Characterization of EEG Signals Using Wavelet Packet and Fuzzy Entropy in Motor Imagination Tasks

Artículos similares

A Semi-Automatic Wheelchair with Navigation Based on Virtual-Real 2D Grid Maps and EEG Signals

Acceso

Ba-Viet Ngo and Thanh-Hai Nguyen

A semi-automatic wheelchair allows disabled people to possibly control in an indoor environment with obstacles and targets. The paper proposes an EEG-based control system for the wheelchair based on a grid map designed to allow disabled people to reach a... ver más

Revista: Applied Sciences

Application of EEG Signals Integration to Proprietary Classification Algorithms in the Implementation of Mobile Robot Control with the Use of Motor Imagery Supported by EMG Measurements

Acceso

Dawid Pawus and Szczepan Paszkiel

This article is a continuation and extension of research on a new approach to the classification and recognition of EEG signals. Their goal is to control the mobile robot through mental commands, using a measuring set such as Emotiv Epoc Flex Gel. The he... ver más

Revista: Applied Sciences

Image-Based Learning Using Gradient Class Activation Maps for Enhanced Physiological Interpretability of Motor Imagery Skills

Acceso

Diego F. Collazos-Huertas, Andrés M. Álvarez-Meza and German Castellanos-Dominguez

Brain activity stimulated by the motor imagery paradigm (MI) is measured by Electroencephalography (EEG), which has several advantages to be implemented with the widely used Brain?Computer Interfaces (BCIs) technology. However, the substantial inter/intr... ver más

Revista: Applied Sciences

From 2D to VR Film: A Research on the Load of Different Cutting Rates Based on EEG Data Processing

Acceso

Feng Tian, Yan Zhang and Yingjie Li

Focusing on virtual reality (VR) and film cutting, this study compared and evaluated the effect of visual mode (2D, VR) and cutting rate (fast, medium, slow) on a load, to make an attempt for VR research to enter the cognitive field. This study uses a 2 ... ver más

Revista: Information

A Novel P300 Classification Algorithm Based on a Principal Component Analysis-Convolutional Neural Network

Acceso

Feng Li, Xiaoyu Li, Fei Wang, Dengyong Zhang, Yi Xia and Fan He

Aiming at enhancing the classification accuracy of P300 Electroencephalogram signals in a non-invasive brain?computer interface system, a novel P300 electroencephalogram signals classification algorithm is proposed which is based on improved convolutiona... ver más

Revista: Applied Sciences

Artículos similares

Revistas destacadas