清晨好,您是今天最早来到科研通的研友!由于当前在线用户较少,发布求助请尽量完整的填写文献信息,科研通机器人24小时在线,伴您科研之路漫漫前行!

A study on CNN image classification of EEG signals represented in 2D and 3D

人工智能 计算机科学 模式识别(心理学) 预处理器 卷积神经网络 脑电图 体素 降维 特征提取 心理学 精神科
作者
Jordan J. Bird,Diego R. Faria,Luis J. Manso,Pedro P. S. Ayrosa,Anikó Ekárt
出处
期刊:Journal of Neural Engineering [IOP Publishing]
卷期号:18 (2): 026005-026005 被引量:26
标识
DOI:10.1088/1741-2552/abda0c
摘要

Abstract Objective. The novelty of this study consists of the exploration of multiple new approaches of data pre-processing of brainwave signals, wherein statistical features are extracted and then formatted as visual images based on the order in which dimensionality reduction algorithms select them. This data is then treated as visual input for 2D and 3D convolutional neural networks (CNNs) which then further extract ‘features of features’. Approach. Statistical features derived from three electroencephalography (EEG) datasets are presented in visual space and processed in 2D and 3D space as pixels and voxels respectively. Three datasets are benchmarked, mental attention states and emotional valences from the four TP9, AF7, AF8 and TP10 10–20 electrodes and an eye state data from 64 electrodes. Seven hundred twenty-nine features are selected through three methods of selection in order to form 27 × 27 images and 9 × 9 × 9 cubes from the same datasets. CNNs engineered for the 2D and 3D preprocessing representations learn to convolve useful graphical features from the data. Main results. A 70/30 split method shows that the strongest methods for classification accuracy of feature selection are One Rule for attention state and Relative Entropy for emotional state both in 2D. In the eye state dataset 3D space is best, selected by Symmetrical Uncertainty. Finally, 10-fold cross validation is used to train best topologies. Final best 10-fold results are 97.03% for attention state (2D CNN), 98.4% for Emotional State (3D CNN), and 97.96% for Eye State (3D CNN). Significance. The findings of the framework presented by this work show that CNNs can successfully convolve useful features from a set of pre-computed statistical temporal features from raw EEG waves. The high performance of K-fold validated algorithms argue that the features learnt by the CNNs hold useful knowledge for classification in addition to the pre-computed features.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
2秒前
lht完成签到 ,获得积分20
24秒前
愤怒的豆腐人完成签到,获得积分10
27秒前
rotator完成签到 ,获得积分10
29秒前
xiaoyi完成签到 ,获得积分10
31秒前
31秒前
曦月完成签到 ,获得积分10
44秒前
doreen完成签到 ,获得积分10
44秒前
xiazhq完成签到,获得积分10
48秒前
曾建完成签到 ,获得积分10
1分钟前
GankhuyagJavzan完成签到,获得积分10
1分钟前
Sylvia_J完成签到 ,获得积分10
1分钟前
缺粥完成签到 ,获得积分10
1分钟前
1分钟前
xiaofenzi完成签到 ,获得积分10
1分钟前
1分钟前
ldjldj_2004完成签到 ,获得积分10
2分钟前
小新小新完成签到 ,获得积分10
2分钟前
雪妮完成签到 ,获得积分10
2分钟前
2分钟前
化工牛马发布了新的文献求助10
2分钟前
2分钟前
莫离完成签到 ,获得积分10
3分钟前
星辰大海应助三井库里采纳,获得50
3分钟前
3分钟前
化工牛马发布了新的文献求助10
3分钟前
elisa828完成签到,获得积分10
3分钟前
慕青应助科研通管家采纳,获得10
3分钟前
3分钟前
萧水白完成签到,获得积分0
3分钟前
lilaccalla完成签到 ,获得积分10
4分钟前
浚稚完成签到 ,获得积分10
4分钟前
Antonio完成签到 ,获得积分10
4分钟前
SJD完成签到,获得积分0
4分钟前
z1y1p1完成签到,获得积分10
5分钟前
归尘应助Benhnhk21采纳,获得30
5分钟前
直率的笑翠完成签到 ,获得积分10
5分钟前
5分钟前
未完成完成签到,获得积分10
5分钟前
阿童木完成签到,获得积分10
5分钟前
高分求助中
Production Logging: Theoretical and Interpretive Elements 2500
Healthcare Finance: Modern Financial Analysis for Accelerating Biomedical Innovation 2000
Applications of Emerging Nanomaterials and Nanotechnology 1111
Agaricales of New Zealand 1: Pluteaceae - Entolomataceae 1040
Les Mantodea de Guyane Insecta, Polyneoptera 1000
Neuromuscular and Electrodiagnostic Medicine Board Review 700
지식생태학: 생태학, 죽은 지식을 깨우다 600
热门求助领域 (近24小时)
化学 医学 材料科学 生物 工程类 有机化学 生物化学 纳米技术 内科学 物理 化学工程 计算机科学 复合材料 基因 遗传学 物理化学 催化作用 细胞生物学 免疫学 电极
热门帖子
关注 科研通微信公众号,转发送积分 3466837
求助须知:如何正确求助?哪些是违规求助? 3059674
关于积分的说明 9067359
捐赠科研通 2750142
什么是DOI,文献DOI怎么找? 1509066
科研通“疑难数据库(出版商)”最低求助积分说明 697126
邀请新用户注册赠送积分活动 696913