Brain Invaders 2013a

P300 dataset bi2013a from a “Brain Invaders” experiment (2013) carried-out at University of Grenoble Alpes. Simple Python scripts for working with the dataset are available at https://github.com/plcrodrigues/BrainInvaders-2013a-Dataset <strong>Dataset Description</strong> This dataset concerns an experiment carried out at GIPSA-lab (University of Grenoble Alpes, CNRS, Grenoble-INP) in 2013. Principal Investigators: Erwan Vaineau, Dr. Alexandre Barachant<br> Scientific Supervisor : Dr. Marco Congedo<br> Technical Supervisor : Anton Andreev The experiment uses the <strong><em>Brain Invaders</em></strong> P300-based Brain-Computer Interface [7], which uses the Open-ViBE platform for on-line EEG data acquisition and processing [1, 9]. For classification purposes the Brain Invaders implements on-line Riemannian MDM classifiers [2, 3, 4, 6]. This experiment features both a training-test (classical) mode of operation and a calibration-less mode of operation [4, 5, 6]. The recordings concerned 24 subjects in total. Subjects 1 to 7 participated to eight sessions, run in different days, subject 8 to 24 participated to one session. Each session consisted in <em>two runs</em>, one in a <em>Non-Adaptive</em> (classical) and one in an <em>Adaptive</em> (calibration-less) mode of operation. The order of the runs was randomized for each session. In both runs there was a <em>Training (calibration) phase</em> and an <em>Online phase</em>, always passed in this order. In the non-Adaptive run the data from the Training phase was used for classifying the trials on the Online phase using the training-test version of the MDM algorithm [3, 4]. In the Adaptive run, the data from the training phase was not used at all, instead the classifier was initialized with generic class geometric means and continuously adapted to the incoming data using the Riemannian method explained in [4]. Subjects were completely blind to the mode of operation and the two runs appeared to them identical. In the Brain Invaders P300 paradigm, a <em>repetition</em> is composed of 12 flashes, of which 2 include the Target symbol (<em>Target</em> flashes) and 10 do not (<em>non-Target</em> flash). Please see [7] for a description of the paradigm. For this experiment, in the Training phases the number of flashes is fixed (80 Target flashes and 400 non-Target flashes). In the Online phases the number of Target and non-Target still are in a ratio 1/5, however their number is variable because the Brain Invaders works with a fixed number of game levels, however the number of repetitions needed to destroy the target (hence to proceed to the next level) depends on the user’s performance [4, 5]. In any case, since the classes are unbalanced, an appropriate score must be used for quantifying the performance of classification methods (e.g., balanced accuracy, AUC methods, etc). This database has been used in the development of the common spatio-temporal pattern method for estimating ERPs [8]. Data were acquired with a Nexus (TMSi, The Netherlands) EEG amplifier: Sampling frequency: 512 samples per second Digital Filter: No Electrodes: 16 wet Silver/Silver Chloride