How to cite (IEEE): M. Melinda, O. Maulisa, N. Nissa, Y. Yunidar, & E. I Ketut "Classification of EEG Signal using Independent Component Analysis and Discrete Wavelet Transform based on Linear Discriminant Analysis," JOIV : International Journal on Informatics Visualization, vol. 7, no. 3, , pp. 830-838, Sep. 2023. https://doi.org/10.30630/joiv.7.3.1219

How to cite (APA): Melinda, M., Maulisa, O., Nissa, N., Yunidar, Y., & I Ketut, E. (2023). Classification of EEG Signal using Independent Component Analysis and Discrete Wavelet Transform based on Linear Discriminant Analysis. JOIV : International Journal on Informatics Visualization, 7(3), 830-838. https://doi.org/10.30630/joiv.7.3.1219

How to cite (Chicago): Melinda, Melinda, Maulisa, Oktiana, Nissa, Nabila, Yunidar, Yunidar, AND I Ketut, Enriko. "Classification of EEG Signal using Independent Component Analysis and Discrete Wavelet Transform based on Linear Discriminant Analysis" JOIV : International Journal on Informatics Visualization [Online], Volume 7 Number 3 (10 September 2023) https://doi.org/10.30630/joiv.7.3.1219

How to cite (Vancouver): Melinda M, Maulisa O, Nissa N, Yunidar Y, & I Ketut E . Classification of EEG Signal using Independent Component Analysis and Discrete Wavelet Transform based on Linear Discriminant Analysis. JOIV : International Journal on Informatics Visualization [Online]. 2023 Sep; 7(3):830-838. https://doi.org/10.30630/joiv.7.3.1219

How to cite (Harvard): Melinda, M., Maulisa, O., Nissa, N., Yunidar, Y., & I Ketut, E. ,2023. Classification of EEG Signal using Independent Component Analysis and Discrete Wavelet Transform based on Linear Discriminant Analysis. JOIV : International Journal on Informatics Visualization, [Online] 7(3), pp. 830-838. https://doi.org/10.30630/joiv.7.3.1219

How to cite (MLA8): Melinda, Melinda, Oktiana Maulisa, Nabila Hasna Nissa, Yunidar Yunidar, & Enriko Agung I Ketut. "Classification of EEG Signal using Independent Component Analysis and Discrete Wavelet Transform based on Linear Discriminant Analysis." JOIV : International Journal on Informatics Visualization [Online], 7.3 (2023): 830-838. Web. 2 Oct. 2023 , https://doi.org/10.30630/joiv.7.3.1219

BibTex Citation Data :

@article{JOIV1219,
    	author = {Melinda Melinda and Oktiana Maulisa and Nabila Nissa and Yunidar Yunidar and Enriko I Ketut},
    	title = {Classification of EEG Signal using Independent Component Analysis and Discrete Wavelet Transform based on Linear Discriminant Analysis},
    	journal = {JOIV : International Journal on Informatics Visualization},
    	volume = {7},
    	number = {3},
    	year = {2023},
    	keywords = {Autism; Electroencephalogram; Linear Discriminant Analysis; Independent Component Analysis; Discrete Wavelet Transform},
    	abstract = {Autism Spectrum Disorder (ASD) is a neurodevelopment syndrome decreasing sufferers' social interaction, communication skills, and emotional expression. Autism syndrome can be detected using an electroencephalogram (EEG). This study utilized the EEG of autistic people to support the classification study of machine learning schemes to produce the best accuracy. One of the best approaches to classify the EEG signal is The Linear Discriminant Analysis (LDA), a machine learning technique to classify autism and normal EEG signals. LDA was chosen because it can maximize the distance between classes and minimize the number of scatters by utilizing between and within-class functions. This method was combined with other methods: Independent Components Analysis (ICA) and Discrete Wavelet Transform (DWT), to improve the accuracy system. ICA removes artifacts or signals other than brain signals that can cause noise in the EEG signal, so the analyzed signal was a complete EEG signal without other factors. DWT can help increase noise suppression in the EEG signal and provide signal information through frequency and time representation. The EEG dataset was collated from 16 children (eight autistic and eight normal). The signals from the dataset were filtered by artifacts using ICA, decomposed by three levels through DWT, and classified using the Linear Discriminant Analysis (LDA) technique. Using the Confusion Matrix, the results reveal the best accuracy of 99%.},
        	issn = {2549-9904},    	pages = {830--838},    	doi = {10.30630/joiv.7.3.1219},
        	url = {https://joiv.org/index.php/joiv/article/view/1219}
    }
    

Refworks Citation Data :

@article{{JOIV}{1219}, author = {Melinda, M., Maulisa, O., Nissa, N., Yunidar, Y., I Ketut, E.}, title = {Classification of EEG Signal using Independent Component Analysis and Discrete Wavelet Transform based on Linear Discriminant Analysis}, journal = {JOIV : International Journal on Informatics Visualization}, volume = {7}, number = {3}, year = {2023}, doi = {10.30630/joiv.7.3.1219}, url = {} }