Brain-computer Interaction Enabled AAC for Visual Interactive Paradigm

Author:

Liu Yaming1,Zhou Zihan2,Wang Aihong2,Samuel R. Dinesh Jackson3,Kumar Priyanmalarvizhi4

Affiliation:

1. College of Design, Beijing Normal University, Zhuhai 519000, China

2. School of Art and Design, Jingdezhen Ceramic Institute, Jingdezhen 333001, China

3. Faculty of Technology, Design and Environment, Visual Artificial Intelligence Lab Oxford Brookes University, Oxford, United Kingdom

4. Department of Computer Science and Engineering, Kyung Hee University, South Korea

Abstract

The brain-computer interface (BCI) has recently provided a potential means for individuals with the least movement to control a computer utilizing their brain waves, with no motor output needed. Augmentative and alternative communication (AAC) is generally utilized by individuals with severe physical and speech disabilities and is one of the primary application fields for BCI technologies. The main objective of this study is to examine students’ brain parameters such as attention, concentration, and the energy of several brain waves using Augmentative and Alternative Communication-based Visual Interactive Paradigm (AAC-VIP) based on BCI in education systems. Particular emphasis is placed on integrating AAC into daily school life to foster every student’s access to and participation in the education curriculum. The effectiveness of the support plans has been assessed via behavioral observations and team interviews. The experimental findings demonstrate that the proposed model allows the completion of communication, with the highest interaction rate of 97.66%. It can be utilized in the classrooms to enhance the educative way of people with intellectual disabilities.

Publisher

World Scientific Pub Co Pte Ltd

Subject

Artificial Intelligence,Artificial Intelligence

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A scoping review of BCIs for learning regulation in mainstream educational contexts;Behaviour & Information Technology;2023-08-01

2. Classification of Visually Evoked Potential EEG Using Hybrid Anchoring-based Particle Swarm Optimized Scaled Conjugate Gradient Multi-Layer Perceptron Classifier;International Journal on Artificial Intelligence Tools;2023-05

3. Extreme Learning Machine (ELM) Method for Classification of Preschool Children Brain Imaging;Journal of Autism and Developmental Disorders;2023-03-07

4. Editorial;International Journal on Artificial Intelligence Tools;2021-12

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