Brain Signal Classification Based on Deep CNN
Article
Article Title | Brain Signal Classification Based on Deep CNN |
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Article Category | Article |
Authors | Gao, Terry (Author) and Wang, Grace Ying (Author) |
Journal Title | International Journal of Security and Privacy in Pervasive Computing |
Journal Citation | 12 (2), pp. 17-29 |
Number of Pages | 13 |
Year | 2020 |
Place of Publication | United States |
Digital Object Identifier (DOI) | https://doi.org/10.4018/IJSPPC.2020040102 |
Web Address (URL) | https://www.igi-global.com/article/brain-signal-classification-based-on-deep-cnn/259340 |
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. |
Keywords | Neural Network; Machine Learning; Mental Status; Machine Learning Techniques; Support Vector; Imaging Features; Learning Approaches; Computationally Efficient; Set Up; Brain Data |
ANZSRC Field of Research 2020 | 520499. Cognitive and computational psychology not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Counties Manukau District Health Board, New Zealand |
Auckland University of Technology, New Zealand | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q7563/brain-signal-classification-based-on-deep-cnn
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