Dendrite Ellipsoidal Neuron Trained by Stochastic Gradient Descent for Motor Imagery Classification

Dendrite ellipsoidal neurons are a novel and different alternative for classification tasks, giving competitive results compared with typical classification methods. Based on k-means++ algorithm, the network allows each dendrite to build a hyperellipsoidal in order to assign each incoming pattern to its respective C class. The main disadvantage of this training algorithm is the lack of accuracy in high dimensional datasets. In this research, we solved this problem by training the dendrite ellipsoidal neuron using stochastic gradient descent. Furthermore, electroencephalography data were acquired during two mental conditions (imaginary movements of the left and right hand) in order to test the new training algorithm. The proposed algorithm outperformed the accuracy acquired by a dendrite ellipsoidal neuron based on k-means++ obtaining 76.02% and 62.77%, respectively. Also, the algorithm was compared with multilayer perceptrons and support vector machines which are some of the most common classifiers used to detect motor-related information in brain signals. These achieved an accuracy of 72.38% and 65.81%, respectively.