E3S Web Conf.
Volume 351, 202210th International Conference on Innovation, Modern Applied Science & Environmental Studies (ICIES’2022)
|Number of page(s)||5|
|Published online||24 May 2022|
Impact of the preprocessing block on the performance of the ΒCI system
Laboratory of Metrology and Information Processing, Faculty of Sciences, Ibn Zohr University, Agadir, Morocco
* Corresponding author: email@example.com
Electroencephalography (ΕΕG) is considered as one of the famous and efficient used methods in the Brain Computer Interface (ΒCI). This is due to its simplicity for implementation, low cost and being portable. The ΕΕG is a technique that examines the electrical activity of the brain using a non-invasive electrodes placed on the scalp. ΕΕG-based BCI system is constituted of five blocks: signal acquisition, preprocessing, feature extraction, classification and command block. In this paper, we will study the impact of the filter type and its order on the performance of the considered BCI system. This system is composed of: bandpass (ΒΡ) filter for the preprocessing step, Common Spatial Pattern (CSP) in the feature extraction block, and for the classification block, we used Support Vector Machine (SVΜ). The obtained results show a good improvement of the proposed BCI system. Indeed, the accuracy of this system can achieve 88.17% and the kappa coefficient is almost 0.76.
Key words: ElectroEncephaloGram (ΕΕG) / Motor Imagery (ΜI) / Brain Computer Interface (ΒCI) / Band-Pass (BP) filter / Common Spatial Pattern (CSP) / Support Vector Machine (SVΜ)
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.