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%.

A comparative study of motor imagery based BCI classifiers on EEG and iEEG data There are many stateoftheart Brain Computer Interface (BCI) classification algorithms designed to perform well when applied to signals acquired using electroencephalography (EEG). EEG has the advantage of being noninvasive in nature, easy to use, and effective in capturing signals in the mu (713 Hz) and beta (1330 Hz) bands during motor imagery tasks. However, EEG recordings are more susceptible to movement artifacts and capture a lower frequency of neural activity when compared with invasive techniques such as electrocorticography (ECoG) or intracranial EEG (iEEG). In this paper, we analyze the performance of four different EEG motor imagery classification algorithms (both classical machine learning methods and deep learningbased methods) on a twohand motor imagery task using both EEG and iEEG data sets. Using various feature visualization techniques, we provide insight into why deep learningbased classifiers designed to learn features endtoend may perform better than the classical machine learningbased models. We also showed on average iEEGbased motor imagery BCIs, using our iEEG data set, do not perform as well as EEGbased BCIs. This work provides a starting point for the implementation of BCI applications using iEEG data.