Brain-Computer Interface (BCI) – IRIIC UIU

Bolte Chai

IRIIC — Thu, 30 Jan 2025 09:44:14 +0000

Bolte Chai

An Android Mobile Application to Support Verbally Challenged ASD Children

‘Bolte Chai’ is an Android mobile application developed for verbally challenged ASD children. Considering the customization capability, the android application is divisible into two different modes offering both Bangla and English language.

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Specifications

Cloud based framework and database
Intelligent integrated collaborative platform for children with disabilities their parents,teachers and speech language therapists
Intelligent analytic system to monitor children progress
Bangla and English language support
All the key features of bolte chai lite with minimal subscription fee
Simplistic user interface
Implementation or picture exchange communication system (PECS) based speech generating device (SGD) type AAC technology
Free of cost user experience

Interface

Publications

Khan, Md Nasfikur R., et al. "Bolte Chai: An augmentative and alternative communication device for enhancing communication for nonverbal children." 2016 International Conference on Medical Engineering, Health Informatics and Technology (MediTec). IEEE, 2016. 2016 International Conference on Medical Engineering, Health Informatics and Technology (MediTec) DOI: 10.1109/MEDITEC.2016.7835391 Date of Conference: 17-18 Dec. 2016 INSPEC Accession Number: 16622976 Publisher: IEEE Institute of Electrical and Electronics Engineers Conference Location: Dhaka, Bangladesh

Abstract

There are different kinds of technologies being used to improve the communication opportunity of children with verbal challenges. Portable communication devices provide the facility to the users for customizing applications according to their needs. Besides, the usability and costing range of these portable communication devices make them less condemning for the users. On the other hand, communication devices efficiently help the users with less motor skills. After considering these points, a communication device named “Bolte Chai”, has been developed and experimented in a special school’s students and teachers based on five criteria to evaluate usability and effectiveness. Those criteria are, the ability to customize the program to fit the needs of the users, motor skills for operating the applications, reasonable time and resources involved in the teaching and utilization procedure, update the system based on current research and proper costing, considering the user’s affordability. Based on the evaluation, it has been seen that this communication device is usable and effective to improve communication skills of nonverbal children.

Khan, Md NM. N. R. Khan, H. H. Sonet, F. Yasmin, S. Yesmin, F. Sarker and K. A. Mamun, "‘Bolte Chai’ — An Android application for verbally challenged children," 2017 4th International Conference on Advances in Electrical Engineering (ICAEE), Dhaka, Bangladesh, 2017, pp. 541-545, doi: 10.1109/ICAEE.2017.8255415. Published in: 2017 4th International Conference on Advances in Electrical Engineering (ICAEE) Date of Conference: 28-30 September 2017 Date Added to IEEE Xplore: 15 January 2018 DOI: 10.1109/ICAEE.2017.8255415 Publisher: IEEE Conference Location: Dhaka, Bangladeshasfikur R., et al. "Bolte Chai: An augmentative and alternative communication device for enhancing communication for nonverbal children." 2016 International Conference on Medical Engineering, Health Informatics and Technology (MediTec). IEEE, 2016. 2016 International Conference on Medical Engineering, Health Informatics and Technology (MediTec) DOI: 10.1109/MEDITEC.2016.7835391 Date of Conference: 17-18 Dec. 2016 INSPEC Accession Number: 16622976 Publisher: IEEE Institute of Electrical and Electronics Engineers Conference Location: Dhaka, Bangladesh

Abstract

Mobile technology has introduced new communication possibilities for patients with verbal challenges. In western countries different kinds of SGD and AAC devices are available to fulfill the communicative needs of users with verbal disabilities. Recently mobile phone applications are also used to fulfill the demand of communication. In developing countries like Bangladesh, mobile phones are much more available, affordable and less stigmatizing for the users of any kind. After considering these points, an android app named “Bolte Chai”, has been developed and an experiment has been performed on verbally challenged students and teachers based on four criteria to evaluate the usability and effectiveness of the application. These criteria are, the ability to customize the application to fit the needs of the users, motor skills for operating the applications, reasonable time and resources involved in the teaching and utilization procedure and proper costing, considering the user's affordability. Overall the application is supposed to be fully customized, more user friendly and effective to improve communication skills for verbally challenged users.

