认知科学研究所支持这项研究。我们感谢原始开源算法的开发人员，这些算法适用于开发 BrainBeats 的一些算法。

揭示脑心相互作用与 EEG 和心脏信号

作者没有什么可披露的。

协议中的关键步骤 关键步骤在步骤 1.1-1.4 中描述。警告和错误消息在工具箱中的不同位置实现，以帮助用户了解他们可能遇到问题的原因（例如，EEG 数据中未加载电极位置、文件长度太短而无法计算超低频 HRV 的可靠测量、信号质量太低而无法进行任何可靠的分析等）。每个功能都为高级用户记录了数据，并且可以轻松微调参数（推荐参数和典型范围记录在本手稿和代码中）。用?...

长期以来，还原论方法一直主导着人类生理学和认知学的科学研究。这种方法涉及将复杂的身体和心理过程分解成更小、更易于管理的组件，使研究人员能够孤立地关注单个系统。这种策略的出现是由于研究人类身心错综复杂和相互关联的本质所面临的挑战1。还原论有助于孤立地理解各个子系统，例如阐明离子通道和动作电位在神经2 或心脏3 通信中的作用。然而，我们对这些孤立的系统如何在更大的空间和时间尺度上相互作用的理解仍然存在重大差距。多模态（综合或生态）框架认为人体是一个复杂的多维系统，其中思想不被视为大脑的产物，而是生物的一种活动，一种将大脑整合到人体日常功能中的活动4。多模态和还原论方法并不排他性，就像我们不能在没有整个大脑的情况下研究一个神经元，或者在不了解单个神经元特性的情况下就无法研究整个大脑一样。它们共同为对人类健康、病理学、认知、心理学和意识的更全面、协同的理解铺平了道路。该方法旨在通过提供脑电图 （EEG） 和心血管信号的联合分析，即心电图 （ECG） 和光电容积脉搏波 （PPG），来简化大脑和心脏之间相互作用的多模态研究。该工具箱在 MATLAB 中作为 EEGLAB 插件实现，解决了现有的方法限制，并成为开源的，以促进科学领域的可访问性和可重复性。它将最新的准则和建议实施到其设计和默认参数中，以鼓励用户遵循已知的最佳实践。对于感兴趣的研究人员和临床医生来说，所提出的工具箱应该是宝贵的资源 1） 研究心跳诱发电位，2） 从 EEG 和 ECG/PPG 信号中提取特征，或 3） 从 EEG 信号中去除心脏伪影。
心脑研究 心脏和大脑之间的关系历来通过神经影像学方法进行研究，例如功能磁共振成像 （fMRI） 和正电子发射断层扫描 （PET）。使用这些工具，研究人员突出显示了一些与心血管控制相关的大脑区域（例如，心率和血压的操纵5），显示了心率对 BOLD 信号的影响6，或确定了导致冠心病的潜在脑身通路（即压力诱发的血压7).虽然这些研究极大地促进了我们对中枢神经系统 （CNS） 和心血管功能之间复杂相互作用的理解，但这些神经影像学技术价格昂贵、可用性有限，并且仅限于受控的实验室环境，这限制了它们在现实世界和大规模应用中的实用性。
相比之下，EEG 和 ECG/PPG 是更实惠和便携的工具，为在更多样化的环境和人群中或更长时间内研究脑心相互作用提供了潜力，提供了新的机会。ECG 通过放置在皮肤（通常位于胸部或手臂）上的电极测量心脏收缩和放松时每次心跳产生的电信号8。PPG 使用光源（例如 LED）和光电探测器（通常放置在指尖、手腕或前额）测量微血管组织中的血容量变化（即血流和脉搏率），具体取决于血液如何比周围组织吸收更多的光9。这两种方法都提供了有关心血管功能的宝贵信息，但用途不同，并提供不同的数据类型。与心电图一样，脑电图记录数千个皮层神经元同步活动产生的电场，这些神经元通过细胞外基质、组织、颅骨和头皮传播，直到到达放置在头皮表面的电极10。因此，脑电图和心电图/PPG 的使用对于促进我们对脑心相互作用背后的生理、认知和情感过程及其对人类健康和福祉的影响的理解具有巨大的前景。因此，使用 BrainBeats 工具箱从 EEG、ECG/PPG 信号捕获心脑相互作用可能对以下科学领域特别有用：临床诊断和预测、大数据机器学习 （ML）、真实世界自我监测11 和移动脑/身体成像 （MoBI）12,13。
联合分析 EEG 和 ECG 信号的两种方法 研究脑电图和心血管信号之间相互作用有两种主要方法：
时域中的心跳诱发电位 （HEP）：事件相关电位 （ERP），时频域中的心跳诱发电位 （HEO）：事件相关频谱扰动 （ERSP） 和试验间相干性 （ITC）。这种方法检查大脑如何处理每个心跳。以毫秒 （ms） 的精度，这种方法要求两个时间序列完全同步，并且心跳要在 EEG 信号中标记。这种方法近年来引起了人们的兴趣 14,15,16,17,18,19。
