BED: Biometric EEG dataset

The BED dataset Version 1.0.0 Please cite as: Arnau-González, P., Katsigiannis, S., Arevalillo-Herráez, M., Ramzan, N., "BED: A new dataset for EEG-based biometrics", IEEE Internet of Things Journal, vol. 8, no. 15, pp. 12219 - 12230, 2021. Disclaimer While every care has been taken to ensure the accuracy of the data included in the BED dataset, the authors and the University of the West of Scotland, Durham University, and Universitat de València do not provide any guaranties and disclaim all responsibility and all liability (including without limitation, liability in negligence) for all expenses, losses, damages (including indirect or consequential damage) and costs which you might incur as a result of the provided data being inaccurate or incomplete in any way and for any reason. 2020, University of the West of Scotland, Scotland, United Kingdom. Contact For inquiries regarding the BED dataset, please contact: Dr Pablo Arnau-González, arnau.pablo [*AT*] gmail.com Dr Stamos Katsigiannis, stamos.katsigiannis [*AT*] durham.ac.uk Prof. Miguel Arevalillo-Herráez, miguel.arevalillo [*AT*] uv.es Prof. Naeem Ramzan, Naeem.Ramzan [*AT*] uws.ac.uk Dataset summary BED (Biometric EEG Dataset) is a dataset specifically designed to test EEG-based biometric approaches that use relatively inexpensive consumer-grade devices, more specifically the Emotiv EPOC+ in this case. This dataset includes EEG responses from 21 subjects to 12 different stimuli, across 3 different chronologically disjointed sessions. We have also considered stimuli aimed to elicit different affective states, so as to facilitate future research on the influence of emotions on EEG-based biometric tasks. In addition, we provide a baseline performance analysis to outline the potential of consumer-grade EEG devices for subject identification and verification. It must be noted that, in this work, EEG data were acquired in a controlled environment in order to reduce the variability in the acquired data stemming from external conditions. The stimuli include: Images selected to elicit specific emotions Mathematical computations (2-digit additions) Resting-state with eyes closed Resting-state with eyes open Visual Evoked Potentials at 2, 5, 7, 10 Hz - Standard checker-board pattern with pattern reversal Visual Evoked Potentials at 2, 5, 7, 10 Hz - Flashing with a plain colour, set as black For more details regarding the experimental protocol and the design of the dataset, please refer to the associated publication: Arnau-González, P., Katsigiannis, S., Arevalillo-Herráez, M., Ramzan, N., "BED: A new dataset for EEG-based biometrics", IEEE Internet of Things Journal, 2021. (Under review) Dataset structure and contents The BED dataset contains EEG recordings from 21 subjects, acquired during 3 similar sessions for each subject. The sessions were spaced one week apart from each other. The BED