Farzana, Walia, et al. "Communication Support Utilizing AAC for Verbally Challenged Children in Developing Countries During COVID-19 Pandemic." International Conference on Human-Computer Interaction. Springer, Cham, 2020. First Online:08 November 2020 DOI: doi.org/10.1007/978-3-030-60703-6_6 Publisher Name: Springer, Cham Online ISBN: 978-3-030-60703-6 Print ISBN:978-3-030-60702-9

Abstract

Functional communication is indispensable for child development at all times but during this COVID-19, non-verbal children become more anxious about social distancing and self-quarantine due to sudden aberration on daily designed practices and professional support. These verbally challenged children require the support of Augmentative and Alternative Communication (AAC) for intercommunication. Therefore, during COVID-19, assistance must be provided remotely to these users by an AAC team involving caregivers, teachers, Speech Language Therapist (SLT) to ensure collaborative learning and development of non-verbal child communication skills. However, most of the advanced AAC, such as Speech Generating Devices (SGD), Picture Exchange Communication System (PECS) based mobile applications (Android & iOS) are designed considering the scenario of developed countries and less accessible in developing countries. Therefore, in this study, we are focusing on representing feasible short term strategies, prospective challenges and as long term strategy, a cloud based framework entitled as “Bolte Chai+”, which is an intelligent integrated collaborative learning platform for non-verbal children, parents, caregivers, teachers and SLT. The intelligent analytics within the platform monitors child overall progress by tracking child activity in mobile application and conversely support parents and AAC team to concentrate on individual child ubiquitous abilities. We believe the proposed framework and strategies will empower non-verbal children and assist researchers, policy makers to acknowledge a definitive solution to implement AAC as communication support in developing countries during COVID-19 pandemic.

Farzana, Walia, et al. "An Evaluation of Augmentative and Alternative Communication Research for ASD Children in Developing Countries: Benefits and Barriers." International Conference on Human-Computer Interaction. Springer, Cham, 2020. First Online: 08 November 2020 DOI: doi.org/10.1007/978-3-030-60703-6_7 Publisher Name: Springer, Cham Online ISBN 978-3-030-60703-6 Print ISBN 978-3-030-60702-9

Abstract

Augmentative and Alternative Communication (AAC) technology research not only enhances communication but also communal skills in verbally challenged children with Autism Spectrum Disorder (ASD). However, the research on AAC technology is mainly concentrated in developed countries and less explored in developing countries. In this study, we utilized evaluation method to analyze the prospects, existing practices and future possibilities, benefits and barriers of AAC research in developing countries. It is found that Speech Generating Devices(SGD)are mostly preferred by children and in future artificial intelligence(AI)based mobile application will augment communication skills among verbally challenged children.We conclude with general recommendation on succeeding research, collaborative approach and implementation with funding opportunities for substantial growth of AAC technology research in developing countries. This study will facilitate directions for initiating AAC research in developing countries and will accommodate researchers, developers and stakeholders to acknowledge opportunities, barriers, probable and current state of AAC research.

Farzana, Walia, et al. "Technological Evolvement in AAC Modalities to Foster Communications of Verbally Challenged ASD Children: A Systematic Review." IEEE Access (2021). Published in: IEEE Access ( Early Access ) Date of Publication: 28 January 2021 Electronic ISSN: 2169-3536 DOI: 10.1109/ACCESS.2021.3055195 Publisher: IEEE