electrodes positioned at FP1, FP2, F5, AFz, F6, T7, Cz, T8, P7, P3, Pz, P4, P8, O1, Oz, O2 according to the 10/20 international system Reference: left ear-lobe Ground: N/A The data for Subject X is available as a subjectX.zip file. In it, there is a folder for each Session that the Subject performed. In each Session's folder, there are four .gdf files, one for each Run performed in the Session. The names of the files and the conditions associated to them are available below and also inside the meta.yml file in the .zip file. - filename: '1.gdf'<br> experimental_condition: adaptive<br> type: training<br> - filename: '2.gdf'<br> experimental_condition: adaptive<br> type: online<br> - filename: '3.gdf'<br> experimental_condition: nonadaptive<br> type: training<br> - filename: '4.gdf'<br> experimental_condition: nonadaptive<br> type: online <strong><em>References</em></strong> [1] Arrouët C, Congedo M, Marvie J-E, Lamarche F, Lècuyer A, Arnaldi B (2005) Open-ViBE: a 3D Platform for Real-Time Neuroscience. Journal of Neurotherapy, 9(1), 3-25. people.rennes.inria.fr/Anatole.Lecuyer/Open-ViBE.pdf) [2] Barachant A, Bonnet S, Congedo M, Jutten C (2013) Classification of covariance matrices using a Riemannian-based kernel for BCI applications. Neurocomputing 112, 172-178. (hal.archives-ouvertes.fr/hal-00820475/document) [3] Barachant A, Bonnet S, Congedo M, Jutten C (2012) Multi-Class Brain Computer Interface Classification by Riemannian Geometry. IEEE Transactions on Biomedical Engineering 59(4), 920-928. (hal.archives-ouvertes.fr/hal-00681328/document) [4] Barachant A, Congedo M (2014) A Plug &amp; Play P300 BCI using Information Geometry, arXiv:1409.0107. (https://arxiv.org/pdf/1409.0107.pdf) [5] Congedo M, Barachant A, Andreev A (2013) A New Generation of Brain-Computer Interface Based on Riemannian Geometry. arXiv:1310.8115 (arxiv.org/ftp/arxiv/papers/1310/1310.8115.pdf) [6] Congedo M, Barachant A, Bhatia R (2017) Riemannian Geometry for EEG-based Brain-Computer Interfaces; a Primer and a Review. Brain-Computer Interfaces, 4(3), 155-174. (hal.archives-ouvertes.fr/hal-01570120/document) [7] Congedo M, Goyat M, Tarrin N, Ionescu G, Rivet B,Varnet L, Rivet B, Phlypo R, Jrad N, Acquadro M, Jutten C (2011) “Brain Invaders”: a prototype of an open-source P300-based video game working with the OpenViBE platform. Proc. IBCI Conf., Graz, Austria, 280-283. (hal.archives-ouvertes.fr/hal-00641412/document) [8] Congedo M, Korczowski L, Delorme A, Lopes da Silva F. (2016) Qpatio-temporal common pattern: A companion method for ERP analysis in the time domain. Journal of Neuroscience Methods, 267, 74-88. (hal.archives-ouvertes.fr/hal-01343026/document) [9] Renard Y, Lotte F, Gibert G, Congedo M, Maby E, Delannoy V, Bertrand O, Lécuyer A (2010) OpenViBE: An Open-Source Software Platform to Design, Test and Use Brain-Computer Interfaces in Real and Virtual Environments. PRESENCE : Teleoperators and Virtual Environments 19(1), 35-53. (hal.archives-ouvertes.fr/hal-00477153/document)P300数据集bi2013a源自格勒诺布尔阿尔卑斯大学开展的“脑入侵者（Brain Invaders）”2013年实验。 可在https://github.com/plcrodrigues/BrainInvaders-2013a-Dataset 获取用于处理该数据集的简易Python脚本。 