dataset includes: The raw EEG recordings with no pre-processing and the log files of the experimental procedure, in text format The EEG recordings with no pre-processing, segmented, structured and annotated according to the presented stimuli, in Matlab format The features extracted from each EEG segment, as described in the associated publication The dataset is organised in 3 folders: RAW RAW_PARSED Features RAW/ Contains the RAW files RAW/sN/ Contains the RAW files associated with subject N Each folder sN is composed by the following files: - sN_s1.csv, sN_s2.csv, sN_s3.csv -- Files containing the EEG recordings for subject N and session 1, 2, and 3, respectively. These files contain 39 columns: COUNTER INTERPOLATED F3 FC5 AF3 F7 T7 P7 O1 O2 P8 T8 F8 AF4 FC6 F4 ...UNUSED DATA... UNIX_TIMESTAMP - subject_N_session_1_time_X.log, subject_N_session_2_time_X.log, subject_N_session_3_time_X.log -- Log files containing the sequence of events for the subject N and the session 1,2, and 3 respectively. RAW_PARSED/ Contains Matlab files named sN_sM.mat. The files contain the recordings for the subject N in the session M. These files are composed by two variables: - recording: size (time@256Hz x 17), Columns: COUNTER INTERPOLATED F3 FC5 AF3 F7 T7 P7 O1 O2 P8 T8 F8 AF4 FC6 F4 UNIX_TIMESTAMP - events: cell array with size (events x 3) START_UNIX END_UNIX ADDITIONAL_INFO START_UNIX is the UNIX timestamp in which the event starts END_UNIX is the UNIX timestamp in which the event ends ADDITIONAL INFO contains a struct with additional information regarding the specific event, in the case of the images, the expected score, the voted score, in the case of the cognitive task the input, in the case of the VEP the pattern and the frequency, etc.. Features/ Features/Identification Features/Identification/[ARRC|MFCC|SPEC]/: Each of these folders contain the extracted features ready for classification for each of the stimuli, each file is composed by two variables, "feat" the feature matrix and "Y" the label matrix. - feat: N x number of features - Y: N x 2 (the #subject and the #session) - INFO: Contains details about the event same as the ADDITIONAL INFO Features/Verification: This folder is composed by 3 different files each of them with one different set of features extracted. Each file is composed by one cstruct array composed by: - data: the time-series features, as described in the paper - y: the #subject - stimuli: the stimuli by name - session: the #session - INFO: Contains details about the event The features provided are in sequential order, so index 1 and index 2, etc. are sequential in time if they belong to the same stimulus. Additional information For additional information regarding the creation of the BED dataset, please refer to the associated publication: Arnau-González, P., Katsigiannis, S., Arevalillo-Herráez, M., Ramzan, N., "BED: A new dataset for EEG-based biometrics", IEEE