Feature Scaling For EEG Human Concentration Using Particle Swarm Optimization
Electroencephalograph (EEG) is a one of recording technique that is widely used to measure human activities through brain signals. One of actively growing research in the past years is to measure human concentration using EEG. Obtaining relevant features for recognizing human concentration state bec...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IEEE
2017
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/18259/ http://umpir.ump.edu.my/id/eprint/18259/ http://umpir.ump.edu.my/id/eprint/18259/1/Feature%20scaling%20for%20EEG%20human%20concentration%20using%20particle%20swarm%20optimization.pdf http://umpir.ump.edu.my/id/eprint/18259/2/Feature%20scaling%20for%20EEG%20human%20concentration%20using%20particle%20swarm%20optimization%201.pdf |
| Summary: | Electroencephalograph (EEG) is a one of recording technique that is widely used to measure human activities through brain signals. One of actively growing research in the past years is to measure human concentration using EEG. Obtaining relevant features for recognizing human concentration state becomes a challenging task due to the nature of EEG signals is a non-stationary. In the past research, various combinations of features have been employed. However, to improve the classification performance, determining the importance of each employed feature is crucially needed. In this study, feature scaling method is introduced to assign different weights for important features. Four different features are investigated in frequency domain using wavelet transform (WT). Then, particle swarm optimization (PSO) is used to scale the features while extreme learning machine (ELM) is used to classify between concentration and non-concentration states. The recorded EEG signals from Neurosky Mindwave are used to evaluate the performance of the proposed technique. The final results indicate that the proposed technique offers higher performance accuracy as compared to the methods without feature scaling. |
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