Abstract
In neuroscience, Electroencephalography (EEG) can be considered as an extensively recognized process, implemented for the purpose of extracting brain signal activities related to involuntary and voluntary tasks. The researchers and scientists in the neuroscience field are concerned with studying the brain-computer interfacing (BCI) as well as enhancing the current systems of BCI. In the brain-computer interfacing area, Brain-Computer Interface (BCI) can be defined as a communication system that is developed for allowing individuals experiencing complete paralysis sending commands or messages without sending them via the normal output path-way of the brain. This study implements algorithms which can separate and classify task-related Electroencephalography (EEG) signals from ongoing EEG signals. The separation was made by hybridization between the classical method represented by the filtering process and modern method represented by Stone’s BSS technique assessed to their capability of isolating and deleting electromyography (EMG), electrooculographic (EOG), (ECG) and power line (LN) artifacts into individual components that are considered artifacts to affect the performance of the system, and task-related Electroencephalography (EEG) signal are classified by Naïve Bayes (NB). The obtained results of the recognition rate were 82% by proposed algorithms. The results are compatible with previous studies.
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Abass, Z.K., Hasan, T.M., Abdullah, A.K. (2020). Brain Computer Interface Enhancement Based on Stones Blind Source Separation and Naive Bayes Classifier. In: Al-Bakry, A., et al. New Trends in Information and Communications Technology Applications. NTICT 2020. Communications in Computer and Information Science, vol 1183. Springer, Cham. https://doi.org/10.1007/978-3-030-55340-1_2
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DOI: https://doi.org/10.1007/978-3-030-55340-1_2
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