Abstract

Augmentative and Alternative Communication (AAC) emerged as a combination of methods or strategies that constitute any device, such as Speech Generating Device (SGD), Program (mobile applications), Procedure (PECS, Picture Exchange Communication System), which enhances an individual's communication ability. Autism Spectrum Disorder (ASD) is a spectrum of comprehensive neurodevelopmental disorder that leads to speech impairments, repetitive behavior, and social communication difficulties; therefore, it is imperative to underscore that at the core of all impediments are communication impairment. This article represents a systematic review of research initiatives that investigate multi-modal AAC strategies and functionality, features of mobile applications to reinforce communication and communal skills in verbally challenged ASD children because other researches are focused only on low or high-tech AAC or interventions to provide insights on ASD children respond to a particular approach. Following the PRISMA method, a total of 60 (January 2015 to October 2020) research articles were reviewed, indexed by Springer, Science Direct, Scopus, ACM, IEEE databases, and published in the AAC journal. The selected research articles are categorized into different themes where most of them focused on interactive mobile applications to improve emotional, social, learning, and overall communication skills in verbally challenged ASD children. This systematic review provides an outline of the paradigm shift in AAC modalities from PECS to Artificial Intelligence (AI), Machine Learning (ML), and Augmented Reality (AR) based applications. It opens up underline future opportunities to integrate intelligent analytics features in mobile applications to strengthen communication skills in verbally undermined ASD children.

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Concentrate Today

IRIIC — Thu, 30 Jan 2025 07:29:40 +0000

Concentrate Today

A Real-Time Brain-Computer Interface (BCI) Based Neurofeedback System for Enhancing Sustained Attention

ConcentrateToday is a neurofeedback system where you will be able to know the instant results of your progress. Neurofeedback is also called EEG biofeedback or Neurotherapy. It makes use of the brain’s ability to change and reshape brain networks. Neurofeedback training can be used to sharpen attention, relieve stress, enhance your mood, and improve your learning and behavioural control. Although the technology is complex, the process is simple. You will learn to alter your brain activity the same way you learn every other skill. You learn through feedback and practice. With neurofeedback, you get instant feedback about changes in your brain’s activity. In our system, every 5-second your brain activity is compared to your target or goal. You can then learn and practice how to influence your brain activity to move closer to your goal. During participating in this experiment you will not only learn tips and techniques that will help you increase concentration level but also you will be able to make some effective habits that will continuously help you increase concentration in your daily life, even after completing this experiment.

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Specifications

Identifying sustained attention from brain signal and improving it by a neurofeedback-based training.
Students; Researchers; Scientists; People who want or need to improve their sustained attention
Improved sustained attention, quality of life and work after practicing training session regularly (at least for 30 days).

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Publications

R. M. S. Parbez and K. A. Mamun, "BlinkFruity: A Real-Time EEG Based Neurofeedback Game for Brain-Computer Interface," 2020 2nd International Conference on Advanced Information and Communication Technology (ICAICT), Dhaka, Bangladesh, 2020, pp. 404-409, doi: 10.1109/ICAICT51780.2020.9333469 Published in: 2020 2nd International Conference on Advanced Information and Communication Technology (ICAICT) Date of Conference: 28-29 November 2020 Date Added to IEEE Xplore: 01 February 2021 DOI: 10.1109/ICAICT51780.2020.9333469 Publisher: IEEE Conference Location: Dhaka, Bangladesh

Abstract:

The Brain-Computer Interface (BCI) is a communication channel between the brain and the computer. It works by detecting the production of currents due to brain cell activity. Among many non-invasive brain signal recording methods, Electroencephalography (EEG) is one of the most popular methods. EEG controlled applications widely range from strictly medical to non-medical applications. Non-medical applications can not only be used for entertainment purposes, but also can help a subject to experience BCI application, achieve better control with rehabilitation systems and can be a strong motivation to practice BCI system. Brain signals are weak and contain lots of artifacts like muscle movements, cardiac, eye blink, power source, and amplitude artifacts. Although eye blink is considered as one of the strongest artifacts, this study uses eye blink as a tool to control signals to play BlinkFruity game, where users collect fruits into a basket using eye blink only. To attain this objective, at first, the brain activity was recorded using OpenBCI device and 500ms window data was taken to process in real-time. Then the notch filter was applied to remove powerline noise. The eye blink was detected by using signal thresholding method reading from EEG data. Blink detection

