Quaternions can be used as an alternative to model the fundamental patterns of electroencephalographic (EEG) signals in the time domain. Thus, this article presents a new quaternion-based technique known as quaternion-based signal analysis (QSA) to represent EEG signals obtained using a brain-computer interface (BCI) device to detect and interpret cognitive activity. This quaternion-based signal analysis technique can extract features to represent brain activity related to motor imagery accurately in various mental states. Experimental tests in which users where shown visual graphical cues related to left and right movements were used to collect BCI-recorded signals. These signals were then classified using decision trees (DT), support vector machine (SVM) and k-nearest neighbor (KNN) techniques. The quantitative analysis of the classifiers demonstrates that this technique can be used as an alternative in the EEG-signal modeling phase to identify mental states.

A bioinspired locomotion system for a quadruped robot is presented. Locomotion is achieved by a spiking neural network (SNN) that acts as a Central Pattern Generator (CPG) producing different locomotion patterns represented by their raster plots. To generate these patterns, the SNN is configured with specific parameters (synaptic weights and topologies), which were estimated by a metaheuristic method based on Christiansen Grammar Evolution (CGE). The system has been implemented and validated on two robot platforms; firstly, we tested our system on a quadruped robot and, secondly, on a hexapod one. In this last one, we simulated the case where two legs of the hexapod were amputated and its locomotion mechanism has been changed. For the quadruped robot, the control is performed by the spiking neural network implemented on an Arduino board with 35% of resource usage. In the hexapod robot, we used Spartan 6 FPGA board with only 3% of resource usage. Numerical results show the effectiveness of the proposed system in both cases.

Brain-computer interfaces(BCI) are a mechanism to record the electrical signals of the brain and translate them into commands to operate an output device like a robotic system. This article presents the development of a real-time locomotion system of a hexapod robot with bio-inspired movement dynamics inspired in the stick insect and tele-operated by cognitive activities of motor imagination. Brain signals are acquired using only four electrodes from a BCI device and sent to computer equipment for processing and classification by the iQSA method based on quaternion algebra. A structure consisting of three main stages are proposed: (1) signal acquisition, (2) data analysis and processing by the iQSA method, and (3) bio-inspired locomotion system using a Spiking Neural Network (SNN) with twelve neurons. An off-line training stage was carried out with data from 120 users to create the necessary decision rules for the iQSA method, obtaining an average performance of 97.72%. Finally, the experiment was implemented in realtime to evaluate the performance of the entire system. The recognition rate to achieve the corresponding gait pattern is greater than 90% for BCI, and the time delay is approximately from 1 to 1.5 seconds. The results show that all the subjects could generate their desired mental activities, and the robotic system could replicate the gait pattern in line with a slight delay. INDEX TERMS Bio-inspired robot, Brain-Computer Interface (BCI), electroencephalography, hexapod robot, iQSA method, motor imagery, spiking neural network, central pattern generator.

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This paper presents a method to design Spiking Central Pattern Generators (SCPGs) to achieve locomotion at different frequencies on legged robots. It is validated through embedding its designs into a Field-Programmable Gate Array (FPGA) and implemented on a real hexapod robot. The SCPGs are automatically designed by means of a Christiansen Grammar Evolution (CGE)-based methodology. The CGE performs a solution for the configuration (synaptic weights and connections) for each neuron in the SCPG. This is carried out through the indirect representation of candidate solutions that evolve to replicate a specific spike train according to a locomotion pattern (gait) by measuring the similarity between the spike trains and the SPIKE distance to lead the search to a correct configuration. By using this evolutionary approach, several SCPG design specifications can be explicitly added into the SPIKE distance-based fitness function, such as looking for Spiking Neural Networks (SNNs) with minimal connectivity or a Central Pattern Generator (CPG) able to generate different locomotion gaits only by changing the initial input stimuli. The SCPG designs have been successfully implemented on a Spartan 6 FPGA board and a real time validation on a 12 Degrees Of Freedom (DOFs) hexapod robot is presented.