Multimodal BCI system for user's performance assessment
As technology advances, more learning materials for students are shifted to an online platform. Learning through online platform must be engaging enough for the students to stay focus and continue learning. Thus, it is crucial to evaluate the engagement level of the students. Multimodal Brain Comput...
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2018
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sg-ntu-dr.10356-744532023-07-07T17:34:23Z Multimodal BCI system for user's performance assessment Nageshwari Rajadharen Zhong Wende School of Electrical and Electronic Engineering A*STAR Institute for Infocomm Research DRNTU::Science As technology advances, more learning materials for students are shifted to an online platform. Learning through online platform must be engaging enough for the students to stay focus and continue learning. Thus, it is crucial to evaluate the engagement level of the students. Multimodal Brain Computer Interface (BCI) system is developed to effectively assess the learning performance of the students. Two different experiments were conducted. For the 1st experiment, electroencephalogram (EEG), gaze positions and photoplethysmogram (PPG) of the students are recorded. For the 2nd experiment, gaze positions and EEG of the students are recorded. Features are extracted from each modality. Data analysis are conducted to analyse the features extracted in depth. Results have revealed that the eye-tracker used is of high accuracy and music does distract the students as their attention level tends to decrease. Not only that, it is also observed that the students showed negative feelings towards the end of the experiment. Using only one modality is not sufficient to assess the engagement level of the students, hence the multimodal system is able to assess visual and mental engagement more accurately. These will aid educators to better assess and understand students’ response towards the learning materials and their performance, enabling more effective intervention and meaningful improvements in learning materials. Bachelor of Engineering 2018-05-18T02:47:43Z 2018-05-18T02:47:43Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/74453 en Nanyang Technological University 57 p. application/pdf |
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As technology advances, more learning materials for students are shifted to an online platform. Learning through online platform must be engaging enough for the students to stay focus and continue learning. Thus, it is crucial to evaluate the engagement level of the students. Multimodal Brain Computer Interface (BCI) system is developed to effectively assess the learning performance of the students. Two different experiments were conducted. For the 1st experiment, electroencephalogram (EEG), gaze positions and photoplethysmogram (PPG) of the students are recorded. For the 2nd experiment, gaze positions and EEG of the students are recorded. Features are extracted from each modality. Data analysis are conducted to analyse the features extracted in depth. Results have revealed that the eye-tracker used is of high accuracy and music does distract the students as their attention level tends to decrease. Not only that, it is also observed that the students showed negative feelings towards the end of the experiment.
Using only one modality is not sufficient to assess the engagement level of the students, hence the multimodal system is able to assess visual and mental engagement more accurately. These will aid educators to better assess and understand students’ response towards the learning materials and their performance, enabling more effective intervention and meaningful improvements in learning materials. |
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Zhong Wende |
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Zhong Wende Nageshwari Rajadharen |
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Final Year Project |
author |
Nageshwari Rajadharen |
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Nageshwari Rajadharen |
title |
Multimodal BCI system for user's performance assessment |
title_short |
Multimodal BCI system for user's performance assessment |
title_full |
Multimodal BCI system for user's performance assessment |
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Multimodal BCI system for user's performance assessment |
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Multimodal BCI system for user's performance assessment |
title_sort |
multimodal bci system for user's performance assessment |
publishDate |
2018 |
url |
http://hdl.handle.net/10356/74453 |
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1772828327343554560 |