Abstract
Electroencephalography (EEG) is almost contaminated with many artifacts while recording the brain signal activity. Clinical diagnostic and brain computer interface applications frequently require the automated removal of artifacts. In digital signal processing and visual assessment, EEG artifact removal is considered to be the key analysis technique. Nowadays, a standard method of dimensionality reduction technique like independent component analysis (ICA) and wavelet transform combination can be explored for removing the EEG signal artifacts. Manual artifact removal is time-consuming; in order to avoid this, a novel method of wavelet ICA (WICA) using fuzzy kernel support vector machine (FKSVM) is proposed for removing and classifying the EEG artifacts automatically. Proposed method presents an efficient and robust system to adopt the robotic classification and artifact computation from EEG signal without explicitly providing the cutoff value. Furthermore, the target artifacts are removed successfully in combination with WICA and FKSVM. Additionally, proposes the various descriptive statistical features such as mean, standard deviation, variance, kurtosis and range provides the model creation technique in which the training and testing the data of FKSVM is used to classify the EEG signal artifacts. The future work to implement various machine learning algorithm to improve performance of the system.
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12 August 2024
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s00500-024-10017-5
Notes
The best possible (optimal) hyperplane was calculated by finding the quadratic equation by using the Kuhn–Tucker.
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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s00500-024-10017-5
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Yasoda, K., Ponmagal, R.S., Bhuvaneshwari, K.S. et al. RETRACTED ARTICLE: Automatic detection and classification of EEG artifacts using fuzzy kernel SVM and wavelet ICA (WICA). Soft Comput 24, 16011–16019 (2020). https://doi.org/10.1007/s00500-020-04920-w
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DOI: https://doi.org/10.1007/s00500-020-04920-w