Combining computer game-based behavioural experiments with high-density EEG and infrared gaze tracking

Rigorous, quantitative examination of therapeutic techniques anecdotally reported to have been successful in people with autism who lack communicative speech will help guide basic science toward a more complete characterisation of the cognitive profile in this underserved subpopulation, and show the extent to which theories and results developed with the high-functioning subpopulation may apply. This study examines a novel therapy, the "Rapid Prompting Method" (RPM). RPM is a parent-developed communicative and educational therapy for persons with autism who do not speak or who have difficulty using speech communicatively.The technique aims to develop a means of interactive learning by pointing amongst multiple-choice options presented at different locations in space, with the aid of sensory "prompts" which evoke a response without cueing any specific response option. The prompts are meant to draw and to maintain attention to the communicative task–making the communicative and educational content coincident with the most physically salient, attention-capturing stimulus – and to extinguish the sensory–motor preoccupations with which the prompts compete.ideo-recorded RPM sessions with nine autistic children ages 8–14years who lacked functional communicative speech were coded for behaviours of interest

Bacteria Hunt: Evaluating multi-paradigm BCI interaction

The multimodal, multi-paradigm brain-computer interfacing (BCI) game Bacteria Hunt was used to evaluate two aspects of BCI interaction in a gaming context. One goal was to examine the effect of feedback on the ability of the user to manipulate his mental state of relaxation. This was done by having one condition in which the subject played the game with real feedback, and another with sham feedback. The feedback did not seem to affect the game experience (such as sense of control and tension) or the objective indicators of relaxation, alpha activity and heart rate. The results are discussed with regard to clinical neurofeedback studies. The second goal was to look into possible interactions between the two BCI paradigms used in the game: steady-state visually-evoked potentials (SSVEP) as an indicator of concentration, and alpha activity as a measure of relaxation. SSVEP stimulation activates the cortex and can thus block the alpha rhythm. Despite this effect, subjects were able to keep their alpha power up, in compliance with the instructed relaxation task. In addition to the main goals, a new SSVEP detection algorithm was developed and evaluated

Proof of concept of a workflow methodology for the creation of basic canine head anatomy veterinary education tool using augmented reality

Neuroanatomy can be challenging to both teach and learn within the undergraduate veterinary medicine and surgery curriculum. Traditional techniques have been used for many years, but there has now been a progression to move towards alternative digital models and interactive 3D models to engage the learner. However, digital innovations in the curriculum have typically involved the medical curriculum rather than the veterinary curriculum. Therefore, we aimed to create a simple workflow methodology to highlight the simplicity there is in creating a mobile augmented reality application of basic canine head anatomy. Using canine CT and MRI scans and widely available software programs, we demonstrate how to create an interactive model of head anatomy. This was applied to augmented reality for a popular Android mobile device to demonstrate the user-friendly interface. Here we present the processes, challenges and resolutions for the creation of a highly accurate, data based anatomical model that could potentially be used in the veterinary curriculum. This proof of concept study provides an excellent framework for the creation of augmented reality training products for veterinary education. The lack of similar resources within this field provides the ideal platform to extend this into other areas of veterinary education and beyond

Learning by volunteer computing, thinking and gaming: What and how are volunteers learning by participating in Virtual Citizen Science?

Citizen Science (CS) refers to a form of research collaboration that engages volunteers without formal scientific training in contributing to empirical scientific projects. Virtual Citizen Science (VCS) projects engage participants in online tasks. VCS has demonstrated its usefulness for research, however little is known about its learning potential for volunteers. This paper reports on research exploring the learning outcomes and processes in VCS. In order to identify different kinds of learning, 32 exploratory interviews of volunteers were conducted in three different VCS projects. We found six main learning outcomes related to different participants' activities in the project. Volunteers learn on four dimensions that are directly related to the scope of the VCS project: they learn at the task/game level, acquire pattern recognition skills, on-topic content knowledge, and improve their scientific literacy. Thanks to indirect opportunities of VCS projects, volunteers learn on two additional dimensions: off topic knowledge and skills, and personal development. Activities through which volunteers learn can be categorized in two levels: at a micro (task/game) level that is direct participation to the task, and at a macro level, i.e. use of project documentation, personal research on the Internet, and practicing specific roles in project communities. Both types are influenced by interactions with others in chat or forums. Most learning happens to be informal, unstructured and social. Volunteers do not only learn from others by interacting with scientists and their peers, but also by working for others: they gain knowledge, new status and skills by acting as active participants, moderators, editors, translators, community managers, etc. in a project community. This research highlights these informal and social aspects in adult learning and science education and also stresses the importance for learning through the indirect opportunities provided by the project: the main one being the opportunity to participate and progress in a project community, according to one's tastes and skills

Supervised ANN vs. unsupervised SOM to classify EEG data for BCI: why can GMDH do better?

Construction of a system for measuring the brain activity (electroencephalogram (EEG)) and recognising thinking patterns comprises significant challenges, in addition to the noise and distortion present in any measuring technique. One of the most major applications of measuring and understanding EGG is the brain-computer interface (BCI) technology. In this paper, ANNs (feedforward back -prop and Self Organising Maps) for EEG data classification will be implemented and compared to abductive-based networks, namely GMDH (Group Methods of Data Handling) to show how GMDH can optimally (i.e. noise and accuracy) classify a given set of BCI’s EEG signals. It is shown that GMDH provides such improvements. In this endeavour, EGG classification based on GMDH will be researched for comprehensible classification without scarifying accuracy. GMDH is suggested to be used to optimally classify a given set of BCI’s EEG signals. The other areas related to BCI will also be addressed yet within the context of this purpose

Bacteria Hunt: A multimodal, multiparadigm BCI game

Brain-Computer Interfaces (BCIs) allow users to control applications by brain activity. Among their possible applications for non-disabled people, games are promising candidates. BCIs can enrich game play by the mental and affective state information they contain. During the eNTERFACE’09 workshop we developed the Bacteria Hunt game which can be played by keyboard and BCI, using SSVEP and relative alpha power. We conducted experiments in order to investigate what difference positive vs. negative neurofeedback would have on subjects’ relaxation states and how well the different BCI paradigms can be used together. We observed no significant difference in mean alpha band power, thus relaxation, and in user experience between the games applying positive and negative feedback. We also found that alpha power before SSVEP stimulation was significantly higher than alpha power during SSVEP stimulation indicating that there is some interference between the two BCI paradigms

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Ideation as an intellectual information acquisition and use context: Investigating game designers’ information-based ideation behavior

Human Information Behavior (HIB) research commonly examines behavior in the context of why information is acquired and how it will be used, but usually at the level of the work or everyday-life tasks the information will support. HIB has not been examined in detail at the broader contextual level of intellectual purpose (i.e. the higher-order conceptual tasks the information was acquired to support). Examination at this level can enhance holistic understanding of HIB as a ‘means to an intellectual end’ and inform the design of digital information environments that support information interaction for specific intellectual purposes. We investigate information-based ideation (IBI) as a specific intellectual information acquisition and use context by conducting Critical Incident-style interviews with ten game designers, focusing on how they interact with information to generate and develop creative design ideas. Our findings give rise to a framework of their ideation-focused HIB, which systems designers can leverage to reason about how best to support certain behaviors to drive design ideation. These findings emphasize the importance of intellectual purpose as a driver for acquisition and desired outcome of use