Research On Brain-Computer Interface By Independent Component Analysis

In recent years, as a new human-computer interaction technology, Brain-Computer Interface (BCI) has been a hotspot in brain science and biomedical engineering. EEG-based BCI can provide a direct communication and control channel for sending messages and instructions from brain to external computers or other electronic devices. With the rapid development of cognitive brain science, neuroscience, computer science and signal processing, BCI is working up to a practical technology.Independent Component Analysis (ICA) is a multi-dimensional statistical analysis method developed in the mid 90's of the 20th century. Recently, the ICA-based BCI research has been attracted more and more researchers'attention. However, from the reported researches, it can be seen that ICA method is mainly used as a preprocessing step for EEG artifact removal and pattern enhancement so as to improve the communication accuracy between brain and computer. And in many cases, only batch ICA as off-line methods are often used for the analysis of BCI system.Considering the strong nonstationarity of EEG, the mainly research work of this thesis is about online ICA algorithm which can be used to analyze the nonstationary signals and its application on BCI. The main innovations of thesis are as follows:put forward a new idea of applying online ICA algorithm based on sliding window method to the real-time envelope extraction of signals. And successfully utilize the thought for the feature extraction and classification of BCI system. By using the method to classify a large number of real-world data, the experiment results prove that the proposed approach is effective for BCI research. Based on the innovations, some distinctive research works which have been finished are as follows:1. Based on studying the basic theory of ICA, the thesis focuses on the four ICA algorithms, including Herault-Jutten algorithm, Cichocki-Unbehauen algorithm, extended Infomax algorithm and the kurtosis-based ICA algorithm. Then the four block algorithms and their corresponding online algorithms updated with a new input sample are explored, and also the performances of block and online algorithms are comparatively analyzed in order to provide us an idea that is how to improve the current algorithms.2. Research on an improved online algorithm based on sliding window ICA. And under the two cases of non-time-varying and time-varying mixing model, the blind separation peformances of the improved online algorithm are respectively explored. Simulation results show that the improved approach not only partly has the stability of the batch algorithm, but also to some extent, it owns the adaptive tracking performance as the online algorithm updated with a new input sample. And for time-varying mixing model, the separation results by the method are also satisfied. In addition, the thesis proposed a novel thought of applying the online silding window ICA algorithm to the signal envelope detection. And simulation results illustrate that using dynamic mixing matrix coefficients got from improved algorithm can do well on the envelope extraction of signal.3. As the strong non-stationarity of EEG, the thesis proposes that using Hilbert-Huang transform (HHT) on BCI research. By analyzing the real-life data, the results show that utilizing the EMD method within HHT theory can successfully perform the pre-processing and feature extraction of SSVEP signal. Furthermore, during the HHT-based analysis of left and right hand motor imagery, a new idea is proposed that of emploring theμrhythm envelope as features to identify and classify the motor imagery EEG. Experimental results show that the proposed idea is feasible and can provide us a direction of applying online ICA algorithm on BCI.4. Propose to apply online ICA algorithm on BCI. Based on the classification results from large quantities of measured data, it can be identified that using online ICA algorithm to extract theμrhythm envelope can successfully classify the left and right hand motor imagery. To further verify the validity of the classification results, the thesis also uses the other four methods to identify and classify motor imagery EEG, such as AR and BP, AR and LDA, AR and SVM, and energy method based on second moment. By comparing of the results from defferent approachs, it shows that the online ICA algorithm can do well on the classification of motor imagery EEG. Moreover, it must be mentioned here that there is no training process when using online ICA algorithm to classify. Therefore, the method which has good classification peformence without training process will very helpful to simplify the BCI system.5. Research the detection of a wave by online ICA algorithm. Experimental results further confirm that using online ICA algorithms for envelope detection of EEG rhythm wave is effective.6. Proform by ourselves the experiment design and data collection successfully for three BCI systems, including left and right hand motor imagery based BCI, SSVEP-based BCI and a wave based BCI. Then a great deal of effective data is recorded and it will help us to further study on BCI.