Discrete Wavelet transform and ANFIS classifier for Brain-Machine Interface based on EEG
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In this paper, an on-line Brain-Machine Interface (BMI) based on Electroencephalography (EEG) that closes and opens a robotic hand when eyes are closed and open, respectively, is presented. This BMI is based on the measurement of the EEG bipolar connection: 01-P3. Moreover, since it is considered to be very important for some BMI biomedical applications a fast processing time and feature classification of the EEG signal, the authors propose a novel algorithm for on-line DWT processing of the EEG signal that, along with the feature classifier, have an average processing time (APT) of 37.9 ms. An Adaptive Neuro-Fuzzy Inference System (ANFIS) was used as the feature classifier obtaining an on-line average classification accuracy (ACA) of 96.0%; after an off-line ANFIS training. The average and the maximum value of the last two calculated level 4 detailed coefficients (cD4), derived from Wavelet's decomposition, were used as the inputs of the ANFIS classifier. The detailed coefficient cD4 was selected due to the fact that this coefficient isolates the EEG alpha wave(7.1914.4 Hz), which presents significant changes on the bipolar connection O1-P3 whenever a subject closes and opens its eyes. The output of the ANFIS classifier is the input voltage of a microcontroller, which generates a Pulse-Width Modulation (PWM) signal that controls the movement of the robotic hand. © 2013 IEEE.