The Method of Integrating Virtual Reality with Brainwave Sensor for an Interactive Math's Game

Nugroho, Erdhi Widyarto; Harnadi, Bernardinus

doi:10.1109/jcsse.2019.8864205

Cited by 6 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recognizing the mental state of undergraduates in medical college, first, the mental state signals of undergraduates in medical college are collected. In this study, brainwave sensors are used to collect EEG signals of undergraduates in medical college (34). The task of EEG signal recognition is to process the collected original EEG analog signal through an analog-to-digital conversion and filtering algorithm, and then perform a simple threshold setting algorithm for its source data, so as to realize the recognition of EEG signal and the mental state of undergraduates in medical college.…”

Section: A Mental State Signal Acquisition Of Undergraduates In Medic...mentioning

confidence: 99%

AI-Based Publicity Strategies for Medical Colleges: A Case Study of Healthcare Analysis

Wang

2022

Front. Public Health

View full text Add to dashboard Cite

The health status and cognition of undergraduates, especially the scientific concept of healthcare, are particularly important for the overall development of society and themselves. The survey shows that there is a significant lack of knowledge about healthcare among undergraduates in medical college, even among medical undergraduates, not to mention non-medical undergraduates. Therefore, it is a good way to publicize healthcare lectures or electives for undergraduates in medical college, which can strengthen undergraduates' cognition of healthcare and strengthen the concept of healthcare. In addition, undergraduates' emotional and mental state in healthcare lectures or electives can be analyzed to determine whether undergraduates have hidden illnesses and how well they understand the healthcare content. In this study, at first, a mental state recognition method of undergraduates in medical college based on data mining technology is proposed. Then, the vision-based expression and posture are used for expanding the channels of emotion recognition, and a dual-channel emotion recognition model based on artificial intelligence (AI) during healthcare lectures or electives in a medical college is proposed. Finally, the simulation is driven by TensorFlow with respect to mental state recognition of undergraduates in medical college and emotion recognition. The simulation results show that the recognition accuracy of mental state recognition of undergraduates in a medical college is more than 92%, and the rejection rate and misrecognition rate are very low, and false match rate and false non-match rate of mental state recognition is significantly better than the other three benchmarks. The emotion recognition of the dual-channel emotion recognition method is over 96%, which effectively integrates the emotional information expressed by facial expressions and postures.

show abstract

Section: A Mental State Signal Acquisition Of Undergraduates In Medic...mentioning

confidence: 99%

AI-Based Publicity Strategies for Medical Colleges: A Case Study of Healthcare Analysis

Wang

2022

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…Neurosky mindwave mobile 2 (MWM2) headset was used to record single-channel EEG from F P 1 position before and during the performance of mental arithmetic task. The headset consists of three electrodes, one for EEG (F P 1 ) and other two electrodes for ground and reference (A 1 position) [36], as shown in the recording set up in Fig. 1.…”

Section: Recorded Database (R)mentioning

confidence: 99%

“…The inbuilt Thinkgear ASIC (application-specific integrated circuit) module (TGAM) pre-processes the raw signal, i.e., removal of powerline interference (PLI) and MAs. Communication is established between the device and computer using a bluetooth module [36]. The data was recorded at a sampling frequency of 512Hz with 12 bit resolution and analyzed in MATLAB software.…”

Section: Recorded Database (R)mentioning

confidence: 99%

Light-Weight 1-D Convolutional Neural Network Architecture for Mental Task Identification and Classification Based on Single-Channel EEG

Saini,

Satija,

Upadhayay

2020

Preprint

View full text Add to dashboard Cite

Mental task identification and classification using single/limited channel(s) electroencephalogram (EEG) signals in real-time play an important role in the design of portable braincomputer interface (BCI) and neurofeedback (NFB) systems. However, the real-time recorded EEG signals are often contaminated with noises such as ocular artifacts (OAs) and muscle artifacts (MAs), which deteriorate the hand-crafted features extracted from EEG signal, resulting inadequate identification and classification of mental tasks. Therefore, we investigate the use of recent deep learning techniques which do not require any manual feature extraction or artifact suppression step. In this paper, we propose a light-weight one-dimensional convolutional neural network (1D-CNN) architecture for mental task identification and classification. The robustness of the proposed architecture is evaluated using artifact-free and artifact-contaminated EEG signals taken from two publicly available databases (i.e, Keirn and Aunon (K) database and EEGMAT (E) database) and in-house (R) database recorded using single-channel neurosky mindwave mobile 2 (MWM2) EEG headset in performing not only mental/non-mental binary task classification but also different mental/mental multi-tasks classification. Evaluation results demonstrate that the proposed architecture achieves the highest subject-independent classification accuracy of 99.7% and 100% for multi-class classification and pair-wise mental tasks classification respectively in database K. Further, the proposed architecture achieves subject-independent classification accuracy of 99% and 98% in database E and the recorded database R respectively. Comparative performance analysis demonstrates that the proposed architecture outperforms existing approaches not only in terms of classification accuracy but also in robustness against artifacts.

show abstract

A LabVIEW Based Brain-Computer Interface Training Environment by Controlling Yoda Holograms Using Eye-Blinks According to an Interactive Game Framework

Rușanu

Roșca

2023

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

The Method of Integrating Virtual Reality with Brainwave Sensor for an Interactive Math's Game

Cited by 6 publications

References 15 publications

AI-Based Publicity Strategies for Medical Colleges: A Case Study of Healthcare Analysis

AI-Based Publicity Strategies for Medical Colleges: A Case Study of Healthcare Analysis

Light-Weight 1-D Convolutional Neural Network Architecture for Mental Task Identification and Classification Based on Single-Channel EEG

A LabVIEW Based Brain-Computer Interface Training Environment by Controlling Yoda Holograms Using Eye-Blinks According to an Interactive Game Framework

Contact Info

Product

Resources

About