State-of-the-Art Versus Deep Learning: A Comparative Study of Motor Imagery Decoding Techniques

Authors

Olawunmi Olaboopo George, Marquette University
Sarthak Dabas, Marquette University
Abdur Sikder, Marquette University
Roger O. Smith, University of Wisconsin-Milwaukee
Praveen Madiraju, Marquette UniversityFollow
Nasim Yahyasoltani, Marquette UniversityFollow
Sheikh Iqbal Ahamed, Marquette UniversityFollow

Document Type

Article

Publication Date

4-6-2022

Publisher

Institute of Electrical and Electronic Engineers

Source Publication

IEEE Access

Source ISSN

2169-3536

Original Item ID

DOI: 10.1109/ACCESS.2022.3165197

Abstract

State-of-the-art techniques (SOTA) for motor imagery decoding have largely involved the use of common spatial patterns (CSP) and power spectral density (PSD), for feature extraction. Other frequency transforms, such as wavelets and empirical mode decomposition (EMD) have also been used but the aforementioned two have been the most popular. For classification, linear discriminant analysis (LDA) and support vector machines (SVM) have been mostly used. It is, however, worth investigating other approaches, such as deep learning, which offer a potential for improvement, but are not yet mainstream. Deep learning techniques based on neural networks (NNs) have been underexplored in motor imagery processing. Considering their success in other fields, which speaks to their potential for obtaining improved results over the SOTA, they should be explored for motor imagery decoding. This study takes a comparative approach in the use of deep learning as compared with the SOTA. From our findings, we infer that neural networks are suitable for motor imagery decoding and might be preferable over the SOTA. The use of specific feature extraction is also not as necessary as seen with SOTA approaches, though it might offer some gains in performance. Our results show a statistically significant improvement in decoding accuracies, up to 20% increase, with the use of NNs as compared with the SOTA. Also, we conclude that the use of crops for data augmentation might yield better results with shallow architectures as against deeper ones and that there might be other factors affecting the effectiveness of crops, needing further investigation.

Comments

Published version. IEEE Access, Vol. 10 (April 2022): 45605-45619. DOI. © 2022 The Authors. Used with permission.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Recommended Citation

George, Olawunmi Olaboopo; Dabas, Sarthak; Sikder, Abdur; Smith, Roger O.; Madiraju, Praveen; Yahyasoltani, Nasim; and Ahamed, Sheikh Iqbal, "State-of-the-Art Versus Deep Learning: A Comparative Study of Motor Imagery Decoding Techniques" (2022). Computer Science Faculty Research and Publications. 70.
https://epublications.marquette.edu/comp_fac/70