Prediction of Scalp EEG Waveforms from Forehead Electrodes Using Convolutional Neural Networks to Improve Signal-to-Noise Ratio

脑电图 前额 α波 头皮 计算机科学 脑-机接口 听力学 信号(编程语言) 人工智能 模式识别(心理学) 语音识别 心理学 医学 神经科学 解剖 外科 程序设计语言
作者
Kazuki Yamawaki,Hiroki Watanabe,Yasushi Naruse
标识
DOI:10.1109/metroxraine54828.2022.9967638
摘要

The electroencephalogram (EEG) is a non-invasive method for measuring brain activity, and event-related potentials (ERPs)—EEG responses observed to be time-locked to events—have been used for brain-computer interfaces (BCI) in real-world environments. An EEG is generally measured from electrodes placed on the scalp. However, it is not suitable for daily use because the preparation time is relatively long, and the electrodes are likely to cause discomfort to the user. EEG measurements from disposable electrodes placed on the forehead (forehead EEG) have been used to mitigate this disadvantage. However, because many ERP components used in BCI show the maximal voltage on the scalp, the signal-to-noise ratio (SNR) of ERPs obtained from a forehead EEG is low, which may affect the reliability of a BCI system. To address this shortcoming, we propose convolutional neural networks that predict the EEG signal measured from electrodes placed on the scalp (scalp EEG) from forehead EEG. In the study, we focused on predicting the mismatch negativity (MMN) responses, and single-trial scalp EEG at Fz was predicted from three forehead EEG measures (Fpz, horizontal, and vertical electrooculograms). Data were measured while nine subjects performed a passive auditory oddball task. To evaluate the proposed model, the mean squared error (MSE) between the observed single-trial EEG at Fz and the predicted single-trial EEG from three forehead EEG measures was calculated, as well as the MSE between the observed ERP difference wave (deviant – standard) at Fz and the difference wave predicted from three forehead EEG measures within the time window in which MMN was observed. The result showed that within the time window in which MMN was observed, the MSE between the ERP difference wave at Fz and the ERP difference wave predicted from three forehead EEG measures was significantly smaller than the MSE between the ERP difference wave at Fz and the ERP difference wave at the forehead (Fpz). This indicates that the proposed neural network improved the SNR of the forehead EEG for predicting ERP responses at the scalp and could lead to enhancing the usefulness of forehead EEG for BCI use in daily life.

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
小可爱完成签到 ,获得积分10
刚刚
杨肉串儿完成签到,获得积分10
刚刚
无花果应助研友_ZAxKMn采纳,获得30
刚刚
xxx完成签到,获得积分10
刚刚
萝卜卷心菜完成签到 ,获得积分10
1秒前
1秒前
1秒前
不知名网友完成签到,获得积分10
1秒前
猫猫up发布了新的文献求助10
2秒前
2秒前
草莓灰灰完成签到,获得积分20
2秒前
3秒前
小二郎应助千迁采纳,获得10
3秒前
3秒前
3秒前
海上聆风发布了新的文献求助10
4秒前
li完成签到,获得积分10
4秒前
洁净青枫发布了新的文献求助10
4秒前
ff完成签到,获得积分10
5秒前
SciGPT应助xiaoya采纳,获得10
5秒前
草莓灰灰发布了新的文献求助10
5秒前
6秒前
6秒前
6秒前
动听的蛟凤完成签到,获得积分10
6秒前
w_完成签到,获得积分10
7秒前
李健应助玉玉采纳,获得10
7秒前
7秒前
栉风沐雨发布了新的文献求助10
8秒前
居居子发布了新的文献求助10
8秒前
hanshuo4400发布了新的文献求助10
8秒前
勤奋向真发布了新的文献求助10
8秒前
lingck发布了新的文献求助10
8秒前
8秒前
Yeyuntian发布了新的文献求助10
9秒前
10秒前
酷波er应助桥鲤梧桐采纳,获得10
10秒前
10秒前
Anna完成签到 ,获得积分10
10秒前
泥踩完成签到,获得积分10
11秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 330
Composite Predicates in English 300
Aktuelle Entwicklungen in der linguistischen Forschung 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 3982770
求助须知:如何正确求助?哪些是违规求助? 3526389
关于积分的说明 11231880
捐赠科研通 3264432
什么是DOI,文献DOI怎么找? 1801977
邀请新用户注册赠送积分活动 880143
科研通“疑难数据库(出版商)”最低求助积分说明 807840