Abstract
In recent years, Electroencephalography (EEG) and Brain–computer interfaces (BCIs) have been widely discussed and become a popular research topic. The BCIs are innovative interfaces for translating the user’s intent into commands to control devices or software applications with recognized symbols. Via using BCI systems, it is capable to recognize the brain signals without the dependence of the normal output pathways of the peripheral nerves or muscles. To accomplish such complex tasks, a pattern recognition algorithm with high reliability is especially needed. Such algorithm basically consists of three parts: data pre-processing, dimensionality reduction, and classification model. In this thesis we proposed a pattern recognition method that used to identify intention of movement from the users. The method is composed of digital wavelet transform (DWT), principal component analysis (PCA), and minimum distance classifier. The proposed method is designed to recognize the subject’s intention of moving a cube. The subject is asked to perform 4 types of mental tasks related to movement, which are lift, neutral, push, and pull. With the proposed pattern recognition method and proper training, the EEG raw data recorded by an EEG headset can be precisely classified into corresponding command of movement with high recognition rate of 92.5%.