Multiclass Classification of Visual Electroencephalogram Based on Channel Selection, Minimum Norm Estimation Algorithm, and Deep Network Architectures.

This work addresses the challenge of classifying multiclass visual EEG signals into 40 classes for brain-computer interface applications using deep learning architectures. The visual multiclass classification approach offers BCI applications a significant advantage since it allows the supervision of more than one BCI interaction, considering that each class label supervises a BCI task. However, because of the nonlinearity and nonstationarity of EEG signals, using multiclass classification based on EEG features remains a significant challenge for BCI systems. In the present work, mutual information-based discriminant channel selection and minimum-norm estimate algorithms were implemented to select discriminant channels and enhance the EEG data. Hence, deep EEGNet and convolutional recurrent neural networks were separately implemented to classify the EEG data for image visualization into 40 labels. Using the k-fold cross-validation approach, average classification accuracies of 94.8% and 89.8% were obtained by implementing the aforementioned network architectures. The satisfactory results obtained with this method offer a new implementation opportunity for multitask embedded BCI applications utilizing a reduced number of both channels (<50%) and network parameters (<110 K).

Multi-subject classification of Motor Imagery EEG signals using transfer learning in neural networks.

Brain-Computer Interfaces are new technologies with a fast development due to their possible usages, which still require overcoming some challenges to be readily usable. The paradigm of motor imagery is among the ones in these types of systems where the pipeline is tuned to work with only one person as it fails to classify the signals of a different person. Deep Learning methods have been gaining attention for tasks involving high-dimensional unstructured data, like EEG signals, but fail to generalize when trained on small datasets. In this work, to acquire a benchmark, we evaluate the performance of several classifiers while decoding signals from a new subject using a leave-one-out approach. Then we test the classifiers on the previous experiment and a method based on transfer learning in neural networks to classify the signals of multiple persons at a time. The resulting neural network classifier achieves a classification accuracy of 73% on the evaluation sessions of four subjects at a time and 74% on three at a time on the BCI competition IV 2a dataset.

PD Control Compensation Based on a Cascade Neural Network Applied to a Robot Manipulator.

A Proportional Integral Derivative (PID) controller is commonly used to carry out tasks like position tracking in the industrial robot manipulator controller; however, over time, the PID integral gain generates degradation within the controller, which then produces reduced stability and bandwidth. A proportional derivative (PD) controller has been proposed to deal with the increase in integral gain but is limited if gravity is not compensated for. In practice, the dynamic system non-linearities frequently are unknown or hard to obtain. Adaptive controllers are online schemes that are used to deal with systems that present non-linear and uncertainties dynamics. Adaptive controller use measured data of system trajectory in order to learn and compensate the uncertainties and external disturbances. However, these techniques can adopt more efficient learning methods in order to improve their performance. In this work, a nominal control law is used to achieve a sub-optimal performance, and a scheme based on a cascade neural network is implemented to act as a non-linear compensation whose task is to improve upon the performance of the nominal controller. The main contributions of this work are neural compensation based on a cascade neural networks and the function to update the weights of neural network used. The algorithm is implemented using radial basis function neural networks and a recompense function that leads longer traces for an identification problem. A two-degree-of-freedom robot manipulator is proposed to validate the proposed scheme and compare it with conventional PD control compensation.