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A review of the state of the art of feature extraction of electroencephalographic signals

We present a review of the state of the art of the techniques and algorithms most used in the selection and detection of characteristics of electroencephalographic signals of people when consciously performing activities. These features are numeric parameters that describe the behavior of the signal and are the basis of patterns. In addition, previous experiences in the acquisition of electroencephalographic signals using the Epoc brain-computer interface manufactured by Emotiv are presented. First, some techniques used to eliminate artifacts (disturbances) present in the signal generated by blinking, strong breathing or other movements that contaminate the signal are presented. Later, the algorithms most frequently used in the processing of electroencephalographic signals are shown for the extraction of characteristics that describe the behavior of these patterns and that can be used to detect and recognize patterns in other signals. Finally, we present the lessons that we have acquired as a work team in the recording of electroencephalographic signals in order to be helpful for beginners.