P300 Detection for Brain Computer Interface
作者： Deepesh Kumar
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摘要: P300 based brain computer interface (BCI) sometimes called machine (BMI) is a way of direct communication between human and external device which provides an alternative link with outside world to the people who are unable communicate via conventional means because sever motor disability. wave event related potential evoked in process decision making can be generated using oddball paradigm. This thesis aims detect as accurate possible. To do that this study proposed discrete wavelet transforms (DWT) feature extraction method from each No-P300 EEG signal entire 64 channel. Principal component analysis (PCA) technique further applied for reduction dimension feature. Detection achieved support vector (SVM) artificial neural network (ANN) classifier. Experimental result shows SVM classifier yields better performance compared ANN.
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