Original scientific paper
https://doi.org/10.1080/00051144.2023.2220207
Enhanced EEG classification using adaptive DWT and heuristic-ICA algorithm
P. Visu
; Department of AI & DS, Velammal Engineering College, Chennai, India
P. S. Smitha
; Department of CSE, Velammal Engineering College, Chennai, India
Murugananthan Velayutham
; School of Computing, Asia Pacific University of Technology & Innovation (APU), Kuala Lumpur, Malaysia
Mohd Wazih Ahmad
; Chair Intelligent Systems SIG, Adama Science and Technology University, Adama, Ethiopia
Abstract
Electroencephalography (EEG) signals contain important information about the inner functioning of the brain. Effective extraction of this information will help in the detection of brain-related health conditions and emotions of a person or it can also be used as a communication medium between humans and machines. In our proposed system, we introduced Adaptive DWT by combining the temporal resolution capability of DWT, with the special capability of Fourier transform to remove the artefacts in the signal. This is achieved by using an adaptive thresholding function rather than hard or soft thresholding to improve the quality parameters of the signal. The proposed filtering model has improved the Signal to Noise ratio when compared to traditional filtering techniques. EEG features are extracted with the help of Heuristic-Independent Component Analysis (ICA) by applying covariance to equalize or improve the data. The main drawback with the existing CNN algorithm is gradient vanishing during training, this reduces the overall performance of the algorithm during classification. Therefore, using the memory function to store the previous value of iteration improves the classification accuracy and reduces the gradient vanishing problem. The proposed technique is found to have better accuracy of about 98% in classifying autism and epilepsy datasets.
Keywords
Principal component analysis (PCA); support vector machine (SVM); electroencephalogram (EEG); discrete wavelet transform (DWT)
Hrčak ID:
315940
URI
Publication date:
13.6.2023.
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