Brain Computer Interface Game Research Articles

Application of Hybrid SSVEP + P300 Brain Computer Interface to Control Avatar Movement in Mobile Virtual Reality Gaming Environment

IntroductionThis research evaluated the feasibility of a hybrid SSVEP + P300 brain computer interface (BCI) for controlling the movement of an avatar in a virtual reality (VR) gaming environment (VR + BCI). Existing VR + BCI gaming environments have limitations, such as visual fatigue, a lower communication rate, minimum accuracy, and poor system comfort. Hence, there is a need for an optimized hybrid BCI system that can simultaneously evoke the strongest P300 and SSVEP potentials in the cortex. MethodsA BCI headset was coupled with a VR headset to generate a VR + BCI environment. The author developed a VR game in which the avatar’s movement is controlled using the user's cortical responses with the help of a BCI headset. Specifically designed visual stimuli were used in the proposed system to elicit the strongest possible responses from the user's brain. The proposed system also includes an auditory feedback mechanism to facilitate precise avatar movement. Results and ConclusionsConventional P300 BCI and SSVEP BCI were also used to control the movements of the avatar, and their performance metrics were compared to those of the proposed system. The results demonstrated that the hybrid SSVEP + P300 BCI system was superior to the other systems for controlling avatar movement.

Designing a Motion-Onset Visual-Evoked Potential-Based Brain–Computer Interface to Control a Computer Game

Brain-computer interface (BCI) establishes an alternative direct communication channel between the brain and computer or other external devices. Motion-onset visual evoked potential (mVEP) related to the individual perception of movement is among synchronous BCI system that utilizes external stimulus. mVEPs stimulation paradigm is like an oddball stimulation paradigm. Researchers have recently been investigating mVEPs to control a computer game. To control a computer game, they used translation stimulus to evoke these potentials in EEG brain signals. In this study, we examine designing a computer game with four different types of stimuli, including translation, contraction, expansion, and rotation. We also used time-domain and wavelet coefficients for the feature extraction. Also, we used different types of linear discriminant analysis (LDA) classification methods, including least absolute shrinkage and selection operator (LASSO), stepwise LDA (SWLDA); moreover, and spatial-temporal discriminant analysis (STDA) for detecting the target stimulus. Our results demonstrate that contraction, expansion, and rotation stimuli, as well as translation, could be employed to control a BCI game. Our results showed that subjects got higher scores, game accuracy control, and information transfer rate using the rotation stimulus with SWLDA/STDA classification methods.

Playing Tetris with Your Mind: A BCI-Controlled Gaming Experience

Brain-Computer Interface Controlled Tetris: Exploring Feasibility and User Experience. This project explores the feasibility and user experience of controlling a Tetris game using a Brain-Computer Interface (BCI). The BCI system utilizes non-invasive Electroencephalography (EEG) to detect P300 brainwaves generated by user focus on specific on-screen cues corresponding to desired game actions (left, right, rotate, drop).An ESP8266 microcontroller board, EEG sensor, and display are used to create a portable BCI system. Software development leverages open-source libraries for EEG signal processing, classification, and game engine control (onboard or external).The experiment evaluates BCI control performance through metrics like accuracy, speed, and Tetris completion rate. A control group playing traditional Tetris (if applicable) allows for comparison. Subjective user experience is assessed through questionnaires and surveys. The results are expected to demonstrate the feasibility of BCI- controlled Tetris using affordable hardware. Potential trade-offs between BCI control speed and traditional methods will be explored. User experience analysis will provide insights into the challenge, enjoyment, and future directions of BCI gaming. This project contributes to the growing field of BCI applications by demonstrating its potential for novel and mind-controlled gaming experiences. Keywords: Brain-Computer Interface, Electroencephalography, performance.

Exploring the effects of different BCI-based attention training games on the brain: A functional near-infrared spectroscopy study

BCI games have been widely employed as non-invasive therapeutic interventions for conditions, but their efficacy remains a subject of debate. This study explores the efficacy of two prevalent forms of Brain-Computer Interface (BCI)-based attention training games: video games (VG) and physical games (PG). The effectiveness of these games has been examined through the lens of neuroscience, using functional Near-Infrared Spectroscopy (fNIRS) to monitor cortical activation. After the fNIRS test, subjects completed an Intrinsic Motivation Inventory (IMI) questionnaire. PG tasks activated six channels (L-PFC, R-PFC and R-TL), while VG tasks activated only one (R-PFC). Furthermore, females exhibited stronger activation during PG tasks, while males had none in either. Our findings suggest that under equivalent game rules and themes, PG may prove more effective for cognitive rehabilitation than VG, with stronger intrinsic motivation. We also found this result may exhibit gender differences. Finally, this research offers valuable insights for the future design of BCI-based games from a neuroscience perspective.

