Improvement of motor imagery electroencephalogram decoding by iterative weighted Sparse-Group Lasso

计算机科学 运动表象 模式识别(心理学) 人工智能 解码方法 判别式 特征选择 脑-机接口 特征(语言学) 支持向量机 聚类分析 机器学习 脑电图 算法 哲学 精神科 语言学 心理学
作者
Bin Liu,Fuwang Wang,Shiwei Wang,Junxiang Chen,Guilin Wen,Rongrong Fu
出处
期刊:Expert Systems With Applications [Elsevier BV]
卷期号:238: 122286-122286
标识
DOI:10.1016/j.eswa.2023.122286
摘要

Discriminative feature selection is vital for enhancing motor imagery decoding performance in electroencephalogram (EEG) signals. However, existing feature optimization methods have not sufficiently explored the intrinsic attribute distribution of features and their associations with the target class, which could result in spurious correlations between optimized features and class labels, yielding suboptimal performance. Therefore, this study proposed an iterative Weighted Sparse-Group Lasso (iWSGL) model for optimizing Common Spatial Pattern (CSP)-based high-dimensional features, thus further enhancing the decoding accuracy of motor imagery. Specifically, the affinity propagation (AP) clustering algorithm was utilized to adaptively partition the high-dimensional features into multiple groups based on the underlying relationships among them. To evaluate the significance of individual feature within each group and the overall significance of the groups themselves, a weight calculation method was proposed based on conditional entropy. With the weights and feature structural information, a weighted sparse regression model was devised within the iterative Sparse-Group Lasso (iSGL) framework to jointly optimize the CSP-based high-dimensional features. The performance of the proposed method was validated on three datasets using the support vector machine (SVM). The experimental results exhibited the exceptional superiority of the proposed method over the current CSP and its variants, demonstrating its remarkable performance. These findings imply that the proposed model can offer a novel optimization strategy for enhancing pattern recognition of brain intentions in Brain-Computer Interface (BCI) applications.
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
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

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
小芒果完成签到,获得积分0
1秒前
2秒前
杰克李李完成签到,获得积分10
3秒前
pakiorder完成签到,获得积分20
5秒前
无心的雅霜完成签到,获得积分10
5秒前
1122完成签到,获得积分10
6秒前
王磊完成签到,获得积分10
6秒前
顺心醉蝶完成签到 ,获得积分10
6秒前
量子星尘发布了新的文献求助10
6秒前
zhao完成签到 ,获得积分10
7秒前
yuncong323发布了新的文献求助10
7秒前
gfasdjsjdsjd发布了新的文献求助30
9秒前
pan完成签到,获得积分10
9秒前
10秒前
11秒前
魔幻的妖丽完成签到 ,获得积分10
13秒前
王小凡完成签到 ,获得积分10
13秒前
14秒前
开心薯片发布了新的文献求助10
15秒前
ZXW完成签到,获得积分10
16秒前
莉莉发布了新的文献求助10
16秒前
眼睛大的擎苍完成签到,获得积分10
18秒前
xr完成签到,获得积分10
19秒前
ZORO完成签到,获得积分10
19秒前
20秒前
临时演员完成签到,获得积分10
21秒前
ABCDE完成签到,获得积分10
21秒前
taotao完成签到,获得积分10
21秒前
ED应助莉莉采纳,获得10
22秒前
ED应助莉莉采纳,获得10
22秒前
扣扣登陆完成签到 ,获得积分10
22秒前
23秒前
白日焰火完成签到 ,获得积分10
23秒前
24秒前
24秒前
没事走两步完成签到,获得积分10
25秒前
狄百招发布了新的文献求助10
26秒前
nilou完成签到,获得积分10
27秒前
心好塞发布了新的文献求助10
28秒前
丘比特应助潇洒日记本采纳,获得10
28秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038235
求助须知:如何正确求助?哪些是违规求助? 3575992
关于积分的说明 11374009
捐赠科研通 3305760
什么是DOI,文献DOI怎么找? 1819276
邀请新用户注册赠送积分活动 892662
科研通“疑难数据库(出版商)”最低求助积分说明 815022