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Edgar Allan Poe/Sullivan's Island Library
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Wando Mount Pleasant Library
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Hurd/St. Andrews Library
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EEG data based human attention recognition using various machine learning techniques: a review.
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- Author(s): Nirmala Devi, A.; Rathna, R.
- Source:
Computer Methods in Biomechanics & Biomedical Engineering: Imaging & Visualisation; Dec2024, Vol. 11 Issue 7, p1-11, 11p- Subject Terms:
- Source:
- Additional Information
- Abstract: Attention is a cognitive process that is essential for human performance. Human development frequently depends on attention; however, this topic still requires additional research. Recently, EEG brain waves have been utilised to identify a person's attention states. This work aims to review numerous machine learning algorithms to analyse the Electroencephalographic (EEG) data to recognise human attention. Various machine learning approaches for the analysis of emotional states with EEG data were reviewed. Moreover, the analysis includes the performance achieved in various works; their benefits and disadvantages are reviewed. An EEG data processing pipeline and a review of human attention recognition are developed to evaluate the performance of various machine learning techniques. According to the analysis, the neural network framework achieves 99.81% accuracy. The results serve as a design framework for future systems using EEG data on brain activity to monitor people's health. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Computer Methods in Biomechanics & Biomedical Engineering: Imaging & Visualisation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Abstract:
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