The PGIRec Software to Support the Evaluation of User-System Interaction with a Synchronous and Multi-perspective Recording System

Audiovisual recording is a highly relevant activity when investigating and analyzing qualitative data, both for scientific researches and for the industry. Games are a particularly challenging application for user experience and interaction evaluation, given the multitude of platforms, modes of operation, number of players and game environments that may occur through different games genres. This diversity of interaction possibilities makes it difficult for employing current audiovisual recording tools in a number of scenarios, especially considering multi-user interaction in the same or in separate environments. In this context, this paper presents an open-source recording tool, called Player-Game Interaction Recorder (PGIRec), specialized in synchronous recording of multiple sources of audio and video. The software was evaluated using brain-computer interface games in two different studies, with a total of 22 participants and over 16 hours of recorded activities using different experimental setups. The evaluation showed that the solution was stable and adaptable, since it generated audiovisual media without occurrences in different situations, with multiple audio and video sources concurrently.

Mind the Move: Developing a Brain-Computer Interface Game with Left-Right Motor Imagery

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.

Evaluation of the User Adaptation in a BCI Game Environment

Brain-computer interface (BCI) technology is a developing field of study with numerous applications. The purpose of this paper is to discuss the use of brain signals as a direct communication pathway to an external device. In this work, Zombie Jumper is developed, which consists of 2 brain commands, imagining moving forward and blinking. The goal of the game is to jump over static or moving “zombie” characters in order to complete the level. To record the raw EEG data, a Muse 2 headband is used, and the OpenViBE platform is employed to process and classify the brain signals. The Unity engine is used to build the game, and the lab streaming layer (LSL) protocol is the connective link between Muse 2, OpenViBE and the Unity engine for this BCI-controlled game. A total of 37 subjects tested the game and played it at least 20 times. The average classification accuracy was 98.74%, ranging from 97.06% to 99.72%. Finally, playing the game for longer periods of time resulted in greater control.

Comparing users' performance and game experience between a competitive and collaborative brain-computer interface

ABSTRACT The recent advancements in Brain-Computer Interfaces (BCIs) technology made possible for its employment in entertainment applications, such as brain-controlled games. However, there are still many challenges to overcome in the design and development of such applications, especially when considering the interaction between multiple users. This paper presents the results of a controlled experiment aimed at comparing the performance and game experience of both competitive and collaborative matches in a multiplayer BCI game. In the experiment, sixteen subjects were divided in pairs into two groups, and each pair played a total of five competitive or collaborative matches of the attention-based game. Quantitative and qualitative data were collected to investigate key features of the subjects' interaction with both the BCI platform and the other players. It was found that there is no statistically significant difference in performance between the subjects of both groups, and between multiplayer and single player modes. Although the group playing competitively reported overall better scores for game experience, there was no statistically significant difference between groups. There were significant correlations between performance and positive aspects of game experience for both modes, and multiplayer matches were significantly shorter than single player ones, although this effect could not be attributed to the mode of interaction alone.

Mind Games: A Web-Based Multiplayer Brain-Computer Interface Game

The advancement of consumer-grade Brain-Computer Interface (BCI) devices has led to the use of brain wave data in disciplines beyond neuroscience. While BCI devices are studied holistically in some domains, the potential remains unexplored in other applicable domains, such as gaming. The majority of BCI games are single-player, only work on a single platform, or require expensive hardware that is difficult to use. To address these challenges, we propose Mind Games: a competitive multiplayer tug-of-war game played entirely with brain wave data in a web browser with a low-cost consumer-grade BCI device. Unlike other BCI games, Mind Games is platform-independent, has a quick setup time, and uses the latest web-based technologies: web-Bluetooth and JavaScript frameworks for networking and signal processing. This new cross-platform BCI pipeline enables physiological computing applications to be more accessible, leading the future of online multiplayer BCI games.