基于特征的方法：这种方法从连续信号中提取 EEG 和心率变异性 （HRV） 特征，并检查它们之间的关联。这是独立进行的 EEG（通常称为定量 EEG 或 qEEG20）、ECG21、22、23 和 PPG24、25、26。这种方法通过捕获与状态和特征相关的变量来提供有前途的应用。请注意，对于 EEG 和心血管信号，记录时间越长，性状变量 27,28,29 就越显性。因此，应用程序取决于记录参数。基于特征的分析越来越受到关注，为预测精神和神经系统疾病的发展、治疗反应或复发提供了新的定量指标 30,31,32,33,34,35。这种方法对于大型和真实世界的数据集（例如，诊所、远程监控）尤其引人注目，这要归功于可穿戴神经技术的最新创新11。一个较少探索的应用是识别特定大脑和心脏特征之间的关联，突出潜在的潜在中枢神经系统动力学。心率变异性 （HRV） 可以根据 ECG 和 PPG 信号计算。它通过测量心跳之间时间间隔的变化（即正常到正常的间隔）来提供有关自主神经系统 （ANS） 的信息27。交感神经 （SNS） 活动增加（例如，在压力或运动期间）通常会降低 HRV，而副交感神经 （PNS） 活动（例如，在放松期间）会增加 HRV。由于 PNS 活动增强，较慢的呼吸频率通常会增加 HRV，尤其是对于短记录（<10 分钟）27。较高的 HRV 分数通常表明 ANS 更具弹性和适应性，而较低的 HRV 可能表明压力、疲劳或潜在的健康问题。长时间的 HRV 记录（即至少 24 小时）为各种健康状况提供预测预后，包括心血管疾病、压力、焦虑和一些神经系统疾病27。血压、心率或胆固醇水平等指标提供有关心血管系统状态的信息。相比之下，HRV 增加了一个动态方面，显示心脏如何响应压力并从压力中恢复。
BrainBeats 相对于现有方法的优势 虽然存在工具，如下所述，可以彼此独立地处理心血管和 EEG 信号，但它们不能联合分析。此外，大多数处理心血管信号的可用方法都涉及昂贵的许可，不允许自动处理（尤其有利于大型数据集），具有阻止透明度和可重复性的专有算法，或者由于不提供图形用户界面 （GUI） 而需要高级编程技能36。据我们所知，四个开源 MATLAB 工具箱支持使用 GUI 进行 HEP/HEO 分析：ecg-kit 工具箱37、BeMoBIL 管道38、HEPLAB EEGLAB 插件39 和 CARE-rCortex 工具箱40。虽然 HEPLAB、BeMoBIL 和 ecg-kit 通过检测心跳并在 EEG 信号中标记它们来促进 HEP 分析，但它们不提供统计分析或仅限于时域（即 HEP）。CARE-rCortex 插件通过支持 ECG 和呼吸信号、时频域分析、统计以及适用于 HEP/HEO 分析的高级基线归一化和校正方法来解决这些问题。然而，它使用 Bonferroni 方法对 1 型错误（即假阳性）进行统计校正，这对于 EEG 应用来说过于保守且生理上不合理，导致 II 型错误（即假阴性）增加41。此外，该工具箱不提供用于自动化的命令行访问。最后，最近的研究建议不要使用基线校正方法 42,43,44，因为它们会降低信噪比 （SNR），并且在统计上是不必要的和不可取的。
为了解决这些限制，我们引入了 BrainBeats 工具箱，目前在 MATLAB 环境中作为开源 EEGLAB 插件实现。与以前的方法相比，它具有以下优点：
1） 易于使用的 GUI 和命令行功能（适用于旨在执行自动化处理的程序员）。2） 经过验证的算法、参数和处理心血管信号的指南，例如检测 R 峰、插值 RR 伪影和计算 HRV 指标（例如，用于窗口化、重采样、归一化等的植入指南27,45,46）。这很重要，因为 Vest 等人证明了这些处理步骤中的适度差异如何导致不同的结果，从而导致 HRV 指标缺乏可重复性和临床适用性46。3） 经过验证的算法、默认参数和处理脑电信号的指南，包括过滤和窗口化44,47，重新引用48,49，去除异常通道和伪影 50,51,52，优化的 ICA 分解和独立组件的分类 53,54,55,56.在使用工具箱满足他们的需求之前，用户可以微调所有预处理参数，甚至使用他们喜欢的方法预处理他们的 EEG 数据（例如，使用 EEGLAB clean_rawdata 插件50,52、BeMoBIL 管道38、PREP 管道57 等）。