Introduction: Due to the increasing popularization of computers and the internet expansion, Alternative and Augmentative Communication technologies have been employed to restore the ability to communicate of people with aphasia and tetraplegia. Virtual keyboards are one of the most primitive mechanisms for alternatively entering text and play a very important role in accomplishing this task. However, the text entry for this kind of keyboard is much slower than entering information through their physical counterparts. Many techniques and layouts have been proposed to improve the typing performance of virtual keyboards, each one concerning a different issue or solving a specific problem. However, not all of them are suitable to assist seriously people with motor impairment. Methods: In order to develop an assistive virtual keyboard with improved typing performance, we performed a systematic review on scientific databases. Results: We found 250 related papers and 52 of them were selected to compose. After that, we identified eight essentials virtual keyboard features, five methods to optimize data entry performance and five metrics to assess typing performance. Conclusion: Based on this review, we introduce a concept of an assistive, optimized, compact and adaptive virtual keyboard that gathers a set of suitable techniques such as: a new ambiguous keyboard layout, disambiguation algorithms, dynamic scan techniques, static text prediction of letters and words and, finally, the use of phonetic and similarity algorithms to reduce the user's typing error rate.

/pdf/a-new-concept-of-assistive-virtual-keyboards-based-on-a-5cdec18epa.pdf

A new concept of assistive virtual keyboards based on a systematic review of text entry optimization techniques

Most of current brain computer interfaces (BCI) designed for environment control or navigation are usually made using motor imagery and expensive data acquisition equipment. This study presents an alternative way of utilizing Electroencephalogram (EEG) signals acquired through a low-cost headset to steer a virtual object. In this paper, a preliminary framework of controlling the end effector of a 7 degree of freedom (DoF) virtual robot manipulator by EEG signals and the P300 speller BCI are built. The EEG signals emitted by the user brain are collected by the Emotiv EPOC neuro-headset, which is connected to the OpenViBE platform, where a BCI is designed to retrieve and process the EEG recording in real time. P300 is an observed peak evoked 250 to 500 ms after a visual stimulus in an EEG signal. The P300 speller BCI is an OpenViBE scenario that allows the user to spell letters using P300 signals. The main idea is to associate a letter with a command to control the virtual robot, where the six letters are corresponding to 6 robot movements in the Cartesian space: +X, −X, +Y, −Y, +Z, −Z. Letters are sent via a VRPN server on Open Vibe platform and transmitted via a C++ client (designed on Microsoft Visual Studio 2010) to V-REP, a software for creating and testing virtual robot. V-REP remote API is used on the C++ client to control directly the end effector. Online experiments have been performed to test the system performance and the functionality.

Mind guided motion control of robot manipulator using EEG signals

Electroencephalograph (EEG) based BCI (Brain-Computer Interfacing) paradigm has become a research topic in computer science and, in particular, human-computer interaction. In this paradigm, BCI applications have been largely focused on improving the quality of life for both able-bodied users as well as users with special needs. Despite the fact that low-cost off-the-shelf EEG headsets are becoming more accessible by and affordable to researchers to conduct research with, the past few years have witnessed unparalleled efforts to make EEG based BCI applications accessible using these headsets. This paper focuses on the challenges associated with developing EEG based BCI control applications, using low-cost off-the-shelf EEG headsets in controlled experiments, and usability issues with the software. We report experiences from several research studies and the barriers that hinder the full utilization of EEG BCI off-the-shelf headset in research contexts.

http://ieeexplore.ieee.org/iel7/6653326/6661709/06661741.pdf

Trials and tribulations of BCI control applications

IntroductionLocked-In Syndrome is admittedly the worst case of motor and speech impairment, it seriously damages the ability of oral and gestural communication of patients. In recent years, alternative and augmentative communication technology has provided resources to restore these patients' ability to communicate.MethodsIn order to relate and classify the main methods with that purpose, this work conducted a systematic review on several journal databases.ResultsWe found 203 related papers and 55 of them were selected to compose the study. After that, we classified them into three major groups and we identified the main difficulties when using each approach.ConclusionIn order to overcome these difficulties, we propose a new system concept to develop an adaptive, robust and low cost communication environment. The proposed system is composed of five modules: data entry, communication, aid to the caregiver and external interaction.

/pdf/a-concept-environment-for-computer-based-augmentative-and-2nam2xufar.pdf

A concept-environment for computer-based augmentative and alternative communication founded on a systematic review

Event-related potentials as a form of neurofeedback using low-cost hardware

This paper presents the findings of an exploratory study comparing two of Brain-Computer Interface approaches, P300 and Motor Imagery, with EEG signals acquired using the Emotiv Neuroheadset. It was conducted to determine the most suitable approach for typing applications based on BCI. Results show that while selection accuracy is similar for both, with mean of 50%, the speed varies greatly, with the former approach being approximately 2 times more efficient in typing. Implications presented in this document are useful for BCI researchers who seek to build brain-controlled Augmentative and Alternative Communication technologies.

/pdf/brainwave-typing-comparative-study-of-p300-and-motor-imagery-4u4ewg6bvz.pdf

Brainwave Typing: Comparative Study of P300 and Motor Imagery for Typing Using Dry-Electrode EEG Devices

This paper describes the research conducted to design the Arabic Brain Communicator (ABC), which is a brain-controlled typing system designed to facilitate communication for people with severe motor disabilities in Arabic. A user centered design was adopted; it included empirical investigations and meetings with Subject-Matter Experts and possible users. Activities conducted in the analysis and design of the system are discussed.

Mind-controlled augmentative and alternative communication for people with severe motor disabilities

Frequent exposure to disturbing content on social media such as posts, sharing, accident news, or even photos of puppies could adversely impact users’ online experience or well-being. Several protection mechanisms exist to provide users with control over content feeding into their personal spaces; such as sensitive and “show less often” markers. Better understanding of users’ conceptions of disturbing content is a prerequisite to providing users with their desired level of privacy. As a first step, we designed a protocol combining two requirement elicitation techniques: affinity diagram and card sorting. In this paper, we report our protocol and reflect on a pilot with two transnational Saudi women groups. Finally, we suggest possible future research directions to further improve our understanding of Arab users’ needs and practices in the context of personal space maintenance.

Aljawharah Al-Abdullatif

Papers

Brainwave Typing: Comparative Study of P300 and Motor Imagery for Typing Using Dry-Electrode EEG Devices

Mind-controlled augmentative and alternative communication for people with severe motor disabilities

Saudis' Conceptualisation of Disturbing Content on Social Media