless Sensor Network (WSN) to systematically collect, transmit, and analyze environmental data. A centralized, cloud-based platform supports real-time monitoring and data integration from Unmanned Aerial and Surface Vehicles (UAV and USV) equipped with sensors and high-resolution cameras. The system also introduces the Beach Cleanliness Index (BCI), a composite indicator that integrates quantitative environmental metrics with user-generated feedback to assess coastal cleanliness in real time. A key innovation of the project’s architecture is the incorporation of a Serious Game (SG), designed to foster public awareness and encourage active participation by local communities and municipal authorities in sustainable waste management practices. Pilot implementations were conducted at selected sites characterized by high tourism activity and accessibility. The results demonstrated the system’s effectiveness in detecting and classifying plastic waste in both coastal and terrestrial settings, while also validating the potential of the Golden Seal initiative to promote sustainable tourism and support marine ecosystem protection. Full article

Steady-state visual evoked potential (SSVEP) paradigms are widely used in brain–computer interface (BCI) systems due to their reliability and fast response. However, traditional static stimuli may reduce user comfort and engagement during prolonged use. This study proposes a dynamic stimulation paradigm combining periodic motion trajectories with speed control. Using four frequencies (6, 8.57, 10, 12 Hz) and three waveform patterns (sinusoidal, square, sawtooth), speed was modulated at 1/5, 1/10, and 1/20 of each frequency’s base rate. An offline experiment with 17 subjects showed that the low-speed sinusoidal and sawtooth trajectories matched the static accuracy (85.84% and 83.82%) while reducing cognitive workload by 22%. An online experiment with 12 subjects participating in a fruit-slicing game confirmed its practicality, achieving recognition accuracies above 82% and a System Usability Scale score of 75.96. These results indicate that coordinated trajectory and speed modulation preserves SSVEP signal quality and enhances user experience, offering a promising approach for fatigue-resistant, user-friendly BCI application. Full article

Designers of digital rehabilitation experiences can accommodate error-prone input devices like brain–computer interfaces (BCIs) by incorporating virtual help mechanisms to adjust the difficulty, but it is unclear on what grounds users are willing to accept such help. To study users’ experience of virtual help mechanisms, we used three help mechanisms in a blink-controlled game simulating a BCI-based stroke rehabilitation exercise. A mixed-method, simulated BCI study was used to evaluate game help by 19 stroke patients who rated their frustration and perceived control when experiencing moderately high input recognition. None of the help mechanisms affected ratings of frustration, which were low throughout the study, but two mechanisms affected patients’ perceived control ratings positively and negatively. Patient ratings were best explained by the amount of positive feedback, including game help, which increased perceived control ratings by 8% and decreased frustration ratings by 3%. The qualitative analysis revealed appeal, interference, self-blame, and prominence as deciding experiential factors of help, but it was unclear how they affected frustration and perceived control ratings. Building upon the results, we redesigned and tested self-reported measures of help quantity, help appeal, irritation, and pacing with game-savvy adults in a follow-up study using the same game. Help quantity appeared larger when game help shielded players from negative feedback, but this did not necessarily appeal to them. Future studies should validate or control for the constructs of perceived help quantity and appeal. Full article

A serious game combines concepts, principles, and methods of game design with information and communication technologies for the achievement of a given goal beyond entertainment. Serious game studies have been reported under a brain–computer interface (BCI) approach, with the specific use of electroencephalographic (EEG) signals. This study presents a review of the technological solutions from existing works related to serious games and EEG signals. A taxonomy is proposed for the classification of the research literature in three different categories according to the experimental strategy for the integration of the game and EEG: (1) evoked signals, (2) spontaneous signals, and (3) hybrid signals. Some details and additional aspects of the studies are also reviewed. The analysis involves factors such as platforms and development languages (serious game), software tools (integration between serious game and EEG signals), and the number of test subjects. The findings indicate that 50% of the identified studies use spontaneous signals as the experimental strategy. Based on the definition, categorization, and state of the art, the main research challenges and future directions for this class of technological solutions are discussed. Full article

