Coerência, modelo oculto de Markov e Perceptron de multi-camadas em imagética motora

Abstract

Brain signals and the interpretation of their patterns provide a new modality of communication: the Brain Computer Interface (BCI). BCI can use scalp potential related movement imagination to activate drive devices, not depending on the brains normal output channels: peripheral nerves and muscles. Magnitude Squared Coherence has beenused to identify the event related potential in Electroencephalogram (EEG) signals. Moreover, techniques such as Hidden Markov Model (HMM) and Artificial Neural Network (ANN with Multilayer Perceptron MLP structure) have shown promising results in classification for BCI systems. Thus, this work aims to investigate classification using HMM and ANN using features from MSC in EEG signals, for the following events: spontaneous EEG; actual index finger movement; and imaginary movement of that finger. EEGs were recorded from three normal subjects from electrodes placed according to the International 10-20 System (1st record) and 10-10 System (2nd and 3rd record). EEG was divided into trials (M - 14 seconds each) synchronized with the event. Each trial wasdivided into six segments: spontaneous EEG; EEG during visualization of red LED (Light Emitting Diode) attention; EEG during visualization of yellow LED preparation for the event; EEG during the event; spontaneous EEG; and spontaneous EEG. MSC was calculated for 12 trials and afterwards, for the maximum trials existent in each electrode.In each segment the MSC was calculated for delta band (0.1 2.0 Hz), alpha band (8.0 13 Hz) and beta band (14 30 Hz), with M=12 trials and M = maximum number of trials. The frequency band that presented the highest MSC was used as observation in HMM and as input for RNA. The average accuracy rates in the classification using HMM for M = 12were 68.5 %, 66.5 % and 67.5 %, for subjects #1, #2 and #3, respectively. For maximum M, they were 73.0 %, 70.0 % and 56.5 %. When MLP was used for classification the results for 12 trials were 64.0 %, 75.5 % and 82.0 % and, using maximum M, the accuracy rates obtained were 79.5 %, 85.5 % and 88.5 %. These results showed that the MSC technique is an efficient tool for feature extraction in EEG recording during differentevents. With these features, it was possible to classify the EEG signals using HMM and RNA, the latter presenting the best performance in event classification.