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Deep Depression

IRIIC — Thu, 30 Jan 2025 06:14:56 +0000

Deep Depression

BCI Based System for Identifying Depression through Neural Connectivity Analysis

Depression is a prevalent mental illness impacting millions globally each year, projected to become a major cause of devastating disease by 2022. According to the World Mental Health Survey, around 3000 suicides and 60,000 attempted suicides occur daily, with these figures worsening during the COVID-19 pandemic. The current diagnostic process, relying on clinical questionnaires and behavioral observation, is time-consuming. To address this, a multidisciplinary team is developing a Brain-Computer Interface (BCI) technology to analyze brain signals and identify depression biomarkers. This innovative system offers a more efficient, painless, and patient-friendly diagnostic tool, aiming to reduce depression-related fatalities and improve overall well-being.

Specifications

Clinical Intervention
Whether a person has depression or not.
Can get to know the severity of his depression.
Can get to know about his depression without interrogating them.
Can save more lives from the hell of suicide.

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Publications

A. Dev et al., "Exploration of EEG-Based Depression Biomarkers Identification Techniques and Their Applications: A Systematic Review," in IEEE Access, vol. 10, pp. 16756-16781, 2022, doi: 10.1109/ACCESS.2022.3146711 Published in: IEEE Access ( Volume: 10) Page(s): 16756 - 16781 Date of Publication: 26 January 2022 Electronic ISSN: 2169-3536 DOI: 10.1109/ACCESS.2022.3146711 Publisher: IEEE

Abstract

Depression is the most common mental illness, which has become the major cause of fear and suicidal mortality or tendencies. Currently, about 10% of the world population has been suffering from depression. The classical approach for detecting depression relies on the clinical questionnaire, which depends on the patients’ responses as well as observing their behavioral activities. However, there is no established method to detect depression from EEG biomarkers. Therefore, exploration of EEG biomarkers for depression assessments is vital and has a great potential to improve our understanding and clinical interventions. In this study, we have conducted a systematic review of 52 research articles using the PRISMA-P systematic review protocol, where we analyzed their research methodologies and outcomes. We categorized the experimentations in these articles according to their physical and psychological aspects scaled by the commonly used clinical questionnaire-based assessments. This study finds that the negative stimuli are the better identification strategies for evaluating depression through EEG signals. From this exploration, researchers observed that the Neural Connectivity Analysis and Brain Topological Mapping have huge potentials for finding depression biomarkers, and it is evident that the right-side hemisphere and frontal and parietal-occipital cortex are distinct regions to detect depression using EEG signals. For this mechanism, researchers are using many signal processing and machine learning approaches. In the case of filtering, Independent Component Analysis (ICA) is commonly used to eliminate physiological and non-physiological artifacts. Among machine learning approaches, Convolutional Neural Network (CNN) and Support Vector Machine (SVM) showed better performance for classifying healthy and depressed brains. The authors hope this study will create an opportunity to explore more in the future for EEG as a diagnostic tool by analyzing brain functional connectivity for focusing on clinical interventions.

Islam, M. S., Mamun, K. A., & Deng, H. (2017). Decoding of Human Movements Based on Deep Brain Local Field Potentials Using Ensemble Neural Networks. Computational Intelligence and Neuroscience, 2017, 5151895. https://doi.org/10.1155/2017/5151895

Abstract

Decoding neural activities related to voluntary and involuntary movements is fundamental to understanding human brain motor circuits and neuromotor disorders and can lead to the development of neuromotor prosthetic devices for neurorehabilitation. This study explores using recorded deep brain local field potentials (LFPs) for robust movement decoding of Parkinson’s disease (PD) and Dystonia patients. The LFP data from voluntary movement activities such as left and right hand index finger clicking were recorded from patients who underwent surgeries for implantation of deep brain stimulation electrodes. Movement-related LFP signal features were extracted by computing instantaneous power related to motor response in different neural frequency bands. An innovative neural network ensemble classifier has been proposed and developed for accurate prediction of finger movement and its forthcoming laterality. The ensemble classifier contains three base neural network classifiers, namely, feedforward, radial basis, and probabilistic neural networks. The majority voting rule is used to fuse the decisions of the three base classifiers to generate the final decision of the ensemble classifier. The overall decoding performance reaches a level of agreement (kappa value) at about for decoding movement from the resting state and about for decoding left and right visually cued movements

