Mood based E-learning using EEG

Tambe, Namita. R.; Khachane, Ajitkumar

doi:10.1109/iccubea.2016.7860018

Cited by 9 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The brainwaves are direct reflection of the state of mind and cannot be tampered with [19]. In terms of EEG usage among the learning disabilities students, the technology is prominently being used to capture and measure the brainwave signals to detect their mood [10,19], emotions [19][20][21] and stress level [22,23]. EEG has been successfully used to recognize and monitor cognitive processes that occur within the brain which related with inner thinking activities.…”

Section: Literature Reviewsmentioning

confidence: 99%

Computational Neuro-physiological Model (CNP) to Profile Lower Primary School Students in Inclusive Education: A Concept Paper

Nor¹,

Zakaria²,

Azuddin³

et al. 2021

IJATCSE

View full text Add to dashboard Cite

Inclusiveeducation (IE) is commonly accepted internationally as a standard right for all children. Malaysia has similar aspiration to implement the concept in the school system. However, it appears IE is challenging for parents, teachers and students. One of the challenges is to produce a well-trained and competent teacher to recognize learning abilities and disabilities of individual students. The Malaysian government is committed to ensure children to receive equality in education. Malaysia Education Blueprint (MEB) spells out 75% of children with learning disabilities must be part of IE by 2025. There is a gap in IE studies in terms of investigating the relationship between emotions and learning ability among students in IE environment. Therefore, this concept paper proposes a standard profiling model by using the Computational Neuro-physiological (CNP) model to profile lower primary school students in IE. This paper also emphasizes the possible use of EEG technology in detecting the emotion, behavior and mental state that can be used to indicate the student’s learning ability in the inclusive education. This research is important to be carried out to have a student's profile which can be used to facilitate teachers to improve their competencies in teaching and handling the students

show abstract

Section: Literature Reviewsmentioning

confidence: 99%

Computational Neuro-physiological Model (CNP) to Profile Lower Primary School Students in Inclusive Education: A Concept Paper

Nor¹,

Zakaria²,

Azuddin³

et al. 2021

IJATCSE

View full text Add to dashboard Cite

show abstract

“…As the key technology of brain computer interface (BCI), EEG can be applied in five stages [7]. Among them, there are three important contents for signal processing, which are the preprocessing, feature extraction, and classification of the EEG signal; all above have been widely studied [8,9]. As early as the end of the 19th century, British physiologist Richard Caton took the lead in using a galvanometer to capture the weak current signal on the surface of the animal's cerebral cortex [10].…”

Section: Introductionmentioning

confidence: 99%

EEG Signal and Feature Interaction Modeling-Based Eye Behavior Prediction Research

Gao

2020

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

In recent years, with the development of brain science and biomedical engineering, as well as the rapid development of electroencephalogram (EEG) signal analysis methods, using EEG signals to monitor human health has become a very popular research field. The innovation of this paper is to analyze the EEG signal for the first time by building a depth factorization machine model, so that on the basis of analyzing the characteristics of user interaction, we can use EEG data to predict the binomial state of eyes (open eyes and closed eyes). The significance of the research is that we can diagnose the fatigue and the health of the human body by detecting the state of eyes for a long time. On the basis of this inference, the proposed method can make a further useful auxiliary support for improving the accuracy of the recommendation system recommendation results. In this paper, we first extract the features of EEG data by wavelet transform technology and then build a depth factorization machine model (FM+LSTM) which combines factorization machine (FM) and Long Short-Term Memory (LSTM) in parallel. Through the test of real data set, the proposed model gets more efficient prediction results than other classifier models. In addition, the model proposed in this paper is suitable not only for the determination of eye features but also for the acquisition of interactive features (user fatigue) in the recommendation system. The conclusion obtained in this paper will be an important factor in the determination of user preferences in the recommendation system, which will be used in the analysis of interactive features by the graph neural network in the future work.

show abstract