Spiking Neural Networks applied to the classification of motor tasks in EEG signals.

Motivated by the recent progress of Spiking Neural Network (SNN) models in pattern recognition, we report on the development and evaluation of brain signal classifiers based on SNNs. The work shows the capabilities of this type of Spiking Neurons in the recognition of motor imagery tasks from EEG signals and compares their performance with other traditional classifiers commonly used in this application. This work includes two stages: the first stage consists of comparing the performance of the SNN models against some traditional neural network models.

Automatic path planning for a mobile robot among obstacles of arbitrary shape.

A novel and fast path planning method for a mobile robot (MR) among objects of arbitrary shape is described. It comprises two phases. During the first phase, the graph including all possible collision-free paths from a top view of the environment is first obtained.