Analyzing recognition of EEG based human attention and emotion using machine learning
An emotionally recognised area of research has already been quite prominent. EEG brain signals have recently been used to recognise an individual's mental condition. Attention often plays a key role in human development, but needs more study. This article offers a noble method of acknowledgment...
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| Format: | Article |
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Elsevier Ltd
2022
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| _version_ | 1796867013927239680 |
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| author | Alam, Mohammad Shabbir A. Jalil, Siti Zura Upreti, Kamal |
| author_facet | Alam, Mohammad Shabbir A. Jalil, Siti Zura Upreti, Kamal |
| author_sort | Alam, Mohammad Shabbir |
| collection | ePrints |
| description | An emotionally recognised area of research has already been quite prominent. EEG brain signals have recently been used to recognise an individual's mental condition. Attention often plays a key role in human development, but needs more study. This article offers a noble method of acknowledgment of human attention by sophisticated machine learning algorithms. Scalp-EEG signalling is a cost-effective, single-swinged mechanism dependent on time. Many trials have shown possible support for emotional identification through brain EEG waves. This paper examines and suggests a modern technology for the identification of emotions through the application of new computer learning principles. Ablations experiments also demonstrate the clear and important benefit to the efficiency of our RGNN model from the adjacent matrix and two regularizers. Finally, neuronal researches reveal key brain regions and inter-channel relationships for EEG related emotional awareness. |
| first_indexed | 2024-03-05T21:20:49Z |
| format | Article |
| id | utm.eprints-101172 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T21:20:49Z |
| publishDate | 2022 |
| publisher | Elsevier Ltd |
| record_format | dspace |
| spelling | utm.eprints-1011722023-06-01T09:32:00Z http://eprints.utm.my/101172/ Analyzing recognition of EEG based human attention and emotion using machine learning Alam, Mohammad Shabbir A. Jalil, Siti Zura Upreti, Kamal QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering An emotionally recognised area of research has already been quite prominent. EEG brain signals have recently been used to recognise an individual's mental condition. Attention often plays a key role in human development, but needs more study. This article offers a noble method of acknowledgment of human attention by sophisticated machine learning algorithms. Scalp-EEG signalling is a cost-effective, single-swinged mechanism dependent on time. Many trials have shown possible support for emotional identification through brain EEG waves. This paper examines and suggests a modern technology for the identification of emotions through the application of new computer learning principles. Ablations experiments also demonstrate the clear and important benefit to the efficiency of our RGNN model from the adjacent matrix and two regularizers. Finally, neuronal researches reveal key brain regions and inter-channel relationships for EEG related emotional awareness. Elsevier Ltd 2022 Article PeerReviewed Alam, Mohammad Shabbir and A. Jalil, Siti Zura and Upreti, Kamal (2022) Analyzing recognition of EEG based human attention and emotion using machine learning. Materials Today: Proceedings, 56 (6). pp. 3349-3354. ISSN 2214-7853 http://dx.doi.org/10.1016/j.matpr.2021.10.190 DOI:10.1016/j.matpr.2021.10.190 |
| spellingShingle | QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Alam, Mohammad Shabbir A. Jalil, Siti Zura Upreti, Kamal Analyzing recognition of EEG based human attention and emotion using machine learning |
| title | Analyzing recognition of EEG based human attention and emotion using machine learning |
| title_full | Analyzing recognition of EEG based human attention and emotion using machine learning |
| title_fullStr | Analyzing recognition of EEG based human attention and emotion using machine learning |
| title_full_unstemmed | Analyzing recognition of EEG based human attention and emotion using machine learning |
| title_short | Analyzing recognition of EEG based human attention and emotion using machine learning |
| title_sort | analyzing recognition of eeg based human attention and emotion using machine learning |
| topic | QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering |
| work_keys_str_mv | AT alammohammadshabbir analyzingrecognitionofeegbasedhumanattentionandemotionusingmachinelearning AT ajalilsitizura analyzingrecognitionofeegbasedhumanattentionandemotionusingmachinelearning AT upretikamal analyzingrecognitionofeegbasedhumanattentionandemotionusingmachinelearning |