4） 心电图信号的心跳诱发电位（HEP，即时域）和振荡（HEO;使用小波或 FFT 方法的事件相关频谱扰动，以及试验间相干性可通过标准 EEGLAB 软件获得）。通过 EEGLAB 的标准软件可获得带有 1 类误差校正的参数和非参数统计。非参数统计包括排列统计和多重比较的时空校正（例如，时空聚类或无阈值聚类增强）58,59。用户可以使用 LIMO-EEG 插件来实现分层线性建模，该模型很好地考虑了受试者内和受试者间的方差，并实施了无假设的质量单变量方法，对 I 型和 II 型误差进行了稳健控制60,61。HEP/HEO 数据统计分析可以在通道域和独立组件域中进行。5） 来自 PPG 信号的 HEP/HEO 和 HRV 分析（首次用于 HEP/HEO）。6） 首次支持脑电和 HRV 特征的联合提取。7） 该工具箱提供各种数据可视化，以检查各种必要处理步骤中的信号，并在主题级别进行输出。
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always"><tbody><tr><td>方法</td>
			<td>从 ECG 中检测 R 峰</td>
			<td>检测 PPG 的 R 波</td>
			<td>HEP/HEO 系列</td>
			<td>EEG & HRV 特性</td>
			<td>从 EEG 中去除心脏伪影</td>
			<td>图形用户界面</td>
			<td>命令行</td>
		</tr><tr><td>心电图套件</td>
			<td>X</td>
			<td>X</td>
			<td>X</td>
			<td></td>
			<td></td>
			<td></td>
			<td>X</td>
		</tr><tr><td>BeMoBIL 贝莫比尔</td>
			<td>X</td>
			<td></td>
			<td>X</td>
			<td></td>
			<td></td>
			<td></td>
			<td>X</td>
		</tr><tr><td>HEPLAB</td>
			<td>X</td>
			<td></td>
			<td>X</td>
			<td></td>
			<td></td>
			<td>X</td>
			<td>X</td>
		</tr><tr><td>CARE-rCortex 皮层</td>
			<td>X</td>
			<td></td>
			<td>X</td>
			<td></td>
			<td></td>
			<td>X</td>
			<td>X</td>
		</tr><tr><td>脑跳</td>
			<td>X</td>
			<td>X</td>
			<td>X</td>
			<td>X</td>
			<td>X</td>
			<td>X</td>
			<td>X</td>
		</tr></tbody></table>表 1：BrainBeats 相对于先前存在的类似方法带来的新颖性。
帮助读者确定该方法是否适合他们的信息 该工具箱适用于任何拥有 EEG 和 ECG/PPG 数据的研究人员或临床医生。该插件尚不支持从单独的文件导入 EEG 和 ECG/PPG 信号（尽管此功能将很快推出）。该工具箱适用于任何旨在执行 HEP/HEO 分析、使用标准化方法提取 EEG 和/或 HRV 特征或简单地从 EEG 信号中去除心脏伪影的任何人。图 1 中总结了 BrainBeats 的整体流程和方法的框图。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65829/65829fig01.jpg"> 图 1.总结 BrainBeats 整体架构和流程的框图。这三种方法中常见的运算是棕色的。特定于心跳诱发电位 （HEP） 和振荡 （HEO） 的操作为绿色。特定于提取 EEG 和 HRV 特征的操作为蓝色。专门用于从 EEG 信号中去除心脏伪影的操作为红色。 <a href="https://www.jove.com/files/ftp_upload/65829/65829fig01large.jpg" target="_blank">请单击此处查看此图的较大版本。</a>

