Motor imagery recognition and its cerebral mapping

This paper proposes to relate a Linear Discriminant Analysis Classification based system with Quality Similarity Clustering process and a Brain Mapping Technique in order to clarify the brain activity differences that could sustain the better classification rates between each of the Motor Imagery (MI - flex right arm, flex left arm, close right hand, close left hand). To achieve this goal the Eletroencephalogram (EEG) entropy was used as principal feature and a Principal Component Analyses (PCA) was also used. EEG signal, from each of the 8-channel transversal set up, was acquired from electrodes Fz, Cz, Pz and Oz to electrodes F3, F4, C3, C4, P3, P4, O1, and O2. Results showed that for each MI there is an specific topographic distribution of the cortex activation. A common point was the minor activation of the motor area. The principal areas were the frontal and the parietal ones. The clustering analysis showed, considering 20% of similarity, that the points which possibly contribute to motor imagery recognition came from those areas. Furthermore, the best results in the classification process were reached to distinguish between left arm and left hand with 89.74% and between arms and hands with 80.77%. Both of these scenes were possible using only the information provided by the frontal electrodes, and this agree with brain mapping. The activation of frontal and parietal lobes reflects the network involved in sensory-motor integration (posterior parietal), decision-making (prefrontal) and preparation of motor response (premotor). Our finding that the frontal electrodes were the most representative may be related to the fact that the premotor cortex is responsible for motor planning and thus, should be the most active area in distinguishing between different motor movements.