This work presents the use of swarm intelligence algorithms as a reliable method for the optimization of electroencephalogram signals for the improvement of the performance of the brain interfaces based on stable states visual events. The preprocessing of brain signals for the extraction of characteristics and the detection of events is of paramount importance for the improvement of brain interfaces. The proposed ant colony optimization algorithm presents an improvement in obtaining the key features of the signals and the detection of events based on visual stimuli. As a reference model, we used the Independent Component Analysis method, which has been used in recent research for the removal of nonrelevant and detection of relevant data from the brain’s electrical signals and also allows the collection of information in response to a stimulus and separates the signals that were generated independently in certain zones of the brain.

Dealing with electromyography (EMG) signals is often not simple. The nature of these signals is nonstationary, noisy, and high dimensional. These EMG characteristics make their predictability even more challenging. Cross recurrence plots (CRPs) have demonstrated in many works their capability of detecting very subtle patterns in signals often buried in a noisy environment. In this contribution, fifty subjects performed ten different hand movements with each hand with the aid of electrodes placed in each arm. Furthermore, the nonlinear features of each subject’s signals using cross recurrence quantification analysis (CRQA) have been performed. Also, a novel methodology is proposed using CRQA as the mainstream technique to detect and classify each of the movements presented in this study. Additional tools were presented to determine to which extent this proposed methodology is able to avoid false classifications, thus demonstrating that this methodology is feasible to classify surface EMG (SEMG) signals with good accuracy, sensitivity, and specificity. Lastly, the results were compared with traditional machine learning methods, and the advantages of using the proposed methodology above such methods are highlighted.

In this research a fully sensorized cooperative robot system for manipulation of needles is presented. The setup consists of a DLR/KUKA Light Weight Robot III especially designed for safe human/robot interaction, a FD-CT robot-driven angiographic C-arm system, and a navigation camera. Also, new control strategies for robot manipulation in the clinical environment are introduced. A method for fast calibration of the involved components and the preliminary accuracy tests of the whole possible errors chain are presented. Calibration of the robot with the navigation system has a residual error of 0.81 mm (rms) with a standard deviation of ±0.41 mm. The accuracy of the robotic system while targeting fixed points at different positions within the workspace is of 1.2 mm (rms) with a standard deviation of ±0.4 mm. After calibration, and due to close loop control, the absolute positioning accuracy was reduced to the navigation camera accuracy which is of 0.35 mm (rms). The implemented control allows the robot to compensate for small patient movements.

A set of electroencephalogram (EEG) data from 29 subjects obtained from a study, in which the subjects performed a set of tests based on visual stimuli and motor images of the hands is presented. Three types of data are provided in this article: (1) Signals based on visual events (VEP), (2) signals based on steady state visual events (SSVEP) and (3) signals based upon Motor Imagery (MI). Several research projects have used this data to test the detection of visual stimuli, classification and selection of characteristics of brain signals, EEG preprocessing and for optimization processes based on heuristic algorithms and algorithms based upon collective animal intelligence. The data was acquired using an Emotiv Epoc + portable EEG with 14 data channels and two reference channels.