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>EEGLAB</td>
			<td>Swartz Center for Computational Neuroscience (SCCN)</td>
			<td></td>
			<td>Free/Open-source</td>
		</tr>
		<tr>
			<td>MATLAB</td>
			<td>The Mathworks, Inc.</td>
			<td></td>
			<td>Requires a license</td>
		</tr>
		<tr>
			<td>Windows PC</td>
			<td>Lenovo, Inc.</td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

brainbeats as an open-source eeglab plugin to jointly analyze eeg and cardiovascular signals

大脑和心血管系统之间的相互作用因其在促进我们对人类生理学的理解和改善健康状况方面的潜力而受到越来越多的关注。然而，由于缺乏指南、标准化信号处理和统计工具、图形用户界面 （GUI） 以及用于处理大型数据集或提高可重复性的自动化，因此对这些信号进行多模态分析具有挑战性。标准化的 EEG 和心率变异性 （HRV） 特征提取方法还存在进一步的空白，破坏了临床诊断或机器学习 （ML） 模型的稳健性。为了应对这些限制，我们引入了 BrainBeats 工具箱。BrainBeats 作为开源 EEGLAB 插件实现，集成了三个主要协议：1） 心跳诱发电位 （HEP） 和振荡 （HEO），用于以毫秒级精度评估时间锁定的脑心相互作用;2） EEG 和 HRV 特征提取，用于检查各种大脑和心脏指标之间的关联/差异或构建强大的基于特征的 ML 模型;3） 在进行 EEG 分析时，从 EEG 信号中自动提取心脏伪影，以去除任何潜在的心血管污染。我们提供了一个分步教程，将这三种方法应用于包含同步 64 通道 EEG、ECG 和 PPG 信号的开源数据集。用户可以使用图形用户界面 （GUI） 或命令行轻松微调参数，以定制他们独特的研究需求。BrainBeats 应该使脑心相互作用研究更容易获得和可重复性。

