Frequency Band and PCA Feature Comparison for EEG Signal Classification

Abstract

The frequency band method is popular in signal processing; this method separates EEG signals into five bands of frequency. Besides the frequency band, the recent research show PCA method gives a good result to classify digits number from EEG signal. Even PCA give a good accuracy to classify digit number from EEG signal, but there are no research shows which one yielded better accuracy between PCA and frequency band to classify digit number from EEG signals. This paper presents the comparison between those methods using secondary data from MindBigData (MDB). The result shows that the frequency band and PCA achieve 9% and 12,5% on average accuracy with the EPOC dataset. The paired Wilcoxon test produces a significant difference in accuracy between methods in the digit classification problem. Experiment with Muse dataset provides 31% accuracy with frequency band method and 24,8% with PCA method. The result is competitive compared to other experiments to classify digit numbers from EEG signals. In conclusion, there is no winner between the two methods since no method fits both datasets used in this research.

Comparative Analysis of SVM and CNN for Pneumonia Detection in Chest X-Ray

“…Complex medical image recognition can be supported by dimensionality reduction methods such as PCA. The increase in image classification performance due to using PCA and machine learning is supported by research results [15]- [18]. The success of image detection is also supported by good feature extraction in medical images; in this case, the wavelet method is used to overcome this.…”

“…PCA was a method used to transform variables that correlate with smaller quantities. In addition, PCA was used for several purposes, such as helping to find relationships between dimensions, helping in feature extraction or extracting information from data, and reducing large dimensions to smaller ones [18]. PCA was also often used to overcome feature duplication problems in data [37].…”