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MindWave Analytics

IRIIC — Thu, 30 Jan 2025 03:44:51 +0000

Mindwave Analytics

An intelligent neuromarketing solution for Bangladesh

Delivering adequate healthcare in rural and remote areas of developing countries is a major challenge. CMED (Cloud-based MEDical system) addresses this by integrating cloud services with healthcare centers and portable services managed by Community Health Workers (CHWs). CMED categorizes patients into healthy, alarming, and emergency groups, ensuring timely and appropriate care. For complex cases, the system connects CHWs with specialist doctors. Using Identity-Based Encryption, CMED ensures the privacy and confidentiality of patient health information, providing a smart and secure solution to enhance healthcare accessibility for underprivileged populations.

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Specifications

Utilizes Brain-Computer Interface (BCI) technology to record brain signals.
Analyzes and predicts consumer responses to marketing stimuli.
Adapts advanced Industry 4.0 technologies for accurate consumer insights.
Builds the first local Neuromarketing platform in Bangladesh.
Provides cost-efficient and customized solutions for local businesses.
Positions Bangladesh on the global stage with innovative marketing research tools.

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Publications

Mashrur, Fazla Rabbi & Mahmudur Rahman, Khandoker & Miya, Mohammad & Vaidyanathan, Ravi & Anwar, Syed & Sarker, Farhana & Mamun, Khondaker. (2023). An EEG-based Intelligent Neuromarketing System for Predicting Consumers’ Choice. 10.1007/978-981-19-8032-9_3.

Abstract:

Marketers use different marketing strategies to elicit the desired response from the target customers. To measure customer engagement, they usually conduct one-on-one interviews, surveys, broad polls, and focus group discussions. However, these are costly and sometimes unreliable. On the other hand, neuromarketing measures customer response to marketing stimuli by measuring the electrical activity of the brain and has the potential to address these drawbacks. which can be overcome by neuromarketing. In this work, we proposed a prediction algorithm that can identify consumer affective attitude (AA) and purchase intention (PI) from EEG signals from the brain. At first, the raw EEG signals are initially preprocessed to remove noise. After that, three features are extracted: time, frequency, and time frequency domain features. Wavelet packet transform is used to separate the EEG bands in time-domain feature extraction. Then, for feature selection, wrapper-based SVM-RFE is utilized. Finally, we use SVM to distinguish the classes in AA and PI. Results show that for SVM with radial basis function the kernel performs better to classify positive and negative AA with an accuracy of $90\pm 4.33$ and PI with an accuracy of $75\pm 2.5$. So, EEG-based neuromarketing solutions can assist companies and organizations in accurately predicting future consumer preferences. As a result, neuromarketing based-solutions have the potential to increase sales by overcoming the constraints of traditional marketing Keywords Consumer neuroscience Neuromarketing Electroencephalography Consumer behavior Pattern recognition

F. Ishtiaque et al., "BCI-based Consumers’ Preference Prediction using Single Channel Commercial EEG Device," 2022 25th International Conference on Computer and Information Technology (ICCIT), Cox's Bazar, Bangladesh, 2022, pp. 43-48, doi: 10.1109/ICCIT57492.2022.10054806. Published in: 2022 25th International Conference on Computer and Information Technology (ICCIT) Date of Conference: 17-19 December 2022 Date Added to IEEE Xplore: 03 March 2023 DOI: 10.1109/ICCIT57492.2022.10054806 Publisher: IEEE Conference Location: Cox's Bazar, Bangladesh

Abstract:

Brain-Computer Interface (BCI) technology is used in neuromarketing to learn how consumers respond to marketing stimuli. This helps evaluate the marketing stimuli which is traditionally done using marketing research procedures. BCI-based neuromarketing promises to replace these traditional marketing research procedures which are time-consuming and costly. Although BCI-based neuromarketing has its difficulty as EEG devices are inconvenient for consumer-grade applications. This study is performed to predict consumers’ affective attitude (AA) and purchase intention (PI) toward a product using EEG signals. EEG signals are collected using a single channel consumer-grade EEG device from 4 healthy participants while they are subject to 3 different types of marketing stimuli; product, promotion, and endorsement. Multi-domain features are extracted from the EEG signals after pre-processing. 52 features are selected among those using SVM-based Recursive Feature Elimination. SMOTE algorithm is used to balance out the dataset. Support Vector Machine (SVM) is used to classify positive and negative affective attitude and purchase intention. The model manages to achieve an accuracy of 88.2% for affective attitude and 80.4% for purchase intention proving the viability of consumer-grade BCI devices in neuromarketing.

Mashrur, F. R., Rahman, K. M., Miya, M. T. I., Vaidyanathan, R., Anwar, S. F., Sarker, F., & Mamun, K. A. (2022). An Intelligent Neuromarketing System for Predicting Consumers’ Future Choice from Electroencephalography Signals. Physiology & Behavior, 113847. IF: 3.2 doi: https://doi.org/10.3389/fnhum.2022.861270

Abstract:

Neuromarketing utilizes Brain-Computer Interface (BCI) technologies to provide insight into consumers' responses on marketing stimuli. In order to achieve insight information, marketers spend about $400 billion annually on marketing, promotion, and advertisement using traditional marketing research tools. In addition, these tools like personal depth interviews, surveys, focus group discussions, etc. are expensive and frequently criticized for failing to extract actual consumer preferences. Neuromarketing, on the other hand, promises to overcome such constraints. In this work, an EEG-based neuromarketing framework is employed for predicting consumer future choice (affective attitude) while they view E-commerce products. After preprocessing, three types of features, namely, time, frequency, and time-frequency domain features are extracted. Then, wrapper-based Support Vector Machine-Recursive Feature Elimination (SVM-RFE) along with correlation bias reduction is used for feature selection. Lastly, we use SVM for categorizing positive affective attitude and negative affective attitude. Experiments show that the frontal cortex achieves the best accuracy of 98.67±2.98, 98±3.22, and 98.67±3.52 for 5-fold, 10-fold, and leave-one-subject-out (LOSO) respectively. In addition, among all the channels, Fz achieves best accuracy 90±7.81, 90.67±9.53, and 92.67±7.03 for 5-fold, 10-fold, and LOSO respectively. Subsequently, this work opens the door for implementing such a neuromarketing framework using consumer-grade devices in a real-life setting for marketers. As a result, it is evident that EEG-based neuromarketing technologies can assist brands and enterprises in forecasting future consumer preferences accurately. Hence, it will pave the way for the creation of an intelligent marketing assistive system for neuromarketing applications in future.

F. R. Mashrur, M. T. I. Miya, F. S. Rawnaque, K. M. Rahman, R. Vaidyanathan, S. F. Anwar, F. Sarker, and K. A. Mamun “MarketBrain: An EEG based intelligent consumer preference prediction system,” in 2021 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) doi: https://doi.org/10.1109/EMBC46164.2021.9629841 Published in: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Date of Conference: 01-05 November 2021 Date Added to IEEE Xplore: 09 December 2021 DOI: 10.1109/EMBC46164.2021.9629841 Publisher: IEEE Conference Location: Mexico

Abstract:

The traditional marketing research tools (Personal Depth Interview, Surveys, FGD, etc.) are cost-prohibitive and often criticized for not extracting true consumer preferences. Neuromarketing tools promise to overcome such limitations. In this study, we proposed a framework, MarketBrain, to predict consumer preferences. In our experiment, we administered marketing stimuli (five products with endorsements), collected EEG signals by EMOTIV EPOC+, and used signal processing and classification algorithms to develop the prediction system. Wavelet Packet Transform was used to extract frequency bands (δ, θ, α, β 1 , β 2 , γ) and then statistical features were extracted for classification. Among the classifiers, Support Vector Machine (SVM) achieved the best accuracy (96.01±0.71) using 5-fold cross-validation. Results also suggested that specific target consumers and endorser appearance affect the prediction of the preference. So, it is evident that EEG-based neuromarketing tools can help brands and businesses effectively predict future consumer preferences. Hence, it will lead to the development of an intelligent market driving system for neuromarketing applications.