Winners and losers in brain computer interface competitive gaming: directional connectivity analysis

Objective. To characterize the direction within and between brain connectivity in winning and losing players in a competitive brain-computer interface game. Approach. Ten dyads (26.9 ± 4.7 yr old, eight females and 12 males) participated in the study. In a competitive game based on neurofeedback, they used their relative alpha (RA) band power from the electrode location Pz, to control a virtual seesaw. The players in each pair were separated into winners (W) and losers (L) based on their scores. Intrabrain connectivity was analyzed using multivariate Granger causality (GC) and directed transfer function, while interbrain connectivity was analyzed using bivariate GC. Main results. Linear regression analysis revealed a significant relationship (p < 0.05) between RA and individual scores. During the game, W players maintained a higher RA than L players, although it was not higher than their baseline RA. The analysis of intrabrain GC indicated that both groups engaged in general social interactions, but only the W group succeeded in controlling their brain activity at Pz. Group L applied an inappropriate metal strategy, characterized by strong activity in the left frontal cortex, indicative of collaborative gaming. Interbrain GC showed a larger flow of information from the L to the W group, suggesting a higher capability of the W group to monitor the activity of their opponent. Significance. Both innate neurological indices and gaming mental strategies contribute to game outcomes. Future studies should investigate whether there is a causal relationship between these two factors.

Factors influencing students' adoption intention of brain–computer interfaces in a game-learning context

PurposeA research framework that explains adoption intention in students with regard to brain–computer interface (BCI) games in the learning context was proposed and empirically examined.Design/methodology/approachIn this study, an approach integrating the decomposed theory of planned behavior, perceived playfulness, risk and the task–technology fit (TTF) concept was used to assess data collected using a post-experiment questionnaire from a student sample in Taiwan. The research model was tested using the partial least-squares structural equation modeling (PLS-SEM) technique.FindingsAttitude, subjective norms and TTF were shown to impact intention to play the BCI game significantly, while perceived behavioral control did not show a significant impact. The influence of superiors and peers was found to positively predict subjective norms. With the exception of perceived ease of use, all of the proposed antecedents were found to impact attitude toward BCI games. Technology facilitating conditions and BCI technology characteristics were shown to positively determine perceived behavior control and TTF, respectively. However, the other proposed factors did not significantly influence the latter two dependents.Originality/valueThis research contributes to the nascent literature on BCI games in the context of learning by highlighting the influence of belief-related psychological factors on user acceptance of BCI games. Moreover, this study highlights the important, respective influences of perceived playfulness, risk and TTF on users' perceptions of a game, body monitoring and technology implementation, each of which is known to influence willingness to play.

The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning.

In addition to helping develop products that aid the disabled, brain–computer interface (BCI) technology can also become a modality of entertainment for all people. However, most BCI games cannot be widely promoted due to the poor control performance or because they easily cause fatigue. In this paper, we propose a P300 brain–computer-interface game (MindGomoku) to explore a feasible and natural way to play games by using electroencephalogram (EEG) signals in a practical environment. The novelty of this research is reflected in integrating the characteristics of game rules and the BCI system when designing BCI games and paradigms. Moreover, a simplified Bayesian convolutional neural network (SBCNN) algorithm is introduced to achieve high accuracy on limited training samples. To prove the reliability of the proposed algorithm and system control, 10 subjects were selected to participate in two online control experiments. The experimental results showed that all subjects successfully completed the game control with an average accuracy of 90.7% and played the MindGomoku an average of more than 11 min. These findings fully demonstrate the stability and effectiveness of the proposed system. This BCI system not only provides a form of entertainment for users, particularly the disabled, but also provides more possibilities for games.

A Child's Right to Play: Results from the Brain-Computer Interface Game Jam 2019 (Calgary Competition).

Children with severe neurological disabilities may be unable to communicate or interact with their environments, depriving them of their right to play. Brain-computer interfaces (BCI) offer a means for such children to control external devices using only their brain signals, thereby introducing new opportunities for interaction. We organized the first North American BCI Game Jam to incite the development of BCI-compatible games for children. Nine games were submitted by 30 participants across North America. Games were judged by researchers and disabled children currently using BCI. Preliminary results demonstrate variety in game criteria preferences amongst the children who judged the games. The BCI Game Jam demonstrated promising potential for the creation of enjoyable games to suit the individual needs and preferences of children with severe neurological disabilities.

