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!

PMF-CNN: parallel multi-band fusion convolutional neural network for SSVEP-EEG decoding

计算机科学 脑-机接口 脑电图 卷积神经网络 模式识别(心理学) 人工智能 解码方法 语音识别 算法 心理学 精神科
作者
Jianli Yang,Songlei Zhao,Zhiyu Fu,Xiuling Liu
出处
期刊:Biomedical Physics & Engineering Express [IOP Publishing]
卷期号:10 (3): 035002-035002
标识
DOI:10.1088/2057-1976/ad2e36
摘要

Abstract Steady-state visual evoked potential (SSVEP) is a key technique of electroencephalography (EEG)-based brain-computer interfaces (BCI), which has been widely applied to neurological function assessment and postoperative rehabilitation. However, accurate decoding of the user’s intended based on the SSVEP-EEG signals is challenging due to the low signal-to-noise ratio and large individual variability of the signals. To address these issues, we proposed a parallel multi-band fusion convolutional neural network (PMF-CNN). Multi frequency band signals were served as the input of PMF-CNN to fully utilize the time-frequency information of EEG. Three parallel modules, spatial self-attention (SAM), temporal self-attention (TAM), and squeeze-excitation (SEM), were proposed to automatically extract multi-dimensional features from spatial, temporal, and frequency domains, respectively. A novel spatial-temporal-frequency representation were designed to capture the correlation of electrode channels, time intervals, and different sub-harmonics by using SAM, TAM, and SEM, respectively. The three parallel modules operate independently and simultaneously. A four layers CNN classification module was designed to fuse parallel multi-dimensional features and achieve the accurate classification of SSVEP-EEG signals. The PMF-CNN was further interpreted by using brain functional connectivity analysis. The proposed method was validated using two large publicly available datasets. After trained using our proposed dual-stage training pattern, the classification accuracies were 99.37% and 93.96%, respectively, which are superior to the current state-of-the-art SSVEP-EEG classification algorithms. The algorithm exhibits high classification accuracy and good robustness, which has the potential to be applied to postoperative rehabilitation.
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
不过尔尔完成签到 ,获得积分10
10秒前
凤里完成签到 ,获得积分10
10秒前
36秒前
yangquanquan发布了新的文献求助10
40秒前
蓝色条纹衫完成签到 ,获得积分10
53秒前
HK完成签到 ,获得积分10
59秒前
1分钟前
刘天宇完成签到 ,获得积分10
1分钟前
科研通AI2S应助yangquanquan采纳,获得10
1分钟前
追寻青柏完成签到,获得积分10
1分钟前
iShine完成签到 ,获得积分10
1分钟前
完美世界应助追寻青柏采纳,获得10
1分钟前
2分钟前
幽默的太阳完成签到 ,获得积分10
2分钟前
2分钟前
2分钟前
拉布拉多多不多完成签到,获得积分10
2分钟前
甜甜友容完成签到,获得积分10
2分钟前
Jasper应助科研通管家采纳,获得10
3分钟前
3分钟前
XD824发布了新的文献求助10
3分钟前
陌小石完成签到 ,获得积分10
3分钟前
4分钟前
科研通AI2S应助zm采纳,获得10
4分钟前
XD824发布了新的文献求助80
4分钟前
lily完成签到 ,获得积分10
4分钟前
4分钟前
爱啃大虾发布了新的文献求助30
4分钟前
zm完成签到,获得积分10
4分钟前
科研通AI2S应助爱啃大虾采纳,获得10
5分钟前
甜美冥茗完成签到 ,获得积分10
5分钟前
5分钟前
XD824发布了新的文献求助10
5分钟前
zoey完成签到,获得积分20
6分钟前
zoey发布了新的文献求助10
6分钟前
foyefeng完成签到 ,获得积分10
6分钟前
濮阳灵竹完成签到,获得积分10
6分钟前
深情安青应助李小猫采纳,获得10
6分钟前
6分钟前
李小猫发布了新的文献求助10
6分钟前
高分求助中
Les Mantodea de Guyane Insecta, Polyneoptera 2500
Technologies supporting mass customization of apparel: A pilot project 450
A Field Guide to the Amphibians and Reptiles of Madagascar - Frank Glaw and Miguel Vences - 3rd Edition 400
A China diary: Peking 400
Brain and Heart The Triumphs and Struggles of a Pediatric Neurosurgeon 400
Cybersecurity Blueprint – Transitioning to Tech 400
Mixing the elements of mass customisation 400
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 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3784804
求助须知:如何正确求助?哪些是违规求助? 3330065
关于积分的说明 10244252
捐赠科研通 3045410
什么是DOI,文献DOI怎么找? 1671678
邀请新用户注册赠送积分活动 800597
科研通“疑难数据库(出版商)”最低求助积分说明 759524