Integration of Assistive Technologies into 3D Simulations: Exploratory Studies

Virtual worlds and environments have many purposes, ranging from games to scientific research. However, universal accessibility features in such virtual environments are limited. As the impairment prevalence rate increases yearly, so does the research interests in the field of assistive technologies. This work introduces research in assistive technologies and presents three software developments that explore the integration of assistive technologies within virtual environments, with a strong focus on Brain-Computer Interfaces. An accessible gaming system, a hands-free navigation software system, and a Brain-Computer Interaction plugin have been developed to study the capabilities of accessibility features within virtual 3D environments. Details of the specification, design, and implementation of these software applications are presented in the thesis. Observations and preliminary results as well as directions of future work are also included

How Visual Stimuli Evoked P300 is Transforming the Brain–Computer Interface Landscape: A PRISMA Compliant Systematic Review

Non-invasive Visual Stimuli evoked-EEGbased P300 BCIs have gained immense attention in recent years due to their ability to help patients with disability using BCI-controlled assistive devices and applications. In addition to the medical field, P300 BCI has applications in entertainment, robotics, and education. The current article systematically reviews 147 articles that were published between 2006-2021*. Articles that pass the pre-defined criteria are included in the study. Further, classification based on their primary focus, including article orientation, participants’ age groups, tasks given, databases, the EEG devices used in the studies, classification models, and application domain, is performed. The application-based classification considers a vast horizon, including medical assessment, assistance, diagnosis, applications, robotics, entertainment, etc. The analysis highlights an increasing potential for P300 detection using visual stimuli as a prominent and legitimate research area and demonstrates a significant growth in the research interest in the field of BCI spellers utilizing P300. This expansion was largely driven by the spread of wireless EEG devices, advances in computational intelligence methods, machine learning, neural networks and deep learning

Brain-Computer Interfaces: Beyond Medical Applications

International audienceBrain-computer interaction has already moved from assistive care to applications such as gaming. Improvements in usability, hardware, signal processing, and system integration should yield applications in other nonmedical areas

Study of non-invasive cognitive tasks and feature extraction techniques for brain-computer interface (BCI) applications

A brain-computer interface (BCI) provides an important alternative for disabled people that enables the non-muscular communication pathway among individual thoughts and different assistive appliances. A BCI technology essentially consists of data acquisition, pre-processing, feature extraction, classification and device command. Indeed, despite the valuable and promising achievements already obtained in every component of BCI, the BCI field is still a relatively young research field and there is still much to do in order to make BCI become a mature technology. To mitigate the impediments concerning BCI, the study of cognitive task together with the EEG feature and classification framework have been investigated. There are four distinct experiments have been conducted to determine the optimum solution to those specific issues. In the first experiment, three cognitive tasks namely quick math solving, relaxed and playing games have been investigated. The features have been extracted using power spectral density (PSD), logenergy entropy, and spectral centroid and the extracted feature has been classified through the support vector machine (SVM), K-nearest neighbor (K-NN), and linear discriminant analysis (LDA). In this experiment, the best classification accuracy for single channel and five channel datasets were 86% and 91.66% respectively that have been obtained by the PSD-SVM approach. The wink based facial expressions namely left wink, right wink and no wink have been studied through fast Fourier transform (FFT) and sample range feature and then the extracted features have been classified using SVM, K-NN, and LDA. The best accuracy (98.6%) has been achieved by the sample range-SVM based approach. The eye blinking based facial expression has been investigated following the same methodology as the study of wink based facial expression. Moreover, the peak detection approach has also been employed to compute the number of blinks. The optimum accuracy of 99% has been achieved using the peak detection approach. Additionally, twoclass motor imagery hand movement has been classified using SVM, K-NN, and LDA where the feature has been extracted through PSD, spectral centroid and continuous wavelet transform (CWT). The optimum 74.7% accuracy has been achieved by the PSDSVM approach. Finally, two device command prototypes have been designed to translate the classifier output. One prototype can translate four types of cognitive tasks in terms of 5 watts four different colored bulbs, whereas, another prototype may able to control DC motor utilizing cognitive tasks. This study has delineated the implementation of every BCI component to facilitate the application of brainwave assisted assistive appliances. Finally, this thesis comes to the end by drawing the future direction regarding the current issues of BCI technology and these directions may significantly enhance usability for the implementation of commercial applications not only for the disabled but also for a significant number of healthy users

