Shallow convolutional network excel for classifying motor imagery EEG in BCI applications

Many studies applying Brain-Computer Interfaces (BCIs) based on Motor Imagery (MI) tasks for rehabilitation have demonstrated the important role of detecting the Event-Related Desynchronization (ERD) to recognize the user’s motor intention. Nowadays, the development of MI-based BCI approaches without or with very few calibration stages session-by-session for different days or weeks is still an open and emergent scope. In this work, a new scheme is proposed by applying Convolutional Neural Networks (CNN) for MI classification, using an end-to-end Shallow architecture that contains two convolutional layers for temporal and spatial feature extraction. We hypothesize that a BCI designed for capturing event-related desynchronization/synchronization (ERD/ERS) at the CNN input, with an adequate network design, may enhance the MI classification with fewer calibration stages. The proposed system using the same architecture was tested on three public datasets through multiple experiments, including both subject-specific and non-subject-specific training. Comparable and also superior results with respect to the state-of-the-art were obtained. On subjects whose EEG data were never used in the training process, our scheme also achieved promising results with respect to existing non-subject-specific BCIs, which shows greater progress in facilitating clinical applications

Monte Carlo dropout for uncertainty estimation and motor imagery classification

Motor Imagery (MI)-based Brain–Computer Interfaces (BCIs) have been widely used as an alternative communication channel to patients with severe motor disabilities, achieving high classification accuracy through machine learning techniques. Recently, deep learning techniques have spotlighted the state-of-the-art of MI-based BCIs. These techniques still lack strategies to quantify predictive uncertainty and may produce overconfident predictions. In this work, methods to enhance the performance of existing MI-based BCIs are proposed in order to obtain a more reliable system for real application scenarios. First, the Monte Carlo dropout (MCD) method is proposed on MI deep neural models to improve classification and provide uncertainty estimation. This approach was implemented using Shallow Convolutional Neural Network (SCNN-MCD) and with an ensemble model (E-SCNN-MCD). As another contribution, to discriminate MI task predictions of high uncertainty, a threshold approach is introduced and tested for both SCNN-MCD and E-SCNN-MCD approaches. The BCI Competition IV Databases 2a and 2b were used to evaluate the proposed methods for both subject-specific and non-subject-specific strategies, obtaining encouraging results for MI recognition

Generación de trayectorias para el brazo robótico (ArmX)

In this paper the modeling and simulation of direct and inverse kinematics of a robot joint four degrees of freedom that clip as end effector to manipulate objects was made, and has defined its working surface in three dimensional space. Kinematic robot control trajectories for tracking point to point, coordinated and continuous to the end is performed. Dynamic control is performed using an action undocked on-off control. Free hardware platform Arduino, Arduino Mega2560 specifically and its development environment was used. It was also designed and implemented a shield plate type with additional electronics needed to control the manipulator. Finally, a graphical interface using as a tool the LabWindows / CVI 9.0 software from National Instruments, which allows the user to send from the PC the desired joint and Cartesian coordinate values, including the position of the clamp and planning was implemented tasks and types of trajectories.En el presente trabajo se realizó la modelación y simulación de la cinemática directa e inversa de un robot articular de cuatro grados de libertad que tiene pinza como efector final para manipular objetos, y tiene definida su superficie de trabajo en un espacio tridimensional. Se realiza el control cinemático del robot para el seguimiento de trayectorias punto a punto, coordinadas y continuas para el extremo. Se realiza el control dinámico desacoplado utilizando una acción de control on-off. Se empleó la plataforma de hardware libre Arduino, específicamente la placa Arduino Mega 2560 y su entorno de desarrollo. Además se diseñó e implementó una placa del tipo escudo con la electrónica adicional necesaria para el control del manipulador. Por último, se implementó una interfaz gráfica utilizando como herramienta el software LabWindows / CVI 9.0 de National Instruments, que permite al usuario enviar desde la PC los valores deseados en coordenadas articulares y cartesianas, incluyendo la posición de la pinza, así como la planificación de tareas y de tipos de trayectorias

Robust Motor Imagery Tasks Classification Approach Using Bayesian Neural Network

The development of Brain–Computer Interfaces based on Motor Imagery (MI) tasks is a relevant research topic worldwide. The design of accurate and reliable BCI systems remains a challenge, mainly in terms of increasing performance and usability. Classifiers based on Bayesian Neural Networks are proposed in this work by using the variational inference, aiming to analyze the uncertainty during the MI prediction. An adaptive threshold scheme is proposed here for MI classification with a reject option, and its performance on both datasets 2a and 2b from BCI Competition IV is compared with other approaches based on thresholds. The results using subject-specific and non-subject-specific training strategies are encouraging. From the uncertainty analysis, considerations for reducing computational cost are proposed for future work