Brainwave Classification of Task Performed by Stroke Patients using ANN

Narudin, S.K.; Nasir, Nazlizan; Fuad, N.

doi:10.33166/aetic.2021.05.004

Cited by 3 publications

(2 citation statements)

References 13 publications

(14 reference statements)

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“…In conclusion, this study has shed light on the current landscape of learning style detection research while providing valuable insights into promising areas for future exploration. By continuing to address these research gaps and advancing methodological approaches, researchers can contribute to the ongoing growth and significance of learning style detection research, ultimately fostering more effective and personalized learning experiences for diverse learners (Narudin, Nasir and Fuad, 2021).…”

Section: Discussionmentioning

confidence: 99%

Mapping Research Themes and Future Directions in Learning Style Detection Research: A Bibliometric and Content Analysis

Wijaya,

Setiawan,

Shapiai

2023

EJEL

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This study aims to provide a comprehensive overview of the current state and potential future research in learning style detection. With the increasing number and diversity of research in this area, a quantitative approach is necessary to map out current themes and identify potential areas for future research. To achieve this goal, a bibliometric and content analysis will be conducted to map out the existing research and identify emerging topics and directions for future research. The study analyzes 1074 bibliographic sources from Scopus and visualizes the results of the bibliometric analysis through co-occurrence and thematic map analysis using VOSviewer and BibliometriX software. Content analysis is then conducted based on the results of the co-occurrence analysis. The findings reveal a significant increase in publications and citations in the field, with popular research topics including classification, adaptive learning, and MOOCs, and the most frequently used learning style models being Felder-Silverman, VARK, and Kolb. Emerging research topics include the use of EEG signals, online learning, and feature extraction. Future research may focus on classification, intelligent tutoring systems, MOOCs, online learning, adaptive learning, and deep learning. This study provides valuable insights into the current and future research trends in learning style detection, which can support the development of adaptive e-learning systems, intelligent tutoring systems, and MOOCs. By identifying popular research topics and emerging areas of study, this research can guide the design and implementation of effective online learning environments. Additionally, the study advances the field of e-learning knowledge by providing a comprehensive overview of the most frequently used learning style models and potential research areas. It sheds light on the ongoing development of learning style detection research and the potential for future advancements in the field, ultimately contributing to the growth and improvement of e-learning practices.

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Section: Discussionmentioning

confidence: 99%

Mapping Research Themes and Future Directions in Learning Style Detection Research: A Bibliometric and Content Analysis

Wijaya,

Setiawan,

Shapiai

2023

EJEL

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“…4). Reference [39] study classified two activities open eyes (OE) and close eyes (CE) sessions for 14 subjects having a stroke. The proposed methodology uses Artificial Neural Network (ANN) to train on input signals having EEG bands i.e., alpha, beta, theta, and delta, results obtained from the study suggested that ANN can be used to successfully classify stroke using brain activity.…”

Section: B Music Methodmentioning

confidence: 99%

A Comprehensive Review on Brain Disease Mapping—The Underlying Technologies and AI Based Techniques for Feature Extraction and Classification Using EEG Signals

Sachadev

Bhatnagar

2021

Studies in Computational Intelligence

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Medical images play an important role in the diagnosis of diseases effectively. Human brain is consisting of millions of neurons which work in proper coordination with one another, and human behavior is an outcome of the response of neurons to internal/external motor or sensory stimuli. These neurons are the carriers of signals from different parts of the human body and the brain. Human cognition studies focus on interpreting either these signals or brain images and there are various technologies which are in use for brain disease studies. Every image or technology generates lot of data in different form which can be modeled using Artificial Intelligence models. The electroencephalogram (EEG) is a recording of the electrical activity of the brain from the scalp. The recorded waveforms reflect the cortical electrical activity. Signal intensity: EEG activity is quite small, measured in microvolts (mV). Delta, Theta, Alpha and Beta are the main frequencies of human EEG waves and EEG electrodes are used to generate EEG plots. In this chapter, authors have tried to capture details on various technologies, techniques, usage of AI models in Brain mapping for disease predictions. This chapter presents a comprehensive account on them and will appeal to researchers in the field of human cognition.

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Classification of hand movements of stroke patients using combination of statistical features and artificial neural network

Narudin,

Nasir,

Joret

et al. 2023

The 5th International Conference on Bioscience and Biotechnology

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Brainwave Classification of Task Performed by Stroke Patients using ANN

Cited by 3 publications

References 13 publications

Mapping Research Themes and Future Directions in Learning Style Detection Research: A Bibliometric and Content Analysis

Mapping Research Themes and Future Directions in Learning Style Detection Research: A Bibliometric and Content Analysis

A Comprehensive Review on Brain Disease Mapping—The Underlying Technologies and AI Based Techniques for Feature Extraction and Classification Using EEG Signals

Classification of hand movements of stroke patients using combination of statistical features and artificial neural network

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