Internet of Things Journal, vol. 8, no. 15, pp. 12219 - 12230, 2021.BED数据集（BED Dataset）版本1.0.0 引用方式：Arnau-González, P., Katsigiannis, S., Arevalillo-Herráez, M., Ramzan, N., "BED: A new dataset for EEG-based biometrics", IEEE Internet of Things Journal, vol. 8, no. 15, pp. 12219 - 12230, 2021. ## 免责声明 尽管已尽全力确保BED数据集所收录数据的准确性，但作者以及西苏格兰大学、杜伦大学和瓦伦西亚大学不提供任何形式的保证，并声明不承担任何责任与义务（包括但不限于过失责任），不对您因本数据集数据在任何方面以任何原因出现不准确或不完整而可能遭受的一切费用、损失、损害（包括间接损害或后果性损害）及成本承担责任。2020年，西苏格兰大学，苏格兰，英国。 ## 联系方式 如有关于BED数据集的咨询，请联系： 巴勃罗·阿诺-冈萨雷斯博士，邮箱：[e-mail] 斯塔莫斯·卡齐吉安尼斯博士，邮箱：[e-mail] 米格尔·阿雷瓦略-埃雷亚斯教授，邮箱：[e-mail] 纳伊姆·拉姆赞教授，邮箱：[e-mail] ## 数据集摘要 BED（生物特征脑电数据集，Biometric EEG Dataset）是专为测试基于脑电（Electroencephalogram, EEG）的生物识别方法而设计的数据集，此类方法采用相对低成本的消费级设备，本数据集具体使用Emotiv EPOC+设备。数据集包含21名受试者对12种不同刺激的脑电响应，分为3个时间上相互独立的实验会话。本数据集还纳入了旨在诱发不同情绪状态的刺激，以助力未来研究情绪对基于脑电生物识别任务的影响。此外，本数据集提供了基线性能分析，以阐明消费级脑电设备在受试者识别与验证任务中的应用潜力。需说明的是，本研究在受控环境中采集脑电数据，以降低外部环境因素导致的采集数据变异性。 刺激类型包括： 1. 用于诱发特定情绪的图像 2. 数学计算任务（两位数加法） 3. 闭眼静息态 4. 睁眼静息态 5. 2Hz、5Hz、7Hz、10Hz视觉诱发电位（标准棋盘格翻转模式） 6. 2Hz、5Hz、7Hz、10Hz视觉诱发电位（纯色闪烁，底色为黑色） 如需了解实验方案与数据集设计的更多细节，请参阅关联学术论文：Arnau-González, P., Katsigiannis, S., Arevalillo-Herráez, M., Ramzan, N., "BED: A new dataset for EEG-based biometrics", IEEE Internet of Things Journal, 2021.（该论文彼时处于审稿阶段） ## 数据集结构与内容 BED数据集包含21名受试者的脑电记录，每名受试者完成3次相同的实验会话，会话间隔为一周。BED数据集包含以下内容： 1. 未经过预处理的原始脑电记录与实验流程日志文件（文本格式） 2. 未经过预处理、已按刺激类型分段、结构化并标注的脑电记录（Matlab格式） 3. 从每段脑电片段中提取的特征（详见关联论文） 本数据集分为3个文件夹：RAW、RAW_PARSED、Features。 ### RAW/ 该文件夹存放原始文件，RAW/sN/ 存放与受试者N相关的原始文件。每个sN文件夹包含以下文件： - sN_s1.csv、sN_s2.csv、sN_s3.csv：分别为受试者N在第1、2、3次会话的脑电记录文件，包含39列数据：COUNTER、INTERPOLATED、F3、FC5、AF3、F7、T7、P7、O1、O2、P8、T8、F8、AF4、FC6、F4……UNUSED DATA……UNIX_TIMESTAMP - subject_N_session_1_time_X.log、subject_N_session_2_time_X.log、subject_N_session_3_time_X.log：分别为受试者N在第1、2、3次会话的事件序列日志文件。 ### RAW_PARSED/ 该文件夹存放名为sN_sM.mat的Matlab文件，包含受试者N在第M次会话的脑电记录。此类文件包含两个变量： - recording：维度为（采样时间@256Hz ×17）的矩阵，列依次为COUNTER、INTERPOLATED、F3、FC5、AF3、F7、T7、P7、O1、O2、P8、T8、F8、AF4、FC6、F4、UNIX_TIMESTAMP - events：维度为（事件数×3）的元胞数组，各列分别为： - START_UNIX：事件开始的UNIX时间戳 - END_UNIX：事件结束的UNIX时间戳 - ADDITIONAL_INFO：包含事件额外信息的结构体，例如图像任务的预期评分、投票评分；认知任务的输入内容；视觉诱发电位的模式与频率等。 ### Features/ #### Features/Identification/[ARRC|MFCC|SPEC]/ 每个子文件夹包含针对各类刺激提取的、可直接用于分类的特征，每个文件包含两个变量： - feat：N×特征数量 的特征矩阵 - Y：N×2 的标签矩阵，分别对应受试者编号与会话编号 - INFO：包含与ADDITIONAL_INFO一致的事件详细信息 #### Features/Verification/ 该文件夹包含3个不同的特征集文件，每个文件包含一个结构体数组，字段如下： - data：时序特征（详见论文） - y：受试者编号 - stimuli：刺激类型名称 - session：会话编号 - INFO：包含事件详细信息 所有特征按时间顺序排列，若属于同一刺激，则索引1、2等在时间上连续。 ## 附加信息 如需了解BED数据集构建的更多信息，请参阅关联学术论文：Arnau-González, P., Katsigiannis, S., Arevalillo-Herráez, M., Ramzan, N., "BED: A new dataset for EEG-based biometrics", IEEE Internet of Things Journal, vol. 8, no. 15, pp. 12219 - 12230, 2021.

搜集汇总

背景与挑战

背景概述

BED数据集是一个专门设计用于测试基于EEG生物识别的数据集，包含21名受试者在3个不同会话中对12种刺激的EEG记录，使用消费级设备Emotiv EPOC+采集。该数据集提供了原始数据、分段注释数据和提取的特征，旨在评估消费级EEG设备在身份识别和验证中的潜力，并考虑了情感刺激对生物识别任务的影响。