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The Classification of Eye State by Using kNN and MLP Classification Models According to the EEG Signals
Open accessAbstract
What is widely used for classification of eye state to detect human’s cognition state is electroencephalography (EEG). In this study, the usage of EEG signals for online eye state detection method was proposed. In this study, EEG eye state dataset that is obtained from UCI machine learning repository database was used. Continuous 14 EEG measurements forms the basic of the dataset. The duration of the measurement is 117 seconds (each measurement has14980 sample). Weka (Waikato Environment for Knowledge Analysis) program is used for classification of eye state. Classification success was calculated by using k-Nearest Neighbors algorithm and multilayer perceptron neural networks models. The obtained success of classification methods were compared. The classification success rates were calculated for various number of neurons in the hidden layer of a multilayer perceptron neural network model. The highest classification success rate have been obtained when the number of neurons in the hidden layer was equal to 7. And it was 56.45%. The classification success rates were calculated with k-nearest neighbors algorithm for different neighbourhood values. The highest success was achieved in the classification made with kNN algorithm. In kNN models, the success rate for 3 nearest neighbor were calculated as 84.05%.
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“From application view, KNN has been widely applied in electroencephalogram signal classification tasks [58] to classify health conditions such as rubeosis iridis, breast cancer and diabetes.”
From Integrating AI in engineering education: A comprehensive review and student-informed module design for U.K. students (2025) · cites this paper as [58]
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Research works printed but not linked: 15
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Hao, Y., Liu, Y., Liu, B., Amarantidis, G., & Ghannam, R. (2025) · IEEE Transactions on Education · doi:10.1109/te.2025.3536105 · Cited by 32
“From application view, KNN has been widely applied in electroencephalogram signal classification tasks [58] to classify health conditions, such as rubeosis iridis, breast cancer and diabetes.”
Metadata
- Authors
- Sabancı, K., & Köklü, M.
- Affiliations
- Karamanoğlu Mehmetbey University (TR)
- Selçuk University (TR)
- Corresponding author
- Köklü, Murat
- Venue
- International Journal of Intelligent Systems and Applications in Engineering
- Published
- 2015-12-30
- Volume
- 3
- Issue
- 4
- Pages
- 127
- ISSN
- 2147-6799 (ISSN-L)
- OpenAlex
- W2272053632
- Cited by
- 62
- References
- 0
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 5 · 2023: 9 · 2022: 6 · 2021: 14 · 2020: 6 · 2019: 7 · 2018: 4 · 2017: 4 · 2016: 3
- FWCI
- 2.20
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- International Journal of Intelligent Systems and Applications in Engineering (publishedVersion)
- International Journal of Intelligent Systems and Applications in Engineering (publishedVersion, cc-by-sa)
- DergiPark (Istanbul University) (submittedVersion)
- eArşiv - KMÜ (Karamanoğlu Mehmetbey University) (submittedVersion, cc-by-nc-nd)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- eye state classification, EEG signals, k-nearest neighbors, multilayer perceptron, hidden neuron number, k-value selection, EEG dataset, cognitive state detection
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- Sabancı, K., & Köklü, M. (2015). The Classification of Eye State by Using kNN and MLP Classification Models According to the EEG Signals. International Journal of Intelligent Systems and Applications in Engineering. https://doi.org/10.18201/ijisae.75836