Background: Serious games (SGs) are primarily aimed at promoting learning, skills training, and rehabilitation. Artificial intelligence, immersive technologies, the metaverse, and neurotechnologies promise the next revolution in gaming. Meta-skills are considered the “must-have” skills for thriving in the era of rapid change, complexity, and innovation. Μeta-skills can be defined as a set of higher-order skills that incorporate metacognitive, meta-emotional, and meta-motivational attributes, enabling one to be mindful, self-motivated, self-regulated, and flexible in different circumstances. Skillfulness, and more specifically meta-skills development, is recognized as a predictor of optimal performance along with mental and emotional wellness. Nevertheless, there is still limited knowledge about the effectiveness of integrating cutting-edge technologies in serious games, especially in the field of meta-skills training. Objectives: The current systematic review aims to collect and synthesize evidence concerning the effectiveness of advanced technologies in serious gaming for promoting meta-skills development. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was employed to identify experimental studies conducted in the last 10 years. Four different databases were employed: Web of Science, PubMed, Scopus, and Google Scholar. Results: Forty-nine studies were selected. Promising outcomes were identified in AI-based SGs (i.e., gamified chatbots) as they provided realistic, adaptive, personalized, and interactive environments using natural language processing, player modeling, reinforcement learning, GPT-based models, data analytics, and assessment. Immersive technologies, including the metaverse, virtual reality, augmented reality, and mixed reality, provided realistic simulations, interactive environments, and sensory engagement, making training experiences more impactful. Non-invasive neurotechnologies were found to encourage players’ training by monitoring brain activity and adapting gameplay to players’ mental states. Healthy participants (n = 29 studies) as well as participants diagnosed with anxiety, neurodevelopmental disorders, and cognitive impairments exhibited improvements in a wide range of meta-skills, including self-regulation, cognitive control, attention regulation, meta-memory skills, flexibility, self-reflection, and self-evaluation. Players were more self-motivated with an increased feeling of self-confidence and self-efficacy. They had a more accurate self-perception. At the emotional level, improvements were observed in emotional regulation, empathy, and stress management skills. At the social level, social awareness was enhanced since they could more easily solve conflicts, communicate, and work in teams. Systematic training led to improvements in higher-order thinking skills, including critical thinking, problem-solving skills, reasoning, decision-making ability, and abstract thinking. Discussion: Special focus is given to the potential benefits, possible risks, and ethical concerns; future directions and implications are also discussed. The results of the current review may have implications for the design and implementation of innovative serious games for promoting skillfulness among populations with different training needs. Full article

Children with attention deficit hyperactivity disorder (ADHD) frequently experience impairments in a range of abilities. Due to their poor attention and concentration, they find it challenging to stay focused when learning. They need help to retain the directions given by teachers and are very animated. Focus issues, hyperactivity, and attention problems may hamper learning. The needs and challenges of children with ADHD have been addressed by numerous digital solutions over the years. These solutions support a variety of needs (e.g., diagnosing versus treating), aim to address a variety of goals (e.g., addressing inattention, impulsivity, working memory, executive functions, emotion regulation), and employ a wide range of technologies, including video games, PC, mobile, web, AR, VR, tangible interfaces, wearables, robots, and BCI/neurofeedback, occasionally even in tandem. According to studies on the psychological impacts of serious games, immersive games can potentially be valuable tools for treating ADHD. This research investigates using PC, mobile/tablet applications, augmented reality, virtual reality, and brain–computer interfaces to develop executive functions and metacognitive and emotional competencies in children with ADHD through serious games. Following PRISMA 2020 criteria, this systematic review includes a comprehensive search of the PubMed, Web of Science, Scopus, and Google Scholar databases. The database search provided 784 records, and 30 studies met the inclusion criteria. The results showed that serious games assisted by multiple technologies could significantly improve a wide range of cognitive and socioemotional meta-competencies among children with ADHD, including visuospatial working memory, attention, inhibition control, cognitive flexibility, planning/organizing, problem-solving, social communication, and emotional regulation. The results of this review may provide positive feedback for creating more inclusive digital training environments for the treatment of ADHD children. Full article

This research explores the integration of the Dry Sensor Interface-24 (DSI-24) EEG headset with a ChatGPT-enabled Furhat robot to monitor cognitive stress in video gaming environments. The DSI-24, a cutting-edge, wireless EEG device, is adept at rapidly capturing brainwave activity, making it particularly suitable for dynamic settings such as gaming. Our study leverages this technology to detect cognitive stress indicators in players by analyzing EEG data. The collected data are then interfaced with a ChatGPT-powered Furhat robot, which performs dual roles: guiding players through the data collection process and prompting breaks when elevated stress levels are detected. The core of our methodology is the real-time processing of EEG signals to determine players’ focus levels, using a mental focusing feature extracted from the EEG data. The work presented here discusses how technology, data analysis methods and their combined effects can improve player satisfaction and enhance gaming experiences. It also explores the obstacles and future possibilities of using EEG for monitoring video gaming environments. Full article

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity that affects a large number of young people in the world. The current treatments for children living with ADHD combine different approaches, such as pharmacological, behavioral, cognitive, and psychological treatment. However, the computer science research community has been working on developing non-pharmacological treatments based on novel technologies for dealing with ADHD. For instance, social robots are physically embodied agents with some autonomy and social interaction capabilities. Nowadays, these social robots are used in therapy sessions as a mediator between therapists and children living with ADHD. Another novel technology for dealing with ADHD is serious video games based on a brain–computer interface (BCI). These BCI video games can offer cognitive and neurofeedback training to children living with ADHD. This paper presents a systematic review of the current state of the art of these two technologies. As a result of this review, we identified the maturation level of systems based on these technologies and how they have been evaluated. Additionally, we have highlighted ethical and technological challenges that must be faced to improve these recently introduced technologies in healthcare. Full article