数据集说明 本数据集对应2013年在格勒诺布尔阿尔卑斯大学、法国国家科研中心（CNRS）、格勒诺布尔国立理工学院下属GIPSA实验室开展的一项实验。 主要研究者：埃尔万·瓦诺（Erwan Vaineau）、亚历山大·巴拉尚博士（Dr. Alexandre Barachant） 科学导师：马可·孔塞多博士（Dr. Marco Congedo） 技术导师：安东·安德烈耶夫（Anton Andreev） 本实验采用基于P300的脑机接口（Brain-Computer Interface, BCI）“脑入侵者”[7]，该系统依托Open-ViBE平台实现在线脑电图（EEG）数据的采集与处理[1, 9]。为实现分类任务，“脑入侵者”集成了在线黎曼MDM分类器[2, 3, 4, 6]。本实验同时支持训练-测试（经典）模式与无校准模式两种运行模式[4, 5, 6]。 本次实验共招募24名受试者：第1至7名受试者分别在不同日期完成8次实验单元，第8至24名受试者仅完成1次实验单元。每次实验单元包含两轮实验，一轮为非自适应（经典）模式，另一轮为自适应（无校准）模式。每轮实验的顺序在各实验单元中均为随机排列。两轮实验均包含训练（校准）阶段与在线阶段，且执行顺序固定为先训练后在线。 在非自适应实验轮次中，研究人员将训练阶段采集的数据用于在线阶段的试次分类，采用MDM算法的训练-测试版本[3, 4]。在自适应实验轮次中，训练阶段的数据完全不被使用，分类器初始化为通用类别几何均值，并通过文献[4]中所述的黎曼方法持续适配传入的实时数据。受试者对实验模式完全不知情，两轮实验在他们看来完全一致。 在“脑入侵者”的P300范式中，一次重复包含12次闪光，其中2次为包含目标符号的目标闪光，剩余10次为非目标闪光。该范式的详细说明请参见文献[7]。本实验的训练阶段中，闪光总数固定（目标闪光80次，非目标闪光400次）。在线阶段中，目标闪光与非目标闪光的比例仍为1:5，但具体数量不固定：这是因为“脑入侵者”采用固定数量的游戏关卡，而击毁目标所需的重复次数（进而进入下一关卡）取决于使用者的操作表现[4, 5]。由于两类样本不平衡，因此需采用合适的指标量化分类方法的性能（如平衡准确率、AUC方法等）。 本数据集已被用于开发用于事件相关电位（Event-Related Potential, ERP）估计的时空公共模式方法[8]。 数据通过荷兰TMSi公司的Nexus脑电图放大器采集： - 采样频率：512样本/秒 - 数字滤波：无 - 电极：按照国际10/20系统放置的16个湿式银/氯化银电极，位置分别为FP1、FP2、F5、AFz、F6、T7、Cz、T8、P7、P3、Pz、P4、P8、O1、Oz、O2 - 参考电极：左耳廓 - 接地电极：无 受试者X的数据以subjectX.zip文件形式提供。该压缩包内包含该受试者所有实验单元对应的文件夹，每个实验单元文件夹内包含4个.gdf格式文件，分别对应该实验单元中的两轮实验。各文件的名称与对应实验条件如下文及压缩包内的meta.yml文件所述。 - 文件名：'1.gdf' 实验条件：自适应 类型：训练 - 文件名：'2.gdf' 实验条件：自适应 类型：在线 - 文件名：'3.gdf' 实验条件：非自适应 类型：训练 - 文件名：'4.gdf' 实验条件：非自适应 类型：在线 参考文献 [1] Arrouët C, Congedo M, Marvie J-E, Lamarche F, Lècuyer A, Arnaldi B (2005) Open-ViBE: a 3D Platform for Real-Time Neuroscience. Journal of Neurotherapy, 9(1), 3-25. people.rennes.inria.fr/Anatole.Lecuyer/Open-ViBE.pdf) [2] Barachant A, Bonnet S, Congedo M, Jutten C (2013) Classification of covariance matrices using a Riemannian-based kernel for BCI applications. Neurocomputing 112, 172-178. (hal.archives-ouvertes.fr/hal-00820475/document) [3] Barachant A, Bonnet S, Congedo M, Jutten C (2012) Multi-Class Brain Computer Interface Classification by Riemannian Geometry. IEEE Transactions on Biomedical Engineering 59(4), 920-928. (hal.archives-ouvertes.fr/hal-00681328/document) [4] Barachant A, Congedo M (2014) A Plug & Play P300 BCI using Information Geometry, arXiv:1409.0107. (https://arxiv.org/pdf/1409.0107.pdf) [5] Congedo M, Barachant A, Andreev A (2013) A New Generation of Brain-Computer Interface Based on Riemannian Geometry. arXiv:1310.8115 (arxiv.org/ftp/arxiv/papers/1310/1310.8115.pdf) [6] Congedo M, Barachant A, Bhatia R (2017) Riemannian Geometry for EEG-based Brain-Computer Interfaces; a Primer and a Review. Brain-Computer Interfaces, 4(3), 155-174. (hal.archives-ouvertes.fr/hal-01570120/document) [7] Congedo M, Goyat M, Tarrin N, Ionescu G, Rivet B,Varnet L, Rivet B, Phlypo R, Jrad N, Acquadro M, Jutten C (2011) “Brain Invaders”: a prototype of an open-source P300-based video game working with the OpenViBE platform. Proc. IBCI Conf., Graz, Austria, 280-283. (hal.archives-ouvertes.fr/hal-00641412/document) [8] Congedo M, Korczowski L, Delorme A, Lopes da Silva F. (2016) Qpatio-temporal common pattern: A companion method for ERP analysis in the time domain. Journal of Neuroscience Methods, 267, 74-88. (hal.archives-ouvertes.fr/hal-01343026/