چکیده مقاله
Using the EEG signal is very helpful for detecting emotional and cognitive states Feature selection and extraction from the recorded signal is an important part of this article and machine learning tools are used The data used in this study is related to the task of working memory and focused attention from 5 subjects with brain tumor In this way, after performing the famous Sternberg Task SWMT and listening to a speech, the EEG signals were received and stored by the device This experiment was conducted to detect 5 different classes and the data is stored in 14 different indexes for each sample In the set of methods used in this study, KNN and SVM methods performed better than others Also, in terms of class separation, these two methods were the best In terms of index selection, there is no improvement in classification accuracy, which of course could be due to the number of data indices not being very large In terms of execution speed, the KNN method is much faster than SVM, so it may be the best method in terms of accuracy and speed
کلیدواژهها
نویسندگان
Rasoul Habashi
Master's degree in bioelectrical medical engineering,
Mohammadreza Daliri
Faculty member of Iran University of Science and Technology
شیوه ارجاع
Habashi, Rasoul and Daliri, Mohammadreza,1403,Comparison of EEG signals of brain tumor patients between two cognitive skills “working memory“ and “focused attention“ using machine learning tools,1st International Congress on Cancer Prevention,Zanjan
ارائهشده در
مجموعه مقالات اولین کنگره بین المللی پیشگیری از سرطان28 شهریور 1403 · زنجان