Search results for "error"
showing 10 items of 1643 documents
Online detection and removal of eye blink artifacts from electroencephalogram
2021
Abstract The most prominent type of artifact contaminating electroencephalogram (EEG) signals are the eye blink (EB) artifacts, which could potentially lead to misinterpretation of the EEG signal. Online identification and elimination of eye blink artifacts are crucial in applications such a Brain-Computer Interfaces (BCI), neurofeedback, and epilepsy diagnosis. In this paper, algorithms that combine unsupervised eye blink artifact detection (eADA) with modified Empirical Mode Decomposition (FastEMD) and Canonical Correlation Analysis (CCA) are proposed, i.e., FastEMD-CCA2 and FastCCA, to automatically identify eye blink artifacts and remove them in an online setting. The average accuracy, …
Automated and Online Eye Blink Artifact Removal from Electroencephalogram
2019
Eyeblink artifacts often contaminates electroencephalogram (EEG) signals, which could potentially confound EEG's interpretation. A lot offline methods are available to remove this artifact, but an online solution is required to remove eyeblink artifacts in near real time for EEG signal to be beneficial in applications such as brain computer interface, (BCI). In this work, approaches that combines unsupervised eyeblink artifact detection with Empirical Mode Decomposition (EMD) and Canonical Correlation Analysis (CCA) are proposed to automatically identify eyeblink artifacts and remove them in an online setting. The proposed approaches are analysed and evaluated in terms of artifact removal a…
Unsupervised Eye Blink Artifact Identification in Electroencephalogram
2018
International audience; The most prominent type of artifact contaminating electroencephalogram (EEG) signals is the eye blink (EB) artifact. Hence, EB artifact detection is one of the most crucial pre-processing step in EEG signal processing before this artifact can be removed. In this work, an approach that identifies EB artifacts without human supervision and automated varying threshold setting is proposed and evaluated. The algorithm functions on the basis of correlation between two EEG electrodes, Fp1 and Fp2, followed by EB artifact threshold determination utilizing the amplitude displacement from the mean. The proposed approach is validated and evaluated in terms of accuracy and error…
Morphology-based measurement of activation time in human atrial fibrillation
2003
The measurement of the activation time is crucial to allow the correct automatic analysis and classification of intracardiac electrograms recorded in the human atria during atrial fibrillation (AF). This study proposes a method which accounts for the morphology of bipolar signals. After ventricular artifact removal and activation wave recognition, the fiducial point of the activation wave was set at its local barycentre (LB). The method was tested on a set of 30 AF bipolar recordings of increasing complexity class; its performance was compared with that of the traditional methods of maximum peak (MP) or maximum slope (MS) estimation, taking the manual measurements performed by an expert car…
An Artificial Neural Network for 3D Localization of Brainstem Functional Lesions
2002
The human brainstem is a highly complex structure where even small lesions can give rise to a variety of symptoms and signs. Localizing the area of dysfunction within the brainstem is often a difficult task.To make localization easier, we have developed a neural net system, which uses 72 clinical and neurophysiological data inputs and displays it (using 5268 voxels) on a three-dimensional model of the human brainstem. The net was trained by means of a back-propagation algorithm, over a pool of 580 example-cases. Assessed on 200 test-cases, the net correctly localized 83.6% of the target voxels; furthermore the net correctly localized the lesion in 31/37 patients. Because our computer-assist…
Classical Training Methods
2006
This chapter reviews classical training methods for multilayer neural networks. These methods are widely used for classification and function modelling tasks. Nevertheless, they show a number of flaws or drawbacks that should be addressed in the development of such systems. They work by searching the minimum of an error function which defines the optimal behaviour of the neural network. Different standard problems are used to show the capabilities of these models; in particular, we have benchmarked the algorithms in a nonlinear classification problem and in three function modelling problems.
CostNet: An End-to-End Framework for Goal-Directed Reinforcement Learning
2020
Reinforcement Learning (RL) is a general framework concerned with an agent that seeks to maximize rewards in an environment. The learning typically happens through trial and error using explorative methods, such as \(\epsilon \)-greedy. There are two approaches, model-based and model-free reinforcement learning, that show concrete results in several disciplines. Model-based RL learns a model of the environment for learning the policy while model-free approaches are fully explorative and exploitative without considering the underlying environment dynamics. Model-free RL works conceptually well in simulated environments, and empirical evidence suggests that trial and error lead to a near-opti…
Recent advances in machine learning for maximal oxygen uptake (VO2 max) prediction : A review
2022
Maximal oxygen uptake (VO2 max) is the maximum amount of oxygen attainable by a person during exercise. VO2 max is used in different domains including sports and medical sciences and is usually measured during an incremental treadmill or cycle ergometer test. The drawback of directly measuring VO2 max using the maximal test is that it is expensive and requires a fixed and controlled protocol. During the last decade, various machine learning models have been developed for VO2 max prediction and numerous studies have attempted to predict VO2 max using data from submaximal and non-exercise tests. This article gives an overview of the machine learning models developed over the past five years (…
Direct intervention through biofeedback, to change disheartening conducts in a soccer goalkeeper
2017
El objetivo de este trabajo es describir una intervención, que buscaba modificar el comportamiento de un portero en relación con los errores que cometía, pues tras recibir un gol, se desanimaba y no dejaba de centrarse en el error cometido. Esta atención en el error cometido le hacía tardar de volver a tener un comportamiento adecuado para cumplir su función en el juego. Esta intervención se realizó mediante biofeedback, técnicas de control emocional y técnicas para el manejo de la atención y la concentración. Dado que es una intervención sobre un portero de 18 años de fútbol once, solicitada por el entrenador, se utilizó un estudio de caso único. De acuerdo con los resultados descriptivos …