Real-time classification of EEG signals using Machine Learning deployment
The prevailing educational methods predominantly rely on traditional classroom instruction or online delivery, often limiting the teachers’ ability to engage effectively with all the students simultaneously. A more intrinsic method of evaluating student attentiveness during lectures can enable the e...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
ICI Publishing House
2024-12-01
|
| Series: | Revista Română de Informatică și Automatică |
| Subjects: | |
| Online Access: | https://rria.ici.ro/documents/1226/art._1_Chowdhuri_et_al_India.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832593928795193344 |
|---|---|
| author | Swati CHOWDHURI Satadip SAHA Samadrita KARMAKAR Ankur CHANDA |
| author_facet | Swati CHOWDHURI Satadip SAHA Samadrita KARMAKAR Ankur CHANDA |
| author_sort | Swati CHOWDHURI |
| collection | DOAJ |
| description | The prevailing educational methods predominantly rely on traditional classroom instruction or online delivery, often limiting the teachers’ ability to engage effectively with all the students simultaneously. A more intrinsic method of evaluating student attentiveness during lectures can enable the educators to tailor the course materials and their teaching styles in order to better meet the students' needs. The aim of this paper is to enhance teaching quality in real time, thereby fostering a higher student engagement in the classroom activities. By monitoring the students' electroencephalography (EEG) signals and employing machine learning algorithms, this study proposes a comprehensive solution for addressing this challenge. Machine learning has emerged as a powerful tool for simplifying the analysis of complex variables, enabling the effective assessment of the students' concentration levels based on specific parameters. However, the realtime impact of machine learning models necessitates a careful consideration as their deployment is concerned. This study proposes a machine learning-based approach for predicting the level of students' comprehension with regard to a certain topic. A browser interface was introduced that accesses the values of the system's parameters to determine a student's level of concentration on a chosen topic. The deployment of the proposed system made it necessary to address the real-time challenges faced by the students, consider the system's cost, and establish trust in its efficacy. This paper presents the efforts made for approaching this pertinent issue through the implementation of innovative technologies and provides a framework for addressing key considerations for future research directions. |
| format | Article |
| id | doaj-art-60a45a84204f42f78c52b27ce400033a |
| institution | Kabale University |
| issn | 1220-1758 1841-4303 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | ICI Publishing House |
| record_format | Article |
| series | Revista Română de Informatică și Automatică |
| spelling | doaj-art-60a45a84204f42f78c52b27ce400033a2025-01-20T07:38:27ZengICI Publishing HouseRevista Română de Informatică și Automatică1220-17581841-43032024-12-01344171810.33436/v34i4y202401Real-time classification of EEG signals using Machine Learning deployment Swati CHOWDHURI0Satadip SAHA1Samadrita KARMAKAR2Ankur CHANDA3Department of Electrical and Electronics Engineering, Institute of Engineering & Management, University of Engineering & Management (UEM), Kolkata, India Department of Electrical and Electronics Engineering, Institute of Engineering & Management, University of Engineering & Management (UEM), Kolkata, India Department of Electrical and Electronics Engineering, Institute of Engineering & Management, University of Engineering & Management (UEM), Kolkata, India Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), Institute of Engineering & Management, University of Engineering & Management (UEM), Kolkata, India The prevailing educational methods predominantly rely on traditional classroom instruction or online delivery, often limiting the teachers’ ability to engage effectively with all the students simultaneously. A more intrinsic method of evaluating student attentiveness during lectures can enable the educators to tailor the course materials and their teaching styles in order to better meet the students' needs. The aim of this paper is to enhance teaching quality in real time, thereby fostering a higher student engagement in the classroom activities. By monitoring the students' electroencephalography (EEG) signals and employing machine learning algorithms, this study proposes a comprehensive solution for addressing this challenge. Machine learning has emerged as a powerful tool for simplifying the analysis of complex variables, enabling the effective assessment of the students' concentration levels based on specific parameters. However, the realtime impact of machine learning models necessitates a careful consideration as their deployment is concerned. This study proposes a machine learning-based approach for predicting the level of students' comprehension with regard to a certain topic. A browser interface was introduced that accesses the values of the system's parameters to determine a student's level of concentration on a chosen topic. The deployment of the proposed system made it necessary to address the real-time challenges faced by the students, consider the system's cost, and establish trust in its efficacy. This paper presents the efforts made for approaching this pertinent issue through the implementation of innovative technologies and provides a framework for addressing key considerations for future research directions. https://rria.ici.ro/documents/1226/art._1_Chowdhuri_et_al_India.pdfelectroencephalogrammachine learningreal-time impactbrain-computer interface (bci)signal classification |
| spellingShingle | Swati CHOWDHURI Satadip SAHA Samadrita KARMAKAR Ankur CHANDA Real-time classification of EEG signals using Machine Learning deployment Revista Română de Informatică și Automatică electroencephalogram machine learning real-time impact brain-computer interface (bci) signal classification |
| title | Real-time classification of EEG signals using Machine Learning deployment |
| title_full | Real-time classification of EEG signals using Machine Learning deployment |
| title_fullStr | Real-time classification of EEG signals using Machine Learning deployment |
| title_full_unstemmed | Real-time classification of EEG signals using Machine Learning deployment |
| title_short | Real-time classification of EEG signals using Machine Learning deployment |
| title_sort | real time classification of eeg signals using machine learning deployment |
| topic | electroencephalogram machine learning real-time impact brain-computer interface (bci) signal classification |
| url | https://rria.ici.ro/documents/1226/art._1_Chowdhuri_et_al_India.pdf |
| work_keys_str_mv | AT swatichowdhuri realtimeclassificationofeegsignalsusingmachinelearningdeployment AT satadipsaha realtimeclassificationofeegsignalsusingmachinelearningdeployment AT samadritakarmakar realtimeclassificationofeegsignalsusingmachinelearningdeployment AT ankurchanda realtimeclassificationofeegsignalsusingmachinelearningdeployment |