P300 detection of brain signals using a combination of wavelet transform techniques

Brain signals known as electroencephalogram (EEG) carry the huge amount of information which is related to nerves activity sending the orders through the brain. The characteristics of brain signals such as transiency and low voltage of it, make them so complicated in term of signal processing. One of the most useful components of EEG is the event related potentials (ERP). P300 is the most robust and studied ERP among them which is appears in low frequency by applying desired stimuli with the latency of about 300 ms after stimuli. Detection of this component is the main challenge of many diagnostics (such as epilepsy) and research applications such as Brain Computer Interface (BCI) and Guilty Knowledge Test (GKT). Now detection of P300 is possible by using large number of channels and repeating the trial for participant. Objectives in this research are reduction of recording EEG channels, and achieving high accuracy in single trial P300 detection; selecting better P300 features and reducing the complexity of classifier, which is a need for real time in online applications. In this research the BCI competition data-set has been processed through 5 optimized detection methods. Wavelet transform (WT), student’s two-sample t-statistic (T-Test) and support vector machines (SVM) used in designing the algorithms. By using three level of channel reduction, three subgroups of channels with the number of 17, 9, and 5 have been chosen based on their ability in P300 pattern recognition. By implementing these optimized methods, high accuracy in single trial P300 detection is achieved for small subgroups of channels. By reduction of recording EEG channels in the single trial based algorithms, the processing time of P300 detection decrease dramatically. The results of all 5 methods were so encouraging in term of the tradeoff between accuracy, processing time, and number of channels. The best result (98%) is achieved via combination of Discrete Wavelet Transform (DWT) and Continuous Wavelet Transform (CWT) for feature extraction, T-test for feature selection and SVM for classification by using only five EEG channels. This research is proving the power of combination of discrete and continuous wavelet transform for achieving high accuracy in single trial detection and visualization of P300. Meanwhile the new approaches in channels selection methods help the algorithms for convenient online usage.

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