Mind your privacy: Privacy leakage through BCI applications using machine learning methods

With the digitization of almost every aspect of our lives, privacy leakage in cyber space has become a pressing concern. Brain–Computer Interface (BCI) systems have become more popular in recent years and are now being used for a variety of applications. BCI data represents an individual’s brain activity at a given time. Like many other kinds of data, BCI data can be utilized for malicious purposes. Electroencephalography (EEG) is one of the most popular brain activity acquisition methods of BCI applications. More specifically, BCI games, represent one of the main EEG applications. However, a malicious BCI application (e.g. game) could allow an attacker to take advantage of an unsuspecting user happily enjoying a game and record the user’s brain activity; by analyzing this data, the attacker can infer private information and characteristics regarding the user, without his/her consent or awareness. This study is the first to demonstrate the ability to predict and infer meaningful personality traits and cognitive abilities by analyzing resting-state EEG (rsEEG) recordings of an individual’s brain activity using a variety of machine learning methods. A comprehensive set of raw rsEEG scans, along with the dissociation level and executive function (EF) performance measures, for the 162 subjects were used in our evaluation. The best results we achieved were an accuracy of 73% for dissociation classification and less than 16% mean absolute error in predicting performance for all examined EFs. These encouraging results are better than those presented in prior research, both in terms of accuracy and data-validity and dataset size.

Security Analysis of Sports Injury Medical System Based on Internet of Health Things Technology

In the absence of physical movement, the dynamic process of rehearsing specific actions in the mind based on memory is called motor imagination (MI). Motor imaging brain-computer interface (BCI) refers to communicating with a computer or controlling external devices by imagining specific movements in the brain, while the Internet of health things technology helps to convey the rehabilitation of the limbs of patients with paralysis and stroke in time. Studies have shown that with the increase of motor imaging tasks, the classification accuracy rate will also decrease. It is necessary to study the construction and security of sports injury rehabilitation systems under the Internet of health things. Based on the study of the feature extraction algorithm, this paper starts with the infusion monitoring in the clinical medical care work, and through the construction of the Internet of health things smart care management system. It could provide application modules such as “infusion monitoring”, “disease call interconnection”, and “PDA interconnection” to realize the closed-loop path management of the whole process of basic nursing work in the ward. The sports rehabilitation training system receives the EEG data collected by the UE-16B EEG amplifier through the socket, and calls MATLAB to perform feature extraction and classification of the EEG data, and feedback the processing results to the sports rehabilitation training system and subjects. On this basis, the system has designed and completed a non-feedback training module, a feedback training module and related BCI game modules to promote users to perform sports imagination training and successfully complete sports rehabilitation training.

Two Player Online Brain-Controlled Chess.

Brain-computer interfaces (BCIs) allow for translating brain signals into control commands, e.g. to control games. One of the biggest quests of the BCI community is to realize new exciting applications. In this paper, we present a two player online chess application where both players control their pieces solely with their brain activity. Control is realized in a two-step process where players first select the desired chess piece and then the field they want to move it to. A selection is accomplished with visual event-related potentials that are elicited by highlighting each possible piece or field one by one. Six healthy volunteers participated in our study by playing a game against each other in pairs over a free chess server. They successfully completed at least one match per pair. Our results show that even BCI-naive players obtain almost perfect control over the application. On average, 96% of the moves were correct. Most of the errors came from technical problems that can be corrected in future versions of the application. Given the high popularity of chess, this intuitive BCI game is an appealing new application for patients and for healthy users that want to explore BCIs.

The Effect of Auditory Stimuli on User’s Meditation and Workload in a Brain–Computer Interface Game

Abstract The advancement of Brain–computer interface (BCI) technology allowed for the development of applications not only for physically-impaired users, but for entertainment purposes as well. However, there are still numerous challenges in the development of such applications, as it is not known to which extent external stimuli may interfere with the captured brain signals. Being so, understanding the possible limitations caused by these external stimuli may help developers and designers in the development of future BCI-based applications. This paper presents the results of a controlled experiment designed for investigating the effects of auditory stimuli (AS) on subjects playing a neurofeedback-based game. The experiment consisted of sixteen volunteer subjects who played a total of twelve game matches each, for a total of 144 matches, over the course of three experiment sessions. Statistical analysis and qualitative instruments were employed to investigate key features of the subjects’ interaction with the game over time, especially regarding the influence of AS in both subjects’ performance and self-assessed, subjective workload. It was concluded that the subjects’ level of meditation tends to increase over time, that the self-assessed workload tends to decrease over time, and that the game’s AS did neither significantly influenced the performance nor the subjective workload of the subjects. Research Highlights The influence of auditory stimuli (AS) was investigated with a brain–computer interface game. Meditation level and workload were assessed and evaluated in a controlled experiment setup. Subjects’ performance tends to increase over time, while self-assessed workload tends to decrease. The presence of AS did not influenced the subjects’ meditation level and workload.

