Gao, Terry and Wang, Grace Ying 
ORCID: https://orcid.org/0000-0003-2063-031X
(2020)
Brain Signal Classification Based on Deep CNN.
International Journal of Security and Privacy in Pervasive Computing, 12 (2).
pp. 17-29.
ISSN 2643-7937
Abstract
It is essential to increase the accuracy and robustness of classification of brain data, including EEG, in order to facilitate a direct communication between the human brain and computerized devices. Different machine learning approaches, such as support vector machine (SVM), neural network, and linear discrimination analysis (LDA), have been applied to set up automatic subjective-classifier, and the findings for their capacities in this regard have been inconclusive. The present study developed an effective classifier for human mental status using deep learning in a convolutional neural network. In contrast to most previous studies commonly using EEG waveform or numeric value of brain signals for classification, the authors utilised imaging features generated from EEG data at alpha frequency band. A new model proposed in this study provides a simple and computationally efficient approach to distinguish mental status during resting. With training, this model could predict new 2D EEG images with above 90% accuracy, while traditional machine learning techniques failed to achieve this accuracy.
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| Item Type: | Article (Commonwealth Reporting Category C) |
|---|---|
| Refereed: | Yes |
| Item Status: | Live Archive |
| Additional Information: | Files associated with this item cannot be displayed due to copyright restrictions. |
| Faculty/School / Institute/Centre: | No Faculty |
| Faculty/School / Institute/Centre: | No Faculty |
| Date Deposited: | 19 May 2022 22:55 |
| Last Modified: | 31 May 2022 04:12 |
| Uncontrolled Keywords: | Neural Network; Machine Learning; Mental Status; Machine Learning Techniques; Support Vector; Imaging Features; Learning Approaches; Computationally Efficient; Set Up; Brain Data |
| Fields of Research (2020): | 52 PSYCHOLOGY > 5204 Cognitive and computational psychology > 520499 Cognitive and computational psychology not elsewhere classified |
| Socio-Economic Objectives (2020): | 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280121 Expanding knowledge in psychology |
| Identification Number or DOI: | https://doi.org/10.4018/IJSPPC.2020040102 |
| URI: | http://eprints.usq.edu.au/id/eprint/48507 |
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