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Feature Selection o...
Feature Selection of EEG Oscillatory Activity Related to Motor Imagery Using a Hierarchical Genetic Algorithm
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- Leon, Miguel (författare)
- Mälardalens högskola,Inbyggda system
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- Ballesteros, Joaquin (författare)
- Mälardalens högskola,Inbyggda system
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- Tidare, Jonatan (författare)
- Mälardalens högskola,Inbyggda system
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- Xiong, Ning (författare)
- Mälardalens högskola,Inbyggda system
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- Åstrand, Elaine (författare)
- Mälardalens högskola,Inbyggda system
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers Inc. 2019
- 2019
- Engelska.
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Ingår i: 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728121536 ; , s. 87-94
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Motor Imagery (MI) classification from neural activity is thought to represent valuable information that can be provided as real-time feedback during rehabilitation after for example a stroke. Previous studies have suggested that MI induces partly subject-specific EEG activation patterns, suggesting that individualized classification models should be created. However, due to fatigue of the user, only a limited number of samples can be recorded and, for EEG recordings, each sample is often composed of a large number of features. This combination leads to an undesirable input data set for classification. In order to overcome this constraint, we propose a new methodology to create and select features from the EEG signal in two steps. First, the input data is divided into different windows to reduce the cardinality of the input. Secondly, a Hierarchical Genetic Algorithm is used to select relevant features using a novel fitness function which combines the data reduction with a correlation feature selection measure. The methodology has been tested on EEG oscillatory activity recorded from 6 healthy volunteers while they performed an MI task. Results have successfully proven that a classification above 75% can be obtained in a restrictive amount of time (0.02 s), reducing the number of features by almost 90%.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
Nyckelord
- EEG Signal
- Hierarchical Genetic Algorithm
- Motor Imagery
- Classification (of information)
- Data reduction
- Genetic algorithms
- Input output programs
- Neurons
- Activation patterns
- Classification models
- Correlation features
- EEG signals
- Healthy volunteers
- Real-time feedback
- Feature extraction
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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