Left And Right Brain Balancing Application With Eeg Biofeedback System


    Professor Dr. Tan Shing Chiang
  • Description of Invention

    The theories regarding brain dominance defining a person’s personality have been developed for decades, for example left brain thinkers are better in mathematics and right brain thinkers are better in arts. Currently, questionnaire assessment is used to determine the brain dominance level. However, there is no evidence in term of practical biofeedback data which reflects the information of our brain to validate the left and right brain dominance concept. Thus, this research is aimed to implement a brain dominance level classification system based on electroencephalogram (EEG) technology and deep neural network architecture to determine the brain dominance and the brain balancing level of the user. This system is aimed to serve the educational purpose by providing a practical scientific data-driven approach to determine brain dominance level. In this way, the academicians and educators are able to determine the initial brain dominance level of the student, design series of training to strengthen the weak hemisphere of the brain, and finally use this system to track the learning progress of the student. Hence, every student will be able to unleash their full potential of their brain instead of performing weakly in one-size-fit-all education. Besides, we also develop the brain balancing training applications using holographic technology. In this way, students can experience the latest holographic technology that provides an immersive and interactive environment. In this way, this project provides a solution for a sustainable education system for the youth and students.
    In this research, we employs Open BCI Mark IV EEG headset to acquire resting state EEG signal of the subject. Next, we implement Metric Learning Based Convolutional Neural Network (MLBCNN) deep learning method to classify the brain dominance based on EEG signal. The classification accuracy of the implemented deep learning method can achieve up to 97.44% accuracy. Besides, we also develop several mixed reality/augmented reality based brain training application using Microsoft Hologram. Our research shows that the MR/AR technology provides more immersive experience to the user and user pay higher attention while using MR/AR technology to perform brain training.
    We recognize that education, knowledge, information, and communication are at the core of human progress, endeavor and well-being. Further, Information and Communication Technologies (ICTs) have an immense impact on virtually all aspects of our lives. The rapid progress of these technologies opens completely new opportunities to attain higher levels of development. The capacity of these technologies to reduce many traditional obstacles, especially those of time and distance, for the first time in history makes it possible to use the potential of these technologies for the benefit of millions of people in all corners of the world. This project matches this principle as this project proposes a system that can track the student's learning progress in a scientific way based on their electroencephalogram (EEG) signal. This is a breakthrough technology implemented to serve education purpose so that every student has a chance to recognize their brain capability and undergoes the training which suitable for them. Hence, this is the first step in revolutionizing the traditional one-size-fit all education syllabus into tailor-made education for unleashing the brain potential of every student. This project also fulfill the Sustainable Development Goal (SDG 4) of Quality Education. This system is innovated to ensure quality education for everyone regardless of their talent. With this innovation, every student can recognize their brain capability and develop their brain based on their talents. Besides, this system also allows the teachers and students themselves to keep track their learning progress with a more scientific approach based on the electroencephalogram (EEG) signal.

  • Intellectual Property (IP) Status

    • Copyright Affirmed
    • Patent Filed
    • TRL Status: 5