Author
Listed:
- Maryam Sorkhi
(School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran)
- Mohammad Reza Jahed-Motlagh
(School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran†School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran)
- Behrouz Minaei-Bidgoli
(School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran)
- Mohammad Reza Daliri
(��School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran)
Abstract
Since EEG signals encode an individual’s intent of executing an action, scientists have extensively focused on this topic. Motor Imagery (MI) signals have been used by researchers to assistance disabled persons, for autonomous driving and even control devices such as wheelchairs. Therefore, accurate decoding of these signals is essential to develop a Brain–Computer interface (BCI) systems. Due to dynamic nature, low signal-to-noise ratio and complexity of EEG signals, EEG decoding is not simple task. Extracting temporal and spatial features from EEG is accessible via Convolution neural network (CNN). However, enhanced CNN models are required to learn the dynamic correlations existing in MI signals. It is found that good features are extracted via CNN in both deep and shallow models, which indicate that various levels related features can be mined. In this case, spatial patterns from multi-scaled data in different frequency bands are learnt at first and then the temporal and frequency band information from projected signals is extracted. Here, to make use of neural activity phenomena, the feature extraction process employed is based on Multi-scale FBCSP (MSFBCSP). In CNN, the envelope of each spatially filtered signal is extracted in time dimension by performing Hilbert transform. However, to access common morphologies, the convolutional operation across time is performed first and then another convolution layer across channels in the frequency band is used to represent the carried information in a more compact form. Moreover, Bayesian approach is used for mapping hyperparameters to a probability of score on the objective function. The prominent feature of the proposed network is the high capacity of preserving and utilizing the information encoded in frequency bands. Our proposed method significantly improves the efficiency of current classification method in specific dataset of the physionet. According to empirical evaluations, strong robustness and high decoding classification are two distinctive characteristics of our proposed work.
Suggested Citation
Maryam Sorkhi & Mohammad Reza Jahed-Motlagh & Behrouz Minaei-Bidgoli & Mohammad Reza Daliri, 2023.
"Learning temporal-frequency features of physionet EEG signals using deep convolutional neural network,"
International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 34(04), pages 1-20, April.
Handle:
RePEc:wsi:ijmpcx:v:34:y:2023:i:04:n:s012918312350047x
DOI: 10.1142/S012918312350047X
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wsi:ijmpcx:v:34:y:2023:i:04:n:s012918312350047x. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/ijmpc/ijmpc.shtml .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.