F.S. Rawnaque, K.M. Rahman, S.F. Anwar, et al. Technological advancements and opportunities in Neuromarketing: a systematic review. Brain Inf. 7, 10 (2020). (IF: 6.245 as per Scimago, Scopus) doi: https://doi.org/10.1186/s40708-020-00109-x Brain Informatics volume 7, Article number: 10 (2020)

Abstract:

Neuromarketing has become an academic and commercial area of interest, as the advancements in neural recording techniques and interpreting algorithms have made it an effective tool for recognizing the unspoken response of consumers to the marketing stimuli. This article presents the very first systematic review of the technological advancements in the Neuromarketing field over the last 5 years. For this purpose, authors have selected and reviewed a total of 57 relevant literatures from valid databases which directly contribute to the Neuromarketing field with basic or empirical research findings. This review finds consumer goods as the prevalent marketing stimuli used in both product and promotion forms in these selected literatures. A trend of analyzing frontal and prefrontal alpha band signals is observed among the consumer emotion recognition-based experiments, which corresponds to frontal alpha asymmetry theory. The use of electroencephalogram (EEG) is found favorable by many researchers over functional magnetic resonance imaging (fMRI) in video advertisement-based Neuromarketing experiments, apparently due to its low cost and high time resolution advantages. Physiological response measuring techniques such as eye tracking, skin conductance recording, heart rate monitoring, and facial mapping have also been found in these empirical studies exclusively or in parallel with brain recordings. Alongside traditional filtering methods, independent component analysis (ICA) was found most commonly in artifact removal from neural signals. In consumer response prediction and classification, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) have performed with the highest average accuracy among other machine learning algorithms used in these literatures. The authors hope this review will assist future researchers with vital information in the field of Neuromarketing for making novel contributions.

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Focus Mind

IRIIC — Fri, 24 Jan 2025 23:08:52 +0000

Focus Mind

A specialized Neurofeedback based education system for the students in Bangladesh

The purpose of this project is to build an efficient educational system for students by detecting the stress, working memory, attention, brain-to-brain synchrony, and engagement metrics of students in Bangladesh. This will help to improve the performance of students and efficiency in learning using neurofeedback with an integrated brain-computer interface (BCI). This project will support students to improve their cognitive abilities and personalize the learning system which will lead to an improvised system of learning and enhance the quality of learning.

Specifications

Improved efficient meeting and classroom discussions
Detection of mental issues of users, like depression, stress & sleep
Improved attention and productivity of users
Real-time tracking of productivity of groups
Provide tips for productivity improvement

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Diagrams

Publications

Tahnia Nazneen, Iffath Binta Islam, Wasifa Jamal, Ravi Vaidyanathan, Tom Chau, Khondaker A. Mamun, " Recent Trends in NON-INVASIVE Neural Recording Based Brain-to-Brain Synchrony Analysis on Multidisciplinary Human Interactions for Understanding Brain Dynamics: A Systematic Review" submitted in Frontiers in Computational Neuroscience. (UNDER REVIEW) Impact F

Iffath Binta Islam, Iftekhar Fahim Aziz, Wasifa Jamal, Khondaker A. Mamun, "An Investigation on university students’ attention level with FocusCalm EEG device through hyperscanning while watching video on AI" 1st International Conference on Information and Communication Technology for Development (ICICTD) 2022. Institute of Information and Communication Technology (IICT), Khulna University of Engineering & Technology (KUET), Khulna-9203, Bangladesh. (SUBMITTED)

Use of different synchrony measures to determine the engagement level of the students.

Providing teachers and students with contextually relevant feedback on a dashboard to gain control over the students’ learning methods.

Development of a neuro-feedback based system to train the students to focus and relax periodically.

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