Statistical Modeling and Forecasting of EEG Signal for BCI System Using ARIMA Model

dc.contributor.author	Asst. Prof. Dr. Naufel B. Mohammed
dc.date.accessioned	2025-12-30T18:14:08Z
dc.date.issued	2023-03-30
dc.description.abstract	This paper presents a description and building of a statistical model of EEG signals with an ARIMA forecasting process. EEG measurement principles are explained for understanding EEG signals features and noise which is essential for building real-time Brain-Computer Interface (BCI). Different statistical modeling and forecasting methods of EEG signals are discussed.
dc.format	application/pdf
dc.identifier.uri	https://scientifictrends.org/index.php/ijst/article/view/78
dc.identifier.uri	https://asianeducationindex.com/handle/123456789/32978
dc.language.iso	eng
dc.publisher	Scientific Trends
dc.relation	https://scientifictrends.org/index.php/ijst/article/view/78/71
dc.rights	https://creativecommons.org/licenses/by-nc-nd/4.0
dc.source	International Journal of Scientific Trends; Vol. 2 No. 3 (2023): IJST; 66-74
dc.source	2980-4299
dc.source	2980-4329
dc.subject	Statistical modeling, EEG signals, BCI, Forecasting, Correlation, ARIMA.
dc.title	Statistical Modeling and Forecasting of EEG Signal for BCI System Using ARIMA Model
dc.type	info:eu-repo/semantics/article
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	Peer-reviewed Article

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