For a long time, the reductionist approach has dominated scientific inquiry in human physiology and cognition. This approach involved dissecting complex bodily and mental processes into smaller, more manageable components, allowing researchers to focus on individual systems in isolation. This strategy arose due to the challenges in studying the intricate and interconnected nature of the human body and mind1. Reductionism has been instrumental in understanding individual subsystems in isolation, such as elucidating the role of ion channels and action potentials for neural2 or cardiac3 communication. However, a significant gap remains in our understanding of how these isolated systems interact on a larger spatial and temporal scale. The multimodal (integrative or ecological) framework considers the human body a complex multidimensional system, where the mind is seen not as a product of the brain but as an activity of the living being, an activity that integrates the brain within the everyday functions of the human body4. The multimodal and reductionist approaches are not exclusive, just like we cannot study one neuron without the whole brain or the whole brain without understanding individual neuron properties. Together, they pave the way for a more comprehensive, synergetic understanding of human health, pathology, cognition, psychology, and consciousness. The present method aims to ease the multimodal investigation of the interplay between the brain and the heart by providing joint analysis of electroencephalography (EEG) and cardiovascular signals, namely electrocardiography (ECG) and photoplethysmography (PPG). This toolbox, implemented as an EEGLAB plugin in MATLAB, addresses existing methodological limitations and is made open source to facilitate accessibility and reproducibility in the scientific area. It implements the latest guidelines and recommendations into its design and default parameters to encourage users to follow known best practices. The proposed toolbox should be a valuable resource for researchers and clinicians interested in 1) studying&#160;heartbeat-evoked potentials&#160;, 2) extracting features from EEG and ECG/PPG signals, or 3) removing heart artifacts from EEG signals.
Heart-brain research 
The relationship between the heart and the brain has been historically studied via neuroimaging methods such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET). Using these tools, researchers highlighted some brain regions associated with cardiovascular control (e.g., manipulation of heart rate and blood pressure5), showed the influence of heart rate on the BOLD signal6, or identified potential brain-body pathways contributing to coronary heart disease (i.e., stress-evoked blood pressure7). While these studies have significantly advanced our understanding of the complex interplay between the central nervous system (CNS) and cardiovascular function, these neuroimaging techniques are expensive, have limited availability, and are confined to controlled laboratory settings, which restricts their practicality for real-world and large-scale applications.
In contrast, EEG and ECG/PPG are more affordable and portable tools that offer the potential for studying brain-heart interactions in more diverse settings and populations or over longer periods, providing new opportunities. ECG measures the electrical signals generated by each heartbeat when the heart contracts and relaxes via electrodes placed on the skin (usually on the chest or arms)8. PPG measures blood volume changes in the microvascular tissues (i.e., blood flow and pulse rate) using a light source (e.g., LED) and a photodetector (commonly placed on a fingertip, wrist, or forehead), relying on how blood absorbs more light than the surrounding tissue9. Both methods provide valuable information about cardiovascular function but serve different purposes and offer distinct data types. Like ECG, EEG records the electrical fields generated by the synchronized activity of thousands of cortical neurons that propagate through the extracellular matrix, tissues, skull, and scalp until they reach the electrodes placed on the scalp&#39;s surface10. As such, the use of EEG and ECG/PPG holds great promise for advancing our understanding of the physiological, cognitive, and emotional processes underlying brain-heart interactions and their implications for human health and well-being. Therefore, capturing heart-brain interplay from EEG, ECG/PPG signals with the BrainBeats toolbox may be particularly useful for the following scientific areas: clinical diagnostic and forecasting, big data machine learning (ML), real-world self-monitoring11, and mobile brain/body imaging (MoBI)12,13.
Two approaches for jointly analyzing EEG and ECG signals 
There are two main approaches to studying interactions between EEG and cardiovascular signals:
The heartbeat-evoked potentials (HEP) in the time domain: event-related potentials (ERP), and the heartbeat-evoked oscillations (HEO) in the time-frequency domain: event-related spectral perturbations (ERSP) and inter-trial coherence (ITC). This approach examines how the brain processes each heartbeat. With millisecond (ms) accuracy, this method requires that both time series are perfectly synchronized and the heartbeats to be marked in the EEG signals. This approach has gained interest in recent years14,15,16,17,18,19.
Feature-based approach: this approach extracts EEG and heart-rate variability (HRV) features from continuous signals and examines associations between them. This has been done independently for EEG (often termed quantitative EEG or qEEG20), ECG21,22,23, and PPG24,25,26. This approach presents promising applications by capturing both state- and trait-related variables. Note that, for both EEG and cardiovascular signals, the longer the recording, the more dominant the trait variable27,28,29. Thus, the applications depend on the recording parameters. Feature-based analyses are gaining growing interest, providing new quantitative metrics for forecasting the development of mental and neurological disorders, treatment-response, or relapse30,31,32,33,34,35. This approach is especially compelling with large and real-world datasets (e.g., clinic, remote monitoring), which can be more easily obtained thanks to the recent innovations in wearable neurotechnology11. A less explored application is the identification of associations between specific brain and heart features, highlighting potential underlying central nervous system dynamics. Heart rate variability (HRV) can be calculated from both ECG and PPG signals. It provides information about the autonomous nervous system (ANS) by measuring the variations in time intervals between heartbeats (i.e., the normal-to-normal intervals)27. Increased sympathetic (SNS) activity (e.g., during stress or exercise) typically reduces HRV, while parasympathetic (PNS) activity (e.g., during relaxation) increases it. A slower breathing rate generally increases HRV due to enhanced PNS activity, especially for short recordings (&#60;10 min)27. Higher HRV scores generally suggest a more resilient and adaptable ANS, while a lower HRV can indicate stress, fatigue, or underlying health issues. Long HRV recordings (i.e., at least 24 h) provide a predictive prognosis for various health conditions, including cardiovascular diseases, stress, anxiety, and some neurological conditions27. Measures like blood pressure, heart rate, or cholesterol levels give information about the cardiovascular system&#39;s status. In contrast, HRV adds a dynamic aspect, showing how the heart responds to and recovers from stress.