Potential of consumer EEG for real-time interactions in immersive VR

Abstract. Virtual reality is an active research subject and has received a lot of attention over the last few years. We have seen multiple commercial VR devices, each improving upon the last iteration become available to the wider public. In addition, interest in brain-computer interface (BCI) devices has increased rapidly. As these devices are becoming more affordable and easy to use, we are presented with more accessible options to measure brain activity. In this study, our aim is to combine these two technologies to enhance the interaction within a virtual environment. In this study we sought to facilitate interaction in VR by using EEG signals. The EEG signals were used to estimate the volume of focus. By applying this concept with VR, we designed two use cases for further exploration. The methods of interactions explored in the study were telekinesis and teleportation. Telekinesis seemed an applicable option for this study since it allows the utilization of the EEG while maintaining a captivating and engaging user experience. With teleportation, the goal was the exploration of different options for locomotion in VR. To test our solution, we built a test environment by using Unity engine. We also invited several participants to gain feedback on the usability and accuracy of our methodology. For evaluation, 13 study participants were divided into two different groups. The other group tested our actual solution for the estimation of the focus. However, the other group used randomized values for the same purpose. Some key differences between the test groups were identified. We were able to create a working prototype where the users could interact with the environment by using their EEG signals. With some improvements, this could be expanded to a more refined solution with a better user experience. There is a lot of potential in combining the use of human brain signals with virtual environments to both enrich the interaction and increase the immersion of virtual reality.Kuluttaja-EEG laitteiden potentiaali reaaliaikaiseen vuorovaikutukseen immersiivisessä virtuaalitodellisuudessa. Tiivistelmä. Virtuaalitodellisuus (VR) on aktiivisen tutkimuksen kohde ja varsinkin viime vuosina herättänyt paljon huomiota. VR-laseissa on tapahtunut huomattavaa kehitystä ja niitä on saatavilla yhä laajemmalle käyttäjäkunnalle. Lisäksi kiinnostus aivo-tietokone -rajapintoihin (BCI) on kiihtymässä. Koska aivokäyrää mittaavat laitteet ovat yhä edullisempia ja kehittymässä helppokäyttöisemmiksi, monia uusia menetelmiä aivosignaalin mittamiseksi on saatavilla. Tässä työssä tavoitteemme oli yhdistää nämä kaksi teknologiaa parantaaksemme vuorovaikutusta virtuaalitodellisuudessa. Tässä tutkimuksessa käytimme aivosähkökäyrää VR-käyttäjäkokemuksen kehittämiseksi. Tätä tekniikkaa hyödyntäen arvioimme käyttäjän keskittymistä. Tutkimusta varten valitsimme kaksi vuorovaikutustapaa. Nämä tutkittavat tavat ovat telekinesia sekä teleportaatio. Telekinesia on mielenkiintoinen tapa hyödyntää aivosähkökäyrää luoden samalla mukaansatempaavan käyttäjäkokemuksen. Teleportaation päämääränä oli löytää uudenlaisia liikkumistapoja VR:ssä. Tutkimustamme varten, suunnittelimme testiympäristön Unity-pelimoottorilla. Kokosimme joukon testaajia, joiden avulla arvioimme työmme käyttökelpoisuutta sekä tarkkuutta. Saadaksemme luotettavampia testituloksia, jaoimme 13 testaajaa kahteen eri ryhmään. Toinen ryhmistä testasi varsinaista toteutustamme ja toinen ryhmä käytti satunnaistettuja keskittymisarvoja. Löysimme ratkaisevia eroja näiden kahden testiryhmän välillä. Onnistuimme kehittämään toimivan prototyypin, jossa käyttäjät kykenivät interaktioon virtuaaliympäristössä hyödyntäen aivosähkökäyrää. Jatkokehitystä tekemällä käyttäjäkokemusta olisi mahdollista parantaa entisestään. Integraatio aivosensoreiden ja virtuaalitodellisuuden välillä huokuu potentiaalia ja tarjoaa mahdollisuuksia tehdä virtuaalimaailmasta yhä immersiivisemmän

TOWARDS STEADY-STATE VISUALLY EVOKED POTENTIALS BRAIN-COMPUTER INTERFACES FOR VIRTUAL REALITY ENVIRONMENTS EXPLICIT AND IMPLICIT INTERACTION

In the last two decades, Brain-Computer Interfaces (BCIs) have been investigated mainly for the purpose of implementing assistive technologies able to provide new channels for communication and control for people with severe disabilities. Nevertheless, more recently, thanks to technical and scientific advances in the different research fields involved, BCIs are gaining greater attention also for their adoption by healthy users, as new interaction devices. This thesis is dedicated to to the latter goal and in particular will deal with BCIs based on the Steady State Visual Evoked Potential (SSVEP), which in previous works demonstrated to be one of the most flexible and reliable approaches. SSVEP based BCIs could find applications in different contexts, but one which is particularly interesting for healthy users, is their adoption as new interaction devices for Virtual Reality (VR) environments and Computer Games. Although being investigated since several years, BCIs still poses several limitations in terms of speed, reliability and usability with respect to ordinary interaction devices. Despite of this, they may provide additional, more direct and intuitive, explicit interaction modalities, as well as implicit interaction modalities otherwise impossible with ordinary devices. This thesis, after a comprehensive review of the different research fields being the basis of a BCI exploiting the SSVEP modality, present a state-of-the-art open source implementation using a mix of pre-existing and custom software tools. The proposed implementation, mainly aimed to the interaction with VR environments and Computer Games, has then been used to perform several experiments which are hereby described as well. Initially performed experiments aim to stress the validity of the provided implementation, as well as to show its usability with a commodity bio-signal acquisition device, orders of magnitude less expensive than commonly used ones, representing a step forward in the direction of practical BCIs for end users applications. The proposed implementation, thanks to its flexibility, is used also to perform novel experiments aimed to investigate the exploitation of stereoscopic displays to overcome a known limitation of ordinary displays in the context of SSVEP based BCIs. Eventually, novel experiments are presented investigating the use of the SSVEP modality to provide also implicit interaction. In this context, a first proof of concept Passive BCI based on the SSVEP response is presented and demonstrated to provide information exploitable for prospective applications