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hi everyone todayamp;#39;s lecture covers eg signal processing techniques one of the critical steps in the design of brain-computer interface or BCI applications based on electroencephalography is to process and analyze such signals in real time in order to identify the mental state of the user the signals are noisy non-stationary complex and of high dimensionality therefore mental state recognition from EEG signals requires a specific signal processing in machine learning tools once you have acquired the signal today weamp;#39;re going to learn about how to do Iggyamp;#39;s signal processing and the kind of algorithms to use to do that the first step is called feature struction describing the EEG signals by a few relevant values called features such features should capture the information embedded into each EG signals that is relevant to describe the mental state to identify while rekt objecting the noise and other non relevant information the second step denoted has classification