BrainBeats&#39; advantages over existing methods 
While tools exist, as reviewed below, to process cardiovascular and EEG signals independently from each other, they cannot be jointly analyzed. Furthermore, most available means to process cardiovascular signals involve costly licensing, do not allow automated processing (especially beneficial for large datasets), have proprietary algorithms that prevent transparency and reproducibility, or require advanced programming skills by not providing a graphical user interface (GUI)36. To our knowledge, four open-source MATLAB toolboxes support HEP/HEO analysis with a GUI: the ecg-kit toolbox37, the BeMoBIL pipeline38, the HEPLAB EEGLAB plugin39, and the CARE-rCortex toolbox40. While HEPLAB, BeMoBIL, and ecg-kit facilitate HEP analysis by detecting heartbeats and marking them in the EEG signals, they do not provide statistical analysis or are limited to the time domain (i.e., HEP). The CARE-rCortex plugin addressed these issues by supporting ECG and respiratory signals, time-frequency domain analysis, statistics, and advanced baseline normalization and correction methods adapted to HEP/HEO analysis. However, it uses the Bonferroni method for statistical correction of the type 1 error (i.e., false positives), which is too conservative and not physiologically sound for EEG applications, leading to an increase in type II errors (i.e., false negatives)41. Furthermore, the toolbox does not offer command-line access for automation. Finally, recent studies recommend against baseline correction methods42,43,44, as they reduce the signal-to-noise ratio (SNR) and are statistically unnecessary and undesirable.
To address these limitations, we introduce the BrainBeats toolbox, currently implemented as an open-source EEGLAB plugin in the MATLAB environment. It incorporates the following advantages over previous methods:
1) An easy-to-use GUI and command-line capabilities (for programmers aiming to perform automated processing). 2) Validated algorithms, parameters, and guidelines for processing cardiovascular signals, such as detecting R peaks, interpolating RR artifacts, and computing HRV metrics (e.g., implanting guidelines for windowing, resampling, normalization, etc.27,45,46). This is important because Vest et al. demonstrated how modest differences in these processing steps can lead to divergent results, contributing to the lack of reproducibility and clinical applicability of HRV metrics46. 3) Validated algorithms, default parameters, and guidelines for processing EEG signals, including filtering and windowing44,47, re-referencing48,49, removal of abnormal channels and artifacts50,51,52, optimized ICA decomposition and classification of independent components53,54,55,56. The users can fine-tune all preprocessing parameters or even preprocess their EEG data with their preferred method before using the toolbox to match their needs (e.g., with EEGLAB clean_rawdata plugin50,52, the BeMoBIL pipeline38, the PREP pipeline57, etc.). 4) Heartbeat-evoked potentials (HEP, i.e., time domain) and oscillations (HEO; event-related spectral perturbations with wavelet or FFT methods, and inter-trial coherence are available through the standard EEGLAB software) from ECG signals. Parametric and nonparametric statistics with corrections for type 1 errors are available via EEGLAB&#39;s standard software. Nonparametric statistics include permutation statistics and spatiotemporal corrections for multiple comparisons (e.g., spatiotemporal clustering or threshold-free cluster enhancement)58,59. Users can use the LIMO-EEG plugin to implement hierarchical linear modeling, which accounts well for within and between-subjects variance and implements an assumption-free mass-univariate approach with robust control for type I and II errors60,61. The HEP/HEO data statistical analyses can be performed in the channel and independent component domains. 5) HEP/HEO and HRV analysis from PPG signals (for the first time for HEP/HEO). 6) Supports the joint extraction of EEG and HRV features for the first time. 7) The toolbox provides various data visualizations to inspect signals at various necessary processing steps and outputs at the subject level.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Method</td>
			<td>Detect R-peaks from ECG</td>
			<td>Detect R-waves from PPG</td>
			<td>HEP/HEO</td>
			<td>EEG &#38; HRV features</td>
			<td>Remove heart artifacts from EEG</td>
			<td>GUI</td>
			<td>Command line</td>
		</tr>
		<tr>
			<td>ecg-kit</td>
			<td>X</td>
			<td>X</td>
			<td>X</td>
			<td></td>
			<td></td>
			<td></td>
			<td>X</td>
		</tr>
		<tr>
			<td>BeMoBIL</td>
			<td>X</td>
			<td></td>
			<td>X</td>
			<td></td>
			<td></td>
			<td></td>
			<td>X</td>
		</tr>
		<tr>
			<td>HEPLAB</td>
			<td>X</td>
			<td></td>
			<td>X</td>
			<td></td>
			<td></td>
			<td>X</td>
			<td>X</td>
		</tr>
		<tr>
			<td>CARE-rCortex</td>
			<td>X</td>
			<td></td>
			<td>X</td>
			<td></td>
			<td></td>
			<td>X</td>
			<td>X</td>
		</tr>
		<tr>
			<td>BrainBeats</td>
			<td>X</td>
			<td>X</td>
			<td>X</td>
			<td>X</td>
			<td>X</td>
			<td>X</td>
			<td>X</td>
		</tr>
	</tbody>
</table>
TABLE 1:&#160;Novelties brought by BrainBeats relative to pre-existing, similar methods.
Information to help readers decide whether the method is appropriate for them 
This toolbox is appropriate for any researcher or clinician with EEG and ECG/PPG data. The plugin does not yet support importing EEG and ECG/PPG signals from separate files (although this feature will be available soon). The toolbox is appropriate for anyone aiming to perform HEP/HEO analysis, extract EEG and/or HRV features with standardized methods, or simply remove heart artifacts from EEG signals. See Figure 1 for a block diagram summarizing BrainBeats&#39; overall flow and methods.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65829/65829fig01.jpg" /> 
FIGURE 1.&#160;Block diagram summarizing BrainBeats&#39; overall architecture and flow.&#160;The operations that are common across the three methods are brown. Operations specific to heartbeat-evoked potentials (HEP) and oscillations (HEO) are green. Operations specific to the extraction of EEG and HRV features are blue. Operations specific to removing heart artifacts from the EEG signals are red. <a href="https://www.jove.com/files/ftp_upload/65829/65829fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

