This work presents the development of a brain computer interface as an alternative communication channel to be used in Robotics. It encompasses the implementation of an electroencephalograph (EEG), as well as the development of all computational methods and necessary techniques to identify mental activities. The developed brain computer interface (BCI) is applied to activate the movements of a 120lb mobile robot, associating four different mental activities to robot commands. The interface is based on EEG signal analyses, which extract features that can be classified as specific mental activities. First, a signal preprocessing is performed from the EEG data, filtering noise, using a spatial filter to increase the scalp signal resolution, and extracting relevant features. Then, different classifier models are proposed, evaluated and compared. At last, two implementations of the developed classifiers are proposed to improve the rate of successful commands to the mobile robot. In one of the implementations, a 91% average hit rate is obtained, with only 1.25% wrong commands after 400 attempts to control the mobile robot.

Motor imagery (MI) tasks of different body parts have been successfully decoded by conventional classifiers, such as LDA and SVM. On the other hand, decoding MI tasks within the same limb is a challenging problem with these classifiers; however, it would provide more options to control robotic devices. This work proposes to improve the hand MI tasks decoding within the same limb in a brain-computer interface using convolutional neural networks (CNNs); the CNN EEGNet, LDA, and SVM classifiers were evaluated for two (flexion/extension) and three (flexion/extension/grasping) MI tasks. Our approach is the first attempt to apply CNNs for solving this problem to our best knowledge. In addition, visual and electrotactile stimulation were included as BCI training reinforcement after the MI task similar to feedback sessions; then, they were compared. The EEGNet achieved maximum mean accuracies of 78.46% (± 12.50%) and 76.72% (± 11.67%) for two and three classes, respectively. Outperforming conventional classifiers with results around 60% and 48%, and similar works with results lower than 67% and 75%, respectively. Moreover, the electrical stimulation over the visual stimulus was not significant during the calibration session. The deep learning scheme enhanced the decoding of MI tasks within the same limb against the conventional framework.

In the aging society, the number of people suffering from vascular disorders is rapidly increasing and has become a social problem. The death rate due to stroke, which is the second leading cause of global mortality, has increased by 40% in the last two decades. Stroke can also cause paralysis. Of late, brain‐computer interfaces (BCIs) have been garnering attention in the rehabilitation field as assistive technology. A BCI for the motor rehabilitation of patients with paralysis promotes neural plasticity, when subjects perform motor imagery (MI). Feedback, such as visual and proprioceptive, influences brain rhythm modulation to contribute to MI learning and motor function restoration. Also, virtual reality (VR) can provide powerful graphical options to enhance feedback visualization. This work aimed to improve immersive VR‐BCI based on hand MI, using visual‐electrotactile stimulation feedback instead of visual feedback. The MI tasks include grasping, flexion/extension, and their random combination. Moreover, the subjects answered a system perception questionnaire after the experiments. The proposed system was evaluated with twenty able‐bodied subjects. Visual‐electrotactile feedback improved the mean classification accuracy for the grasping (93.00% ± 3.50%) and flexion/extension (95.00% ± 5.27%) MI tasks. Additionally, the subjects achieved an acceptable mean classification accuracy (maximum of 86.5% ± 5.80%) for the random MI task, which required more concentration. The proprioceptive feedback maintained lower mean power spectral density in all channels and higher attention levels than those of visual feedback during the test trials for the grasping and flexion/extension MI tasks. Also, this feedback generated greater relative power in the μ‐band for the premotor cortex, which indicated better MI preparation. Thus, electrotactile stimulation along with visual feedback enhanced the immersive VR‐BCI classification accuracy by 5.5% and 4.5% for the grasping and flexion/extension MI tasks, respectively, retained the subject’s attention, and eased MI better than visual feedback alone.

Adaptive neuro fuzzy Inference systems (ANFIS) has been applied in brain computer interfaces (BcI) in different ways such as mapping of P300 or fusing information from EEG channels and it has reached high classification accuracy. This work proposes a combination of ANFIS classifiers by voting for a single-trial detection of a P300 wave in a BCI, using four channels; five healthy subjects and three post-stroke patients have participated in this study, each participant performs 4 BCI sessions, crossvalidation is applied to evaluate the classifier performance. The results of average accuracy were greater than 75% for all subjects, similar results were gotten for healthy subjects and post-stroke patients, but the better classifiers for each subject have achieved accuracies greater than 80%.

Visual feedback in a brain computer interface (BCI) influences significantly in its performance; but when this BCI will be applied in a rehabilitation therapy for post stroke patients, it will be a determining factor to enhance the neuroplasticity process. Many studies have demonstrated the efficiency of virtual reality (VR) in a BCI, motor cortex increases its activation levels due to be an immersive environment for the subject; specifically, a BCI based on motor imagery (MI) with VR feedback has positive effects in patients. This work proposes to apply a BCI with VR to support an upper limb rehabilitation therapy for post stroke patients, but it has been tested with eighteen healthy subjects, they performed MI tasks of flexion and extension of their arms, then they could see a virtual arm in 3D performing the same requested movement. Comparison of power spectral density (PSD) estimation is done during MI tasks in training and online test sessions, and feedback in online test sessions; and it is possible to observe the brain activity in alpha and beta bands remained during online sessions, topographic map shows activated premotor and motor cortex areas, it is significant evidence for the application of this system to motor disable patients.

Brain Machine Interfaces (BMI) have been developed as an alternative way to decode brain signals into control commands and communication devices. A typical BMI uses a computer to process EEG signals; however, current embedded PCs have enough computational resources for fully embedded BMI systems. In this work, the performance of the Odroid-xu4 embedded PC is evaluated as a processing and control device for BMI based on a 2-class motor imagery paradigm. Results show the best accuracy (82.1%) using SVM classifier and minimal processing times (0.11s) on the embedded device, which allows the development of a portable, low cost and trustworthy system.