Deep Convolutional Neural Networks and Power Spectral Density Features for Motor Imagery Classification of EEG Signals

机译：脑电信号运动图像分类的深度卷积神经网络和功率谱密度特征

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摘要

A Brain-Computer Interface (BCI) is a communication and control system that attempts to provide real-time interaction between a user and a computer device, based on the brain electrical signals that are generated when user imagine specific movements or actions. For doing so, classification models are developed to identify the user movement intention according to specific signal features. This paper presents a classification model to BCI that is based on the processing of Electroencephalography (EEG) signals. The power spectral density (PSD) representation of EEG signals is used for training a deep Convolutional Neural Network (CNN) that is able to differentiate among four different movement intentions: left-hand movement, right-hand movement, feet movement, and tongue movement. Performance evaluation results reported a mean accuracy of 0.8797 ± 0.0296 for the well-known BCI Competition IV Dataset 2a, which outperform state-of-the-art approaches.

机译：脑机接口（BCI）是一种通信和控制系统，它试图根据用户想象特定运动或动作时所产生的脑电信号来提供用户与计算机设备之间的实时交互。为此，开发了分类模型以根据特定的信号特征识别用户的移动意图。本文提出了一种基于脑电图（EEG）信号处理的BCI分类模型。 EEG信号的功率谱密度（PSD）表示用于训练深层卷积神经网络（CNN），该神经网络能够区分四种不同的运动意图：左手运动，右手运动，脚运动和舌头运动。性能评估结果报告了著名的BCI Competition IV数据集2a的平均准确度为0.8797±0.0296，优于最新方法。

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《International conference on human-computer interaction;International conference on augmented cognition》|2018年|158-169|共12页
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A. F. Perez-Zapata; A. F. Cardona-Escobar; J. A. Jaramillo-Garzon; Gloria M. Diaz;
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Brain Computer Interface (BCI); Electroencephalography (EEG); Power Spectral Density (PSD); Deep learning; Convolutional Neural Network (CNN);

机译：脑计算机接口（BCI）;脑电图（EEG）;功率谱密度（PSD）;深度学习;卷积神经网络（CNN）;

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1. Convolutional neural network based features for motor imagery EEG signals classification in brain–computer interface system [J] . Samaneh Taheri, Mehdi Ezoji, Sayed Mahmoud Sakhaei SN Applied Sciences . 2020,第4期

机译：基于卷积神经网络的脑机接口系统中运动图像脑电信号分类的特征
2. Spatial-Frequency Feature Learning and Classification of Motor Imagery EEG Based on Deep Convolution Neural Network [J] . Minmin Miao, Wenjun Hu, Hongwei Yin, Computational and mathematical methods in medicine . 2020,第1期

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3. Automatic Classification of Motor Impairment Neural Disorders from EEG Signals Using Deep Convolutional Neural Networks [J] . Vrbancic Grega, Podgorelec Vili Elektronika ir Elektrotechnika . 2018,第4期

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4. Deep Convolutional Neural Networks and Power Spectral Density Features for Motor Imagery Classification of EEG Signals [C] . A. F. Perez-Zapata, A. F. Cardona-Escobar, J. A. Jaramillo-Garzon, International Conference on Augmented Cognition . 2018

机译：EEG信号电机图像分类的深度卷积神经网络和功率谱密度特征
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6. Spatial-Frequency Feature Learning and Classification of Motor Imagery EEG Based on Deep Convolution Neural Network [O] . Minmin Miao, Wenjun Hu, Hongwei Yin, 2020

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7. A Novel Simplified Convolutional Neural Network Classification Algorithm of Motor Imagery EEG Signals Based on Deep Learning [O] . Feng Li, Fan He, Fei Wang, 2020

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8. Keypoint Density-Based Region Proposal for Fine-Grained Object Detection and Classification Using Regions with Convolutional Neural Network Features. [R] . Turner, J. T., Gupta, K., Morris, B., 2015

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Deep Convolutional Neural Networks and Power Spectral Density Features for Motor Imagery Classification of EEG Signals

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