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A Comparison of the Analysis of Methods for Feature Extraction and Classification by Wavelet Transform in SSVEP BCIs
Zahra Einalou
Islamic Azad University Tehran North Branch
Corresponding Author
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Most of the studies in the field of Brain-Computer Interface (BCI) based on electroencephalography have a wide range of applications. Extracting Steady State Visual Evoked Potential (SSVEP) is regarded as one of the most useful tools in BCI systems. In this study, different methods such as feature extraction with different spectral methods (Shannon entropy, skewness, kurtosis, mean, variance) (bank of filters, narrow-bank IIR filters, and wavelet transform magnitude), feature selection performed by various methods (decision tree, principle component analysis (PCA), t-test, Wilcoxon, Receiver operating characteristic (ROC)), and classification step applying k nearest neighbor (k-NN), perceptron, support vector machines (SVM), Bayesian, multiple layer perceptron (MLP) were compared from the whole stream of signal processing.
Through combining such methods, the effective overview of the study indicated the accuracy of classical methods. In addition, the present study relied on a rather new feature selection described by decision tree and PCA, which is used for the BCI-SSVEP systems. Finally, the obtained accuracies were calculated based on the four recorded frequencies representing four directions including right, left, up, and down.
Keywords
Brain Computer Interface, Steady State Visual Evoked Potential, Classification, Wavelet
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CURRENT STATUS: Under Review
Version 1
Posted 28 Sep, 2020
No community comments so far
Review #3 received
Received 14 Nov, 2020
Editorial decision: Major revision
On 14 Nov, 2020
Review #1 received
Received 23 Oct, 2020
Review #2 received
Received 22 Oct, 2020
Reviewer #4 agreed
On 19 Oct, 2020
Reviewers invited
Invitations sent on 12 Oct, 2020
Reviewer #1 agreed
On 12 Oct, 2020
Reviewer #2 agreed
On 12 Oct, 2020
Reviewer #3 agreed
On 12 Oct, 2020
Editor assigned
On 23 Sep, 2020
Submission checks complete
On 22 Sep, 2020
Editor invited
On 22 Sep, 2020
First submitted
On 21 Sep, 2020
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Subject Areas
Biomedical Engineering
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This preprint is under consideration at Brain Informatics. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
A Comparison of the Analysis of Methods for Feature Extraction and Classification by Wavelet Transform in SSVEP BCIs
Zahra Einalou
Islamic Azad University Tehran North Branch
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 28 Sep, 2020
No community comments so far
Review #3 received
Received 14 Nov, 2020
Editorial decision: Major revision
On 14 Nov, 2020
Review #1 received
Received 23 Oct, 2020
Review #2 received
Received 22 Oct, 2020
Reviewer #4 agreed
On 19 Oct, 2020
Reviewers invited
Invitations sent on 12 Oct, 2020
Reviewer #1 agreed
On 12 Oct, 2020
Reviewer #2 agreed
On 12 Oct, 2020
Reviewer #3 agreed
On 12 Oct, 2020
Editor assigned
On 23 Sep, 2020
Submission checks complete
On 22 Sep, 2020
Editor invited
On 22 Sep, 2020
First submitted
On 21 Sep, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Biomedical Engineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Most of the studies in the field of Brain-Computer Interface (BCI) based on electroencephalography have a wide range of applications. Extracting Steady State Visual Evoked Potential (SSVEP) is regarded as one of the most useful tools in BCI systems. In this study, different methods such as feature extraction with different spectral methods (Shannon entropy, skewness, kurtosis, mean, variance) (bank of filters, narrow-bank IIR filters, and wavelet transform magnitude), feature selection performed by various methods (decision tree, principle component analysis (PCA), t-test, Wilcoxon, Receiver operating characteristic (ROC)), and classification step applying k nearest neighbor (k-NN), perceptron, support vector machines (SVM), Bayesian, multiple layer perceptron (MLP) were compared from the whole stream of signal processing.
Through combining such methods, the effective overview of the study indicated the accuracy of classical methods. In addition, the present study relied on a rather new feature selection described by decision tree and PCA, which is used for the BCI-SSVEP systems. Finally, the obtained accuracies were calculated based on the four recorded frequencies representing four directions including right, left, up, and down.
Figure 1
Figure 2
Figure 3
Figure 4
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.