Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges

In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices

Brain-Computer Interfaces using Machine Learning

This thesis explores machine learning models for the analysis and classification of electroencephalographic (EEG) signals used in Brain-Computer Interface (BCI) systems. The goal is 1) to develop a system that allows users to control home-automation devices using their mind, and 2) to investigate whether it is possible to achieve this, using low-cost EEG equipment. The thesis includes both a theoretical and a practical part. In the theoretical part, we overview the underlying principles of Brain-Computer Interface systems, as well as, different approaches for the interpretation and the classification of brain signals. We also discuss the emergent launch of low-cost EEG equipment on the market and its use beyond clinical research. We then dive into more technical details that involve signal processing and classification of EEG patterns using machine leaning. Purpose of the practical part is to create a brain-computer interface that will be able to control a smart home environment. As a first step, we investigate the generalizability of different classification methods, conducting a preliminary study on two public datasets of brain encephalographic data. The obtained accuracy level of classification on 9 different subjects was similar and, in some cases, superior to the reported state of the art. Having achieved relatively good offline classification results during our study, we move on to the last part, designing and implementing an online BCI system using Python. Our system consists of three modules. The first module communicates with the MUSE (a low-cost EEG device) to acquire the EEG signals in real time, the second module process those signals using machine learning techniques and trains a learning model. The model is used by the third module, that takes control of cloud-based home automation devices. Experiments using the MUSE resulted in significantly lower classification results and revealed the limitations of the low-cost EEG signal acquisition device for online BCIs

Emerging ExG-based NUI Inputs in Extended Realities : A Bottom-up Survey

Incremental and quantitative improvements of two-way interactions with extended realities (XR) are contributing toward a qualitative leap into a state of XR ecosystems being efficient, user-friendly, and widely adopted. However, there are multiple barriers on the way toward the omnipresence of XR; among them are the following: computational and power limitations of portable hardware, social acceptance of novel interaction protocols, and usability and efficiency of interfaces. In this article, we overview and analyse novel natural user interfaces based on sensing electrical bio-signals that can be leveraged to tackle the challenges of XR input interactions. Electroencephalography-based brain-machine interfaces that enable thought-only hands-free interaction, myoelectric input methods that track body gestures employing electromyography, and gaze-tracking electrooculography input interfaces are the examples of electrical bio-signal sensing technologies united under a collective concept of ExG. ExG signal acquisition modalities provide a way to interact with computing systems using natural intuitive actions enriching interactions with XR. This survey will provide a bottom-up overview starting from (i) underlying biological aspects and signal acquisition techniques, (ii) ExG hardware solutions, (iii) ExG-enabled applications, (iv) discussion on social acceptance of such applications and technologies, as well as (v) research challenges, application directions, and open problems; evidencing the benefits that ExG-based Natural User Interfaces inputs can introduceto the areaof XR.Peer reviewe

Emerging ExG-based NUI Inputs in Extended Realities : A Bottom-up Survey

Incremental and quantitative improvements of two-way interactions with extended realities (XR) are contributing toward a qualitative leap into a state of XR ecosystems being efficient, user-friendly, and widely adopted. However, there are multiple barriers on the way toward the omnipresence of XR; among them are the following: computational and power limitations of portable hardware, social acceptance of novel interaction protocols, and usability and efficiency of interfaces. In this article, we overview and analyse novel natural user interfaces based on sensing electrical bio-signals that can be leveraged to tackle the challenges of XR input interactions. Electroencephalography-based brain-machine interfaces that enable thought-only hands-free interaction, myoelectric input methods that track body gestures employing electromyography, and gaze-tracking electrooculography input interfaces are the examples of electrical bio-signal sensing technologies united under a collective concept of ExG. ExG signal acquisition modalities provide a way to interact with computing systems using natural intuitive actions enriching interactions with XR. This survey will provide a bottom-up overview starting from (i) underlying biological aspects and signal acquisition techniques, (ii) ExG hardware solutions, (iii) ExG-enabled applications, (iv) discussion on social acceptance of such applications and technologies, as well as (v) research challenges, application directions, and open problems; evidencing the benefits that ExG-based Natural User Interfaces inputs can introduceto the areaof XR.Peer reviewe

Past, Present, and Future of EEG-Based BCI Applications

An electroencephalography (EEG)-based brain–computer interface (BCI) is a system that provides a pathway between the brain and external devices by interpreting EEG. EEG-based BCI applications have initially been developed for medical purposes, with the aim of facilitating the return of patients to normal life. In addition to the initial aim, EEG-based BCI applications have also gained increasing significance in the non-medical domain, improving the life of healthy people, for instance, by making it more efficient, collaborative and helping develop themselves. The objective of this review is to give a systematic overview of the literature on EEG-based BCI applications from the period of 2009 until 2019. The systematic literature review has been prepared based on three databases PubMed, Web of Science and Scopus. This review was conducted following the PRISMA model. In this review, 202 publications were selected based on specific eligibility criteria. The distribution of the research between the medical and non-medical domain has been analyzed and further categorized into fields of research within the reviewed domains. In this review, the equipment used for gathering EEG data and signal processing methods have also been reviewed. Additionally, current challenges in the field and possibilities for the future have been analyzed

Brain-Switches for Asynchronous Brain−Computer Interfaces: A Systematic Review

A brain–computer interface (BCI) has been extensively studied to develop a novel communication system for disabled people using their brain activities. An asynchronous BCI system is more realistic and practical than a synchronous BCI system, in that, BCI commands can be generated whenever the user wants. However, the relatively low performance of an asynchronous BCI system is problematic because redundant BCI commands are required to correct false-positive operations. To significantly reduce the number of false-positive operations of an asynchronous BCI system, a two-step approach has been proposed using a brain-switch that first determines whether the user wants to use an asynchronous BCI system before the operation of the asynchronous BCI system. This study presents a systematic review of the state-of-the-art brain-switch techniques and future research directions. To this end, we reviewed brain-switch research articles published from 2000 to 2019 in terms of their (a) neuroimaging modality, (b) paradigm, (c) operation algorithm, and (d) performance