This is an update from the OpenBCI Discovery Program. Click here for details on how to apply.
What is the project about?
Being able to concentrate on study and work plays a very vital role in learning, performance and achievement in the level of perfection. However, the concentration level can be increased by practising. By measuring brain signals with non-invasive Electroencephalography (EEG) data we can design a neurofeedback-based Brain-Computer Interface (BCI) system, which can help users to know about the state of their concentration and show them the progress of their training.
In this project, we are developing a BCI enabled interactive system to enhance the concentration level of users. Here users will play a real-time neurofeedback-based game for several sessions where concentration would be the control parameter. Using neuro-feedback, the user will be able to know about their progress. Moreover, the system would suggest some methods and guideline for helping users to enhance the concentration level process faster.
Nowadays, attention-related EEG feature is used as the control parameter for most of the current neurofeedback based games since it is a key determinant for human cognition. There are several ways to identify an individual’s concentrated state. Among them, EEG measurement of engagement ratio of band power features in theta (4-8 Hz), alpha (8-13 Hz) and beta (13-30 Hz), and entropy-based features are widely used method. However, besides these, there are more parameters available that should be considered while evaluating concentration score. For example, when people are concentrating, they will blink less, stay alert and engaged, even they might feel happy. In our proposed algorithm we are going to consider these parameters to detect concentration in a more robust and sophisticated way.
How are OpenBCI tools being applied?
We are using OpenBCI tools during the system design, analysis and experimental sessions. Right now, we are in the phase of exploring and developing our proposed concentration detection algorithm. To design a low cost but robust system, we are using Cyton Biosensing Board (8-channels) with Ultracortex Mark IV EEG Headset. The electrodes are being placed at Fp1, Fp2, F3, F4, Fz, Pz, O1 and O2 position following the international 10-20 electrode placement systems.
What is the impact of this work?
The primary targeted users of this application are students who want to or need to work on improving their concentration level. This system can become helpful in evaluating and supporting students in educational institutes. Besides, it could be integrated with any other available neurofeedback-based systems available for students. However, the objective of this project is not using as an alternative to any clinical process for patients like ADHD, but it can support and enhance the treatment process.
We firmly believe that this project will have a positive impact on the field of neurotechnology. In addition to this, our system will help people who are having difficulties concentrating and intend to increase their concentration level.
Who is involved in this project?
Rana Depto is working as a Research Assistant in the area of Brain-Computer Interface (BCI) at Advanced Intelligent Multidisciplinary Systems Lab (AIMS Lab). He obtained his B.S degree in Computer Science and Engineering from United International University (UIU), Bangladesh in 2019 and now pursuing his M.S degree. Currently, he is working on developing real-time neurofeedback based BCI enabled system for enhancing concentration level and proposing a more robust and sophisticated concentration detection algorithm.
Prof. Khondaker A. Mamun received his PhD in Computer and Biomedical Engineering from the University of Southampton, UK in 2012. After that, he worked as a Postdoctoral Research Fellow in the PRISM Lab with a joint appointment from Institute of Biomaterials and Biomedical Engineering (IBBME), University of Toronto, Toronto, Canada and Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Canada. Since October 2014, he is working as an Associate Professor and then Professor at the Department of CSE, United International University (UIU), Bangladesh. He is the founder and Director of Advanced Intelligent Multidisciplinary Systems Lab (AIMS Lab) at UIU, where he actively performs research in areas of machine learning, intelligent System, healthcare, disabilities and education as well as human-machine interface (HMI), brain-computer interface (BCI), rehabilitation engineering, and mobile technology for health care and rehabilitation applications.
What is the timeline for the next few months?
Our application is almost ready. We are studying different concentration detection algorithm and working on the development of our proposed concentration detection algorithm. We expect to complete the part of our proposed algorithm by mid-April 2021. The next step will be to apply our algorithm in real-time on several participants to compare the result with other established concentration detection algorithms and validate the proposed algorithm. We will improve our system based on the result. Once it will be validated, we will deploy the application and publish the result.
Our work would be open source and shared in the OpenBCI Community when it will be completed. Until then, please stay tuned here. We would like to convey our thanks to everyone at OpenBCI for admitting us into the OpenBCI Sponsorship Program!