The past twenty years have ignited a new spark in the research of Electroencephalogram (EEG), which was pursued to develop innovative Brain Computer Interfaces (BCIs) in order to help severely disabled people live a better life with a high degree of independence. Current BCIs are more theoretical than practical and are suffering from numerous challenges. New trends of research propose combining EEG to other simple and efficient bioelectric inputs such as Electro-oculography (EOG) resulting from eye movements, to produce more practical and robust Hybrid Brain Computer Interface systems (hBCI) or Brain/Neuronal Computer Interface (BNCI). Working towards this purpose, existing research in EOG based Human Computer Interaction (HCI) applications, must be organized and surveyed in order to develop a vision on the potential benefits of combining both input modalities and give rise to new designs that maximize these benefits. Our aim is to support and inspire the design of new hBCI systems based on both EEG and EOG signals, in doing so; first the current EOG based HCI systems were surveyed with a particular focus on EOG based systems for communication using virtual keyboard. Then, a survey of the current EEG-EOG virtual keyboard was performed highlighting the design protocols employed. We concluded with a discussion of the potential advantages of combining both systems with recommendations to give deep insight for future design issues for all EEG-EOG hBCI systems. Finally, a general architecture was proposed for a new EEG-EOG hBCI system. The proposed hybrid system completely alters the traditional view of the eye movement features present in EEG signal as artifacts that should be removed; instead EOG traces are extracted from EEG in our proposed hybrid architecture and are considered as an additional input modality sharing control according to the chosen design protocol.
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http://dx.doi.org/10.1007/s12021-018-9402-0 | DOI Listing |
J Med Internet Res
October 2024
Department of Psychiatry, University of Illinois Chicago, Chicago, IL, United States.
Background: Passive sensing through smartphone keyboard data can be used to identify and monitor symptoms of mood disorders with low participant burden. Behavioral phenotyping based on mobile keystroke data can aid in clinical decision-making and provide insights into the individual symptoms of mood disorders.
Objective: This study aims to derive digital phenotypes based on smartphone keyboard backspace use among 128 community adults across 2948 observations using a Bayesian mixture model.
PLoS One
October 2024
Department of Mathematics and Computer Science, University of Douala, Douala, Cameroon.
This study presents a novel multi-stage hierarchical approach to optimize key selection on virtual keyboards using eye gaze. Existing single-stage selection algorithms have difficulty with distant keys on large interfaces. The proposed technique divides the standard QWERTY keyboard into progressively smaller regions guided by eye movements, with boundary fixations first selecting halves and quarters to sequentially narrow the search area.
View Article and Find Full Text PDFSensors (Basel)
October 2024
Équipes de Traitement de l'Information et Systèmes, UMR 8051, CY Cergy Paris Université, École Nationale Supérieure de l'Electronique et de ses Applications (ENSEA), Centre National de la Recherche Scientifique (CNRS), 95000 Cergy, France.
This study introduces an integrated computational environment that leverages Brain-Computer Interface (BCI) technology to enhance information access for individuals with severe disabilities. Traditional assistive technologies often rely on physical interactions, which can be challenging for this demographic. Our innovation focuses on creating new assistive technologies that use novel Human-Computer interfaces to provide a more intuitive and accessible experience.
View Article and Find Full Text PDFText entry with word-gesture keyboards (WGK) is emerging as a popular method and becoming a key interaction for Extended Reality (XR). However, the diversity of interaction modes, keyboard sizes, and visual feedback in these environments introduces divergent word-gesture trajectory data patterns, thus leading to complexity in decoding trajectories into text. Template-matching decoding methods, such as SHARK2 [32], are commonly used for these WGK systems because they are easy to implement and configure.
View Article and Find Full Text PDFIEEE Trans Vis Comput Graph
November 2024
Authentication in digital security relies heavily on text-based passwords, even with other available methods like biometrics and graphical passwords. While virtual reality (VR) keyboards are typically invisible to onlookers, the presence of inconspicuous sensors, including accelerometers, gyroscopes, and barometers, poses a potential risk of unauthorized observation and recording. Traditional defense shoulder-surfing attack methods typically involve breaking apart the Qwerty layout, which destroys the user's inherent familiarity with the layout.
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