A motor-imagery channel-selection method based on SVM-CCA-CS

运动表象 支持向量机 脑电图 计算机科学 模式识别(心理学) 频道(广播) 人工智能 脑-机接口 集合(抽象数据类型) 特征选择 选择(遗传算法) 语音识别 心理学 神经科学 电信 程序设计语言
作者
Qisong Wang,Tianao Cao,Dan Liu,Meiyan Zhang,Jingyang Lu,Ou Bai,Jinwei Sun
出处
期刊:Measurement Science and Technology [IOP Publishing]
卷期号:32 (3): 035701-035701 被引量:15
标识
DOI:10.1088/1361-6501/abc205
摘要

Abstract In electroencephalography, multi-channel electroencephalogram (EEG) signals are usually utilized to improve classification accuracy. However, a large set of EEG channels increases the computational complexity, reduces the real-time performance and causes wearability difficulties. Channel selection methods have been widely investigated to reduce the number of channels with an acceptable loss of accuracy for EEG-based motor-imagery recognition. In this paper, we present a novel algorithm, called Support Vector Machine-Canonical Correlation Analysis-Channel Selection (SVM-CCA-CS). First, the energy features of the wavelet packet subnodes of the motor-imagery EEG signals are extracted. Then the weights of feature groups are calculated as initial channel weights, based on the CCA algorithm. The initial channel weights are further adjusted, according to the contribution of each channel to the classification accuracy via SVM, and the top channels with larger weights are eventually selected. The results show that the average accuracy of all subjects can reach 80.03% by using the first 30 channels with the largest weights from among the total of 118 channels. For the right hand and foot motor-imagery tasks, the generally applicable optimal channels are mostly located in the left hemisphere. Our generally applicable channel observation of the whole brain cortex suggests contralateral control correspondence: for unilateral motor imagery, the optimal channels are concentrated in the contralateral hemisphere. This is consistent with the contralateral control of the body by the human brain: the majority of the human motor and sensory fibers tend to control the contralateral limbs and pass through the midline of the body. Our proposed method provides optimal acquisition and analysis of the positions of EEG signals in specific motor-imagery tasks.
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
orixero应助galioo3000采纳,获得10
刚刚
牛小牛发布了新的文献求助10
1秒前
Emma应助kiyo_v采纳,获得10
1秒前
1秒前
FashionBoy应助黑黑黑采纳,获得10
1秒前
Zhaojiaokeyan关注了科研通微信公众号
1秒前
gfi完成签到,获得积分10
2秒前
2秒前
眼睛大的问丝关注了科研通微信公众号
2秒前
123完成签到,获得积分20
2秒前
明亮的冷雪完成签到,获得积分10
2秒前
风趣的老太应助哈哈哈采纳,获得10
2秒前
陈文学发布了新的文献求助10
3秒前
彭于晏应助小王吧采纳,获得10
3秒前
3秒前
啊啊啊完成签到,获得积分10
3秒前
4秒前
ye完成签到,获得积分10
4秒前
Zzzz完成签到,获得积分20
5秒前
陆吉完成签到,获得积分10
5秒前
芷莯发布了新的文献求助10
5秒前
xy发布了新的文献求助10
5秒前
5秒前
123发布了新的文献求助10
6秒前
6秒前
Ftplanet发布了新的文献求助10
6秒前
6秒前
blue完成签到,获得积分10
7秒前
water应助多久之前采纳,获得10
7秒前
7秒前
yu发布了新的文献求助10
8秒前
8秒前
华仔应助ATREE采纳,获得10
8秒前
9秒前
梦XING发布了新的文献求助10
9秒前
小胡发布了新的文献求助10
9秒前
Cheng_Wei发布了新的文献求助10
9秒前
9秒前
情怀应助牛小牛采纳,获得10
10秒前
陆吉发布了新的文献求助10
10秒前
高分求助中
Picture Books with Same-sex Parented Families: Unintentional Censorship 700
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
Effective Learning and Mental Wellbeing 300
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 3974779
求助须知:如何正确求助?哪些是违规求助? 3519193
关于积分的说明 11197417
捐赠科研通 3255311
什么是DOI,文献DOI怎么找? 1797760
邀请新用户注册赠送积分活动 877150
科研通“疑难数据库(出版商)”最低求助积分说明 806187