Abstract
Mental fatigue hampers the productivity and overall cognitive performance, increases the propensities for errors and slows the reaction time. However, the effects of mental fatigue can be alleviated through intervention by the presence of selected stimuli. In contrast, the response to the intensity of stimuli and perception of duration varies from one person to another during mental fatigue intervention. Therefore, the aim of this study is to produce a framework for personalized mental fatigue intervention through BCI. As the study output, a framework for personalized intervention using BCI is potentially implemented to address mental fatigue problems that have become common at the present time due to prolonged and demanding cognitive efforts in performing daily routines, such as attending virtual/online classes, driving and performing surgery. This is to support the Malaysia Mental Health Policy which emphasizes on treatment and rehabilitation, and aims to improve mental health services for populations at risk of developing psychosocial problems. On top of that, it also addresses the Goal 3 of United Nation Sustainable Development Goals in ensuring healthy mental health and well-being, by providing more personalized mental fatigue intervention based on real-time brain activation analysis through BCI.
Researcher
Farhad Hossain (Postgraduate Student)
Hamwira Yaacob (Supervisor)
Grant
Period: 2021 – Present
Funder: Ministry of Higher Education
Amount: RM58000
Abstract
This study aims to analyze effects of different video games desig styles (abstract vs realistic) on brain activation recorded as EEG using neuroaffective computational model.
Researcher
Ayub abdul Rahman (Postgraduate Student)
Hamwira Yaacob (Supervisor)
Mohd Syarqawy Hamzah (Co-Supervisor)
Abdul Wahab Abdul Rahman (Chairman)
Publication
bin Abdul Rahman, A. , bin Hamzah, M.S., Yaacob, H., & bin Abdul Rahman, A.W. (2021, February). Traces Of The Brain’s Learning Potential Presnet Within “Uneducational” Video Games. In IOP Conference Series: Materials Science and Engineering (Vol. 1077, No. 1, p. 012037). IOP Publishing.
Abstract
Social media is loaded with expressions of feelings in a variety of languages, including Malay. This information is very useful for the purpose of understanding people’s reaction and sentiments towards certain issues. An emotional corpus is very useful for this purpose. However, emotional meanings of words in existing Mlay online corpura are not clear and inconsistent. Therefore, EMMOC is designed to analyze the emotional connotations of words in different online Malay corpus using supervised machine learning technique.
Researcher
Hafizuddin Adnan (PhD Student)
Hamwira Yaacob (Supervisor)
Normi Sham Awang Abu Bakar (Co-Supervisor)
Abstract
A real-time computer based system called Brain-Computer Interface (BCI) has contributed to diverse areas. Mental fatigue among students however was not reported in existing BCI applications. The implementation of BCI to mental readiness for online learning should be considered. The students might be unaware of mental fatigue that has been a disturbance since many classes have been conducted virtually. Therefore, the aim of this project is to develop a BCI application which can detect mental readiness using Neurosky Mindwave Mobile 2 headset, Phyton programming language, Flask framework, and Bootstrap. The application is caled Meneady referring to mental and ready. By analyzing brain signals successfullt, mental fatigue among students could be identified and students will be notified about the current state of their brain. In addition, our system, will provide students a few musiic that can be listened to enhace their concentration level, so they can resume the online classes while they are mentally prepared.
Team Members
Atiqah Hazirah Akmaluddin (FYP Student)
Sabaria Lahak (FYP Student)
Hamwira Yaacob (Supervisor)
Dini Handayani (Co-Supervisor)
Awards
Bronze
KICT Final Year Project Showcase
21 & 22 January 2022
Abstract
Brain signals have been analyzed to understand the affective state of different cognitive and mental conditions. For eexample, the analysis, we can visualize the changes of emotion while driving, identify if a kid autistic, understand the conditions that stimulate sttention while studying, and many more. This can be done through a machine learning technique, which includes data acquisition, preprocessing, feature extraction, and training. However, these steps are done separately which makes it difficult to perform. Futhermore, no existing brain signal analysisi tools that perform analysis based on emotion. Therefore, this project aims to develop a brain analysis tool, namely Unified Neuro-Affective Classification Tool (UNACT). It consists of 3 main functions including: training, classfifying and analysis. It uses the Butterworth Bandpass method feature extraction and MPL as the classifier. This tool can be used by a non- technical person to perform analysis.
