Essex EEG Movie Memory dataset
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# Essex EEG Movie Memory Dataset Authors: Ana Matran-Fernandez and Sebastian Halder ### Description This dataset contains raw electroencephalography (EEG) signals recorded from 27 participants while watching 10-second long clips extracted from movies that they had previously watched. For each clip, participants were asked whether they recognised the movie it belonged to, and if so, whether they remembered having watched it previously or not. If a participant reported recognising or remembering a clip, it was shown a second time to capture (via a mouse click) time annotations of the instants that prompted this recognition. ### EEG EEG data were acquired with a BioSemi ActiveTwo system with 64 electrodes positioned according to the international 10-20 system. The sampling rate was 2048 Hz. ### Stimuli The clips used in the study were originally annotated in terms of their memorability by Cohendet et al (see References). This dataset can be requested from the authors. ### Example code We have prepared an example script to demonstrate how to load the EEG data into Python using MNE and MNE-BIDS packages. This script is located in the 'code' directory. ### References Romain Cohendet, Karthik Yadati, Ngoc Q. K. Duong, and Claire-Hélène Demarty. 2018. Annotating, Understanding, and Predicting Long-term Video Memorability. In Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval (ICMR '18). Association for Computing Machinery, New York, NY, USA, 178–186. https://doi.org/10.1145/3206025.3206056 References ---------- Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896).https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103.https://doi.org/10.1038/s41597-019-0104-8
# 埃塞克斯脑电电影记忆数据集（Essex EEG Movie Memory Dataset） 作者：Ana Matran-Fernandez与Sebastian Halder ### 数据集说明 本数据集包含27名被试观看此前已观看过的电影所提取的10秒片段时，采集得到的原始脑电图（electroencephalography, EEG）信号。针对每个片段，研究人员会向被试询问是否认出该片段所属的电影；若被试认出，则进一步询问其是否记得此前曾观看过该电影。若被试报告认出或记得某一片段，则会再次播放该片段，以通过鼠标点击记录下触发该识别或记忆效应的时刻的时间标注。 ### 脑电采集参数 脑电数据采用BioSemi ActiveTwo系统采集，共布设64个电极，依照国际10-20系统（international 10-20 system）定位，采样率为2048 Hz。 ### 刺激材料 本研究使用的片段最初由Cohendet等人依据视频记忆性进行标注（详见参考文献）。本数据集可向作者申请获取。 ### 示例代码 我们已编写示例脚本，用于演示如何借助MNE与MNE-BIDS软件包将脑电数据加载至Python环境中。该脚本位于`code`目录下。 ### 参考文献 Romain Cohendet、Karthik Yadati、Ngoc Q. K. Duong与Claire-Hélène Demarty. 2018. 标注、理解与预测长期视频记忆性. 见：2018年ACM多媒体检索国际会议（ICMR '18）论文集. 美国计算机协会，美国纽约州纽约市，178–186. https://doi.org/10.1145/3206025.3206056 参考文献 ---------- Appelhoff, S.、Sanderson, M.、Brooks, T.、Vliet, M.、Quentin, R.、Holdgraf, C.、Chaumon, M.、Mikulan, E.、Tavabi, K.、Höchenberger, R.、Welke, D.、Brunner, C.、Rockhill, A.、Larson, E.、Gramfort, A.与Jas, M.（2019）. MNE-BIDS：将脑电数据整理为BIDS格式并助力其分析. 开源软件期刊，第4卷：(1896). https://doi.org/10.21105/joss.01896 Pernet, C. R.、Appelhoff, S.、Gorgolewski, K. J.、Flandin, G.、Phillips, C.、Delorme, A.、Oostenveld, R.（2019）. EEG-BIDS：面向脑电图领域的脑成像数据结构扩展. 科学数据，第6卷，103. https://doi.org/10.1038/s41597-019-0104-8