The PERSON project: a serious brain-computer interface game for treatment in cognitive impairment

The cognitive and computational neurosciences have developed neurorehabilitative tools able to treat suffering subjects from early symptoms, in order to give priority to a home environment. In this way, the curative treatment would not burden the hospital with excessive costs and the patient with psychological disorientation. Recent studies have shown the efficacy of video games on improving cognitive processes impaired by ageing’s physiological effect, neurodegenerative or other diseases, with potential beneficial effects. The PERvasive game for perSOnalized treatment of cognitive and functional deficits associated with chronic and Neurodegenerative diseases (PERSON) project proposed new tools for cognitive rehabilitation, aiming to improve the quality of life for patients with cognitive impairments, especially at early stages, by the use of sophisticated, non-invasive technology. This article is an overview of game solutions for training cognitive abilities and it presents the tools developed within the PERSON project. These tools are serious games based on virtual reality, connected to a brain-computer interface based on electroencephalography (EEG) and to haptic devices. The project was born thanks to a strategic synergy between research and public health, to implement a technology for personalized medicine that relies on the cloud infrastructure of the REte di CAlcolo per SuperB ed altre applicazioni (ReCaS)-Bari data centre. PERSON developed a completely open source and innovative framework to interface the game device with the computational resources in the cloud. We exploited the container technology and the Software as a Service (SaaS) paradigm to implement a genetic algorithm that analyses the neural responses in EEG recordings. The paper focuses on technical aspects of the designed tools. A test was conducted on a few volunteers for the purpose of tuning the overall system. The paper does not contain results of a clinical trial as this is planned in a second testing phase, when the user’s perception of the system will also be tested.

Concept, development and evaluation of a mind action game with the electro encephalograms as an auxiliary input

Games are interactive applications that require input devices in order to send messages for the interaction. Normally this input devices are mouse, keyboards and joysticks. Lately, this input has been done in different ways, such as voice, touch and movement with new input devices. One type of input that has not been very explored is the use of the brain waves as a input for the game. While in past these devices where expensive, nowadays Brain Computer Interface (BCI) have become accessible, cheap and can be acquired with nonintrusive top off-the-shelf products, which can create a new paradigm of interaction for games. This work presents a novel architecture and framework that can help the development of games with both BCI and traditional interfaces. As a proof of concept, this paper shows the experience in designing and developing a game prototype using the framework and EEG brainwaves as one of the players input. The game is an action slice game, similar to Fruit Ninja, called MindNinja. This game differ form most BCI game, since it is based on an action game, using touch input where the BCI is used as an auxiliary input to change the game behavior. This game was tested and evaluated with a group of person, showing promising results in the fun level, as well as increasing the attention level of subjects.

Brain-computer interface for individuals after spinal cord injury.

To investigate the feasibility of brain-computer interface (BCI) with patients on an inpatient spinal cord injury (SCI) unit. Research Method/Design: This study included 25 participants aged 18-64 who sustained traumatic or nontraumatic SCI and did not have severe cognitive or psychiatric impairment. Participants completed a variety of screening measures related to cognition, psychological disposition, pain, and technology experience/interest. The Emotiv electroencephalography system was used in conjunction with a cube rotation and manipulation game presented on a laptop computer. The majority of participants successfully completed the BCI game and reported enjoyment of the experience. Outside of a mild trend of lower performance among participants with a past or present head injury, there were no demographic variables, injury variables or screening measures significantly associated with BCI performance. The BCI paradigm demonstrated feasibility and safety across participant age range, educational and vocational background, and level of injury. Despite the rapid integration of technology into rehabilitation health care settings, there are few evidence-based studies regarding the feasibility of technology with specific inpatient populations. Clinical implications and challenges of using this technology in a rehabilitation setting are discussed. (PsycINFO Database Record