Soft Computing-Based EEG Classification by Optimal Feature Selection and Neural Networks

Brain computer interface translates electroencephalogram (EEG) signals into control commands so that paralyzed people can control assistive devices. This human thought translation is a very challenging process as EEG signals contain noise. For noise removal, a bandpass filter or a filter bank is use...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2019-10, Vol.15 (10), p.5747-5754
Main Authors: Bhatti, Muhammad Hamza, Khan, Javeria, Khan, Muhammad Usman Ghani, Iqbal, Razi, Aloqaily, Moayad, Jararweh, Yaser, Gupta, Brij
Format: Article
Language:English
Subjects:
Bandpass filters
Brain-computer interfaces
Classification
Cutoff frequency
Datasets
Discriminant analysis
Electroencephalogram (EEG)
Electroencephalography
Feature extraction
Filter banks
Frequencies
Human-computer interface
Image classification
Linear discriminant analysis
linear discriminant analysis (LDA)
Neural networks
Radial basis function
Radial basis function networks
radial basis function neural networks (RBFNN)
sequential backward floating selection (SBFS)
Signal classification
Signal processing
Soft computing
Support vector machines
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Summary:Brain computer interface translates electroencephalogram (EEG) signals into control commands so that paralyzed people can control assistive devices. This human thought translation is a very challenging process as EEG signals contain noise. For noise removal, a bandpass filter or a filter bank is used. However, these techniques also remove useful information from the signal. Furthermore, after feature extraction, there are such features which do not play any significant role in effective classification. Thus, soft computing-based EEG classification followed by extraction and then selection of optimal features can produce better results. In this paper, subband common spatial patterns using sequential backward floating selection is being proposed in order to classify motor-imagery-based EEG signals. The signal is decomposed into subband using a filter bank having overlapped frequency cutoffs. Linear discriminant analysis followed by common spatial pattern is applied to the output of each filter for features extraction. Then, sequential backward floating selection is applied for selection of optimal features to train radial basis function neural networks. Two different datasets have been used for evaluation of results, i.e., Open BCI dataset and EEG signals acquired by Emotiv Epoc. The proposed system shows an overall accuracy of 93.05% and 85.00% for both datasets, respectively. The results show that the proposed optimal feature selection and neural network-based classification approach with overlapped frequency bands is an effective method for EEG classification as compared to previous techniques.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2019.2925624