Team Members
Farhad Hossain (FYP Student)
Hamwira Yaacob (Supervisor)
Award
Silver Medalist Winner
Virtual Student’s Design Competition at National Symposium of Human Computer Interaction 2020
Aspiring Innovator Award
In the 9th International Innovation, Invention and Design
INDES 2020
Publication
Hossain, M. F., Yaacob, H., & Nordin, A. (2021, February). Development of Unified Neuro-Affective Classification Tool (UNACT). In IOP Conference Series: Materials Science and Engineering (Vol. 1077, No. 1, p. 012031). IOP Publishing.
Abstract
MOVIC is an integrated online Malay corpus. It integrates several Malay corpora including SEALang library, Malay Concordance Project (MCP) corpus, MyBaca corpus and Dewan Bahasa dan Pustaka (DBP) corpus in one platform.
Team Members
Normi Sham Awang Abu Bakar (International Islamic University Malaysia)
Hamwira Yaacob (International Islamic University Malaysia)
Dini Handayani Oktarina (Taylor’s University)
Mustafa Ali Abuzuraida (Universiti Utara Malaysia)
Publication
Bakar, N. S. A. A., Yaacob, H., Handayani, D., & Abuzaraida, M. A. (2018, July). Malay Online Virtual Integrated Corpus (MOVIC): A Systematic Review. In 2018 International Conference on Information and Communication Technology for the Muslim World (ICT4M) (pp. 243-248). IEEE.
Handayani, D., Bakar, N. S. A. A., Yaacob, H., & Abuzaraida, M. A. (2018, July). Sentiment analysis for Malay language: systematic literature review. In 2018 International Conference on Information and Communication Technology for the Muslim World (ICT4M) (pp. 305-310). IEEE.
Grant
Development of Malay Online Virtual Integrated Corpus (MOVIC) for Sentiment Analysis using Web-scraping.
Period: 2017 – 2020
Funding Agency: IIUM
Amount: RM20000
Abstract
CCMA is a feature extraction technique for profiling different emotional states based on brain electrical signals, EEG. It is designed based on a supervised machine learning techniques which involve the acquisition of EEG signals.
Team Members
Hamwira Yaacob (International Islamic University Malaysia)
Abdul Wahab Abdul Rahman (INternational Islamic University Malaysia)
Mariam Adawiah Dzulkifli (International Islamic University Malaysia)
Haslinda Kamaruddin (Universiti Teknologi MARA)
Publications
Yaacob, H., Abdul, W., & Kamaruddin, N. (2015). CMAC-Based Computational Model of Affects (CCMA) from Self-Organizing Feature Mapping Weights for Classification of Emotion Using EEG Signals. International Journal for Computers & Their Applications, 22(1).
Yaacob, H. S. (2015). A novel emotion profiling based on CMAC-Based computational models of affects.
Yaacob, H., Abdul, W., Al Shaikhli, 1. F., & Kamaruddin, N. (2014 November). CMAC based Computational Model of Affects (CCMA) for profiling emotion from EEG signals. In The 5th International Conference on Information and Communication Technology for The Muslim World (ICT4M) (pp. 1-6). IEEE.
Yaacob, H. Abdul, W., & Kamaruddin, N. (2013, December). Extracting features using computational cerebellar model for emotion classification. In 2013 International Conference on Advanced Computer Science Applications and Technologies (pp. 367-372). IEEE.
Yaacob, H., Kamaruddin, N., & Abdul, W. (2013. November). Brain topographic mapping of emotions using computational cerebellum. In 2013 International Conference on Electronics, Computer and Computation (ICECCO) (pp. 28-31). IEEE.
Yaacob, H. Abdul, W., & Kamaruddin N. (2013, March). Classification of EEG signals using MLP based on categorical and dimensional perceptions of emotions. In 2013 5th International Conference on Information and Communication Technology for the Muslim World (ICT4M) (pp. 1-6). IEEE
Yaacob, H., Karim, L, Wahab, A, & Kamaruddin, N. (2012, August). Two dimensional affective state distribution of the brain under emotion stimuli. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 6052-6055) IEEE.
Grant
Development of Malay Online Virtual Integrated Corpus (MOVIC) for Sentiment Analysis using Web-scraping.
Period: 2017 – 2020
Funding Agency: IIUM
Amount: RM20000