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

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

Método para detección de estados estacionarios: aplicación a unidades de generación eléctrica

La detección de ventanas o intervalos en los que un proceso continuo esté operando en un estado estacionario es útil para la monitorización a largo plazo y en especial cuando se tienen modelos de estado estacionario que están siendo usados para la optimización del proceso. En el presente trabajo se presenta un método, bajo el nombre de sigma – gamma, basado en ventanas deslizantes, que mejora significativamente los algoritmos existentes. Combina algoritmos basados en el análisis de la desviación estándar de las mediciones con el método de las medias móviles y puede aplicarse no sólo a mediciones contaminadas con ruido blanco; sino también sobre series temporales afectadas por ruido coloreado. Se evalúa su desempeño comparándolo con dos de los métodos más recientes. Las pruebas indican que para los diferentes niveles y tipos de ruido analizados el método propuesto ofrece una reducción estadísticamente significativa de los  errores de Tipo I y de Tipo II.  Las series temporales que sirven de base a los experimentos de evaluación están relacionadas con los tipos de respuestas esenciales bajo las que operan las unidades de generación eléctrica

A complex two-dimensional first integral of magnetohydrodynamics equations

<p>purpose of this article is to study some question related to the complex twodimensional</p> <p>first integral equations of magnetohydrodynamics (FI-MHD). Most of the</p> <p>questions that we investigated are related to the transformations of differential magnetohydrodynamics</p> <p>operators from the real plane (R) to the complex plane (C). A new type</p> <p>of complete set of field equations appears: the first integral complex MHD equations. We</p> <p>also calculated a special case of complex solution for these FI-CMHD equations. In this</p> <p>family, with many members of coupling solutions the magnetic field appears with the</p> <p>same structure as the velocity field.</p

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