Fourier Spectral Domain Functional Principal Component Analysis of EEG Signals Functional principal component analysis (FPCA) is a natural tool for investigating the functional pattern of functional data. Clustering functional data using FPCA can be an important area in machine learning for signal processing, in particular, for signals that contain a deterministic component. Also, FPCA can be useful for analysis of random signals if an appropriate transformation is applied. In this work, we propose a novel approach by extracting key features of EEG signals in the Fourier spectral domain using FPCA. By first transforming EEG signals into their Fourier power spectra, the functionality of signals is greatly enhanced. Due to this improvement, the application of FPCA becomes much more meaningful in signal feature extraction. Our study shows a great potential of using spectral domain FPCA as a feature extractor for processing EEG signals in both epilepsy diagnosis and epileptic seizure detection.

Improved Discrete Wavelet Analysis and Principal Component Analysis for EEG Signal Processing Electroencephalogram (EEG) has significant applications on medical diagnosis and Brain Computer Interface (BCI). But the main obstacle of analyzing EEG signal is various types of noises to get actual information. Extracting important features is a key issue in this study. This paper uses the BCI Competition IV 2b motion imagery data, in which we provide a review of various prior art to determine the motion imaginary MI mission. Using machine learning to identify two different movements in the EEG signal, the data from nine subjects were analyzed by principal component analysis (PCA) combined with discrete wavelet (DWT) packet analysis. The extracted DWT feature is input into the support vector machine (SVM) classifier, and the experimental results shows that this method is better than traditional methods with a classification accuracy rate of 86.7%.