Brain-computer interfaces (BCIs) are becoming an increasingly popular technology, used in a variety of fields such as medical, gaming, and lifestyle. This paper describes a 3D non-invasive BCI game that uses a Muse 2 EEG headband to acquire electroencephalogram (EEG) data and OpenViBE platform for processing the signals and classifying them into three different mental states: left and right motor imagery and eye blink. The game is developed to assess user adjustment and improvement in BCI environment after training. The classification algorithm used is Multi-Layer Perceptron (MLP), with 96.94% accuracy. A total of 33 subjects participated in the experiment and successfully controlled an avatar using mental commands to collect coins. The online metrics employed for this BCI system are the average game score, the average number of clusters and average user improvement. Full article

Brain–Computer Interfaces (BCIs) have become increasingly popular in recent years due to their potential applications in diverse fields, ranging from the medical sector (people with motor and/or communication disabilities), cognitive training, gaming, and Augmented Reality/Virtual Reality (AR/VR), among other areas. BCI which can decode and recognize neural signals involved in speech and handwriting has the potential to greatly assist individuals with severe motor impairments in their communication and interaction needs. Innovative and cutting-edge advancements in this field have the potential to develop a highly accessible and interactive communication platform for these people. The purpose of this review paper is to analyze the existing research on handwriting and speech recognition from neural signals. So that the new researchers who are interested in this field can gain thorough knowledge in this research area. The current research on neural signal-based recognition of handwriting and speech has been categorized into two main types: invasive and non-invasive studies. We have examined the latest papers on converting speech-activity-based neural signals and handwriting-activity-based neural signals into text data. The methods of extracting data from the brain have also been discussed in this review. Additionally, this review includes a brief summary of the datasets, preprocessing techniques, and methods used in these studies, which were published between 2014 and 2022. This review aims to provide a comprehensive summary of the methodologies used in the current literature on neural signal-based recognition of handwriting and speech. In essence, this article is intended to serve as a valuable resource for future researchers who wish to investigate neural signal-based machine-learning methods in their work. Full article

Objective: This study aimed at assessing the effects of two verbal encouragement modalities on the different offensive and defensive performance indicators in handball small-sided games practiced in physical education settings. Methods: A total of 14 untrained secondary school male students, aged 17 to 18, took part in a three-session practical intervention. Students were divided into two teams of seven players (four field players, a goalkeeper, and two substitutes). During each experimental session, each team played one 8 min period under teacher verbal encouragement (TeacherEN) and another under peer verbal encouragement (PeerEN). All sessions were videotaped for later analysis using a specific grid focusing on the balls played, balls won, balls lost, shots on goal, goals scored, as well as the ball conservation index (BCI), and the defensive efficiency index (DEI). Results: The findings showed no significant differences in favor of TeacherEN in all the performance indicators that were measured, whereas significant differences in favor of PeerEN were observed in balls played and shots on goal. Conclusions: When implemented in handball small-sided games, peer verbal encouragement can produce greater positive effects than teacher verbal encouragement in terms of offensive performance. Full article

Navigation in virtual worlds is ubiquitous in games and other virtual reality (VR) applications and mainly relies on external controllers. As brain–computer interfaces (BCI)s rely on mental control, bypassing traditional neural pathways, they provide to paralyzed users an alternative way to navigate. However, the majority of BCI-based navigation studies adopt cue-based visual paradigms, and the evoked brain responses are encoded into navigation commands. Although robust and accurate, these paradigms are less intuitive and comfortable for navigation compared to imagining limb movements (motor imagery, MI). However, decoding motor imagery from EEG activity is notoriously challenging. Typically, wet electrodes are used to improve EEG signal quality, including a large number of them to discriminate between movements of different limbs, and a cuedbased paradigm is used instead of a self-paced one to maximize decoding performance. Motor BCI applications primarily focus on typing applications or on navigating a wheelchair—the latter raises safety concerns—thereby calling for sensors scanning the environment for obstacles and potentially hazardous scenarios. With the help of new technologies such as virtual reality (VR), vivid graphics can be rendered, providing the user with a safe and immersive experience; and they could be used for navigation purposes, a topic that has yet to be fully explored in the BCI community. In this study, we propose a novel MI-BCI application based on an 8-dry-electrode EEG setup, with which users can explore and navigate in Google Street View^®. We pay attention to system design to address the lower performance of the MI decoder due to the dry electrodes’ lower signal quality and the small number of electrodes. Specifically, we restricted the number of navigation commands by using a novel middle-level control scheme and avoided decoder mistakes by introducing eye blinks as a control signal in different navigation stages. Both offline and online experiments were conducted with 20 healthy subjects. The results showed acceptable performance, even given the limitations of the EEG set-up, which we attribute to the design of the BCI application. The study suggests the use of MI-BCI in future games and VR applications for consumers and patients temporarily or permanently devoid of muscle control. Full article