# User-driven Intelligent Interface on the Basis of Multimodal Augmented   Reality and Brain-Computer Interaction for People with Functional   Disabilities

**Authors:** S. Stirenko, Yu. Gordienko, T. Shemsedinov, O. Alienin, Yu. Kochura,, N. Gordienko, A. Rojbi, J.R. L\'opez Benito, E. Artetxe Gonz\'alez

arXiv: 1704.05915 · 2018-12-11

## TL;DR

This paper proposes a user-driven intelligent interface combining multimodal augmented reality and brain-computer interaction to enhance accessibility and provide immediate neurophysical feedback, especially benefiting individuals with disabilities.

## Contribution

It introduces a novel integration of multimodal AR and BCI technologies for user-driven interfaces, emphasizing applications for people with functional disabilities.

## Key findings

- BCI technology offers new strategies to overcome current interface limits.
- Combining ML, multimodal interactions, and BCI enhances feedback and information delivery.
- AR-BCI interfaces can provide highly adaptable, personalized services for disabled users.

## Abstract

The analysis of the current integration attempts of some modes and use cases of user-machine interaction is presented. The new concept of the user-driven intelligent interface is proposed on the basis of multimodal augmented reality and brain-computer interaction for various applications: in disabilities studies, education, home care, health care, etc. The several use cases of multimodal augmentation are presented. The perspectives of the better human comprehension by the immediate feedback through neurophysical channels by means of brain-computer interaction are outlined. It is shown that brain-computer interface (BCI) technology provides new strategies to overcome limits of the currently available user interfaces, especially for people with functional disabilities. The results of the previous studies of the low end consumer and open-source BCI-devices allow us to conclude that combination of machine learning (ML), multimodal interactions (visual, sound, tactile) with BCI will profit from the immediate feedback from the actual neurophysical reactions classified by ML methods. In general, BCI in combination with other modes of AR interaction can deliver much more information than these types of interaction themselves. Even in the current state the combined AR-BCI interfaces could provide the highly adaptable and personal services, especially for people with functional disabilities.

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Source: https://tomesphere.com/paper/1704.05915