作者聚焦：使用用于多模态信号分析的综合 EEGLAB 插件推进脑心相互作用的研究

BrainBeats 作为开源 EEGLAB 插件，用于联合分析 EEG 和心血管信号

The scope of the BrainBeats toolbox is to provide an open source and automated tool for conducting research on brain-heart interplay. It allows researchers to perform multimodal analysis of EEG and cardiovascular signals through standardized signal processing, feature extraction, graphical user interface, and data visualization. As a result of the historical reductionist approach in science, the brain and the heart systems have been separated and isolated, leading to a limited understanding of their interaction and mutual influence.The BrainBeats toolbox aims to bridge this gap. Understanding the interplay between the brain and the heart allows researchers to gain deeper insights into how cognitive and emotional states influence cardiovascular function and vice versa. In turn, these insights can lead to new therapeutic interventions and treatments.

首先，使用 BrainBeats 插件对 EEG 和 ECG 数据进行预处理，识别和去除伪影，并分析心跳诱发电位 （HEP） 和振荡 （HEO）。BrainBeats 成功地从 ECG 信号和一些 RR 伪影中检测到 RR 间期（图 2）。BrainBeats 还在命令窗口中报告说，11/305 （3.61%） 的检测信号被标记为伪影并进行内插。RR 间隔的平均信号质量指数 （SQI） （插值前） 的值为 1，这是最高值。低信号质量对应于 20% 或更多的...

Read this Scientific Article Protocol about BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals at JoVE.com

BrainBeats 工具箱是一个开源的 EEGLAB 插件，旨在联合分析 EEG 和心血管 （ECG/PPG） 信号。它包括心跳诱发电位 （HEP） 评估、基于特征的分析以及从 EEG 信号中提取心脏伪影。该协议将有助于通过两个镜头（HEP 和特征）研究脑心相互作用，从而提高可重复性和可及性。

The interplay between the brain and the cardiovascular systems is garnering increased attention for its potential to advance our understanding of human ...

BrainBeats 工具箱的范围是提供一个开源的自动化工具，用于进行脑心相互作用的研究。它允许研究人员通过标准化信号处理、特征提取、图形用户界面和数据可视化对 EEG 和心血管信号进行多模态分析。由于科学中历史上的还原论方法，大脑和心脏系统已经分离和孤立，导致对它们的相互作用和相互影响的理解有限。BrainBeats 工具箱旨在弥合这一差距。了解大脑和心脏之间的相互作用使研究人员能够更深入地了解认知和情绪状态如何影响心血管功能，反之亦然。反过来，这些见解可以带来新的治疗干预和治疗方法。

brainbeats-as-an-open-source-eeglab-plugin-to-jointly-analyze-eeg

Research

JoVE Journal

Neuroscience

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

1.1K 次观看.  Toulouse III University. BrainBeats 工具箱的范围是提供一个开源的自动化工具，用于进行脑心相互作用的研究。它允许研究人员通过标准化信号处理、特征提取、图形用户界面和数据可视化对 EEG 和心血管信号进行多模态分析。由于科学中历史上的还原论方法，大脑和心脏系统已经分离和孤立，导致对它们的相互作用和相互影响的理解有限。BrainBeats 工具箱旨在弥合这一差距。了解大脑和心脏之?...

视频: 作者聚焦：使用用于多模态信号分析的综合 EEGLAB 插件推进脑心相互作用的研究

The interplay between the brain and the cardiovascular systems is garnering increased attention for its potential to advance our understanding of human physiology and improve health outcomes. However, the multimodal analysis of these signals is challenging due to the lack of guidelines, standardized signal processing and statistical tools, graphical user interfaces (GUIs), and automation for processing large datasets or increasing reproducibility. A further void exists in standardized EEG and heart-rate variability (HRV) feature extraction methods, undermining clinical diagnostics or the robustness of machine learning (ML) models. In response to these limitations, we introduce the BrainBeats toolbox. Implemented as an open-source EEGLAB plugin, BrainBeats integrates three main protocols: 1) Heartbeat-evoked potentials (HEP) and oscillations (HEO) for assessing time-locked brain-heart interplay at the millisecond accuracy; 2) EEG and HRV feature extraction for examining associations/differences between various brain and heart metrics or for building robust feature-based ML models; 3) Automated extraction of heart artifacts from EEG signals to remove any potential cardiovascular contamination while conducting EEG analysis. We provide a step-by-step tutorial for applying these three methods to an open-source dataset containing simultaneous 64-channel EEG, ECG, and PPG signals. Users can easily fine-tune parameters to tailor their unique research needs using the graphical user interface (GUI) or the command line. BrainBeats should make brain-heart interplay research more accessible and reproducible.

The BrainBeats toolbox is an open-source EEGLAB plugin designed to jointly analyze EEG and cardiovascular (ECG/PPG) signals. It includes heartbeat-evoked potentials (HEP) assessment, feature-based analysis, and heart artifact extraction from EEG signals. The protocol will aid in studying brain-heart interplay through two lenses (HEP and features), enhancing reproducibility and accessibility.

科研

神经科学

Setting Up and Analyzing Heartbeat-Evoked Potentials (HEP) with BrainBeats in EEGLAB

To begin, open MATLAB on the computer. In MATLAB's home panel, click the Set Path button to add the path to the EEGLAB folder. Click the Add Folder button and select the eeglab folder that has been unzipped.Then click Save, followed by Close. In MATLAB's command window, type eeglab to launch EEGLAB. To install the BrainBeats plugin, click File, followed by Manage EEGLAB Extensions.Then in the search bar, type brainbeats, select the brainbeats plugin in the list, and click Install. For loading the sample dataset into EEGLAB, click File and Load existing data. Navigate to the eeglab folder, then go to the plugins and BrainBeats folders.Open the sample data folder and select the file dataset.set. In EEGLAB, sequentially click Tools, BrainBeats, and 1st level, subject level, to open the BrainBeats first general user interface or GUI. Select heartbeat-evoked potentials, or HEP, as the analysis to run and ECG as the heart data type.Then click on the button to display the list of channels and select the ECG channel. Keep the options visualize outputs and save outputs selected and click OK.In the second GUI window, from the preprocess EEG section, change the power line noise to 50 Hz and click OK to launch. Once the warning message to remove the detected PPG channel appears, click Yes.For the PPG signal, load the dataset. set file again, then in the EEGLAB, sequentially click Tools, BrainBeats, and 1st level, subject level, to open BrainBeats'first GUI. Select heartbeat-evoked potentials, or HEP, as the analysis to run and PPG as the heart data type.Then click on the button to display the list of channels and select the PPG channel. Keep the options visualize outputs and save outputs selected and click OK.In the second GUI window from the preprocess EEG section, change the power line noise to 50 Hz and click OK to launch. Once the warning message to remove the extra ECG channel appears, click yes.BrainBeats successfully detected the RR intervals from the ECG signal and some RR artifacts. A significant heartbeat-evoked oscillation in the alpha band from 150 to 400 milliseconds post-heartbeat at a fronto-central scalp site was observed. On the other hand, the intertrial coherence analysis suggested no significant phase locking or resetting of the EEG phase with respect to the heartbeats.BrainBeats also detected the RR intervals from the PPG signal and some RR artifacts. A significant effect at the fronto-central scalp site in almost the whole time window following the R-wave and almost the whole frequency range was observed. Intertrial coherence analysis suggesting no significant phase locking or resetting of the EEG phase.

在 EEGLAB 中使用 BrainBeats 设置和分析心跳诱发电位 （HEP）

Extracting EEG and Heart-Rate Variability (HRV) Features and Heart Artifacts with BrainBeats in EEGLAB

To load the sample dataset into EEGLAB, click File and Load existing data. Navigate to the EEGLAB folder, then go to the plugins folder and BrainBeats folder. Open the sample data folder and select the file dataset.set.After loading the sample dataset into EEGLAB, sequentially click Tools, BrainBeats, and 1st level subject level to open the main GUI and to select the parameters. Select Extract EEG HRV features for the analysis type and ECG for the heart signal type. Select ECG in the list of electrode labels and click Ok.Then, a second GUI window pops up with different parameters for the EEG preprocessing and extracting HRV and EEG features.In the Preprocess EEG section, change the Power line noise to 50 hertz and click Ok to launch. Once the warning message to remove the extra PPG channel appears, click Yes. At the end of all operations, type eegh in MATLAB's command window to print the command line and repeat all steps via a single command line.After loading the same dataset file, sequentially click Tools, BrainBeats, and 1st level subject level to open the main GUI to select the parameters. Select Extract EEG HRV features for the analysis and PPG for the heart signal type. Select PPG for the channel name and click Ok.In the second GUI window, from the Preprocess EEG section, change the Power line noise to 50 hertz and click Ok to run.Once the warning message to remove the detected ECG channel appears, click yes, and review the generated output. Load the sample dataset file, dataset.set. Then, sequentially click Tools, BrainBeats, and 1st level subject level to open the main GUI.Select Extract heart artifacts from EEG signals for the analysis type and ECG for the heart signal type. Type ECG in the list of electrode labels and click Ok.After selecting preprocessing parameters for EEG signals, select 50 hertz for line noise, check the Boost mode box, and click Ok to launch. Once the warning message to remove the extra PPG channel appears, click Yes.Using BrainBeats, EEG and heart-rate variability features were extracted from ECG signals, revealing a peak in heart-rate variability power spectral density at approximately 0.19 hertz within the high-frequency band, and an EEG at approximately 10.5 hertz in the alpha band. Scalp topographies showed predominant localizations in posterior regions with higher complexity in the frontal right and posterior areas. A subsequent analysis using PPG signals for heart-rate variability indicated a peak at approximately 0.04 hertz in the low-frequency band and a bifurcated peak around approximately 0.19 hertz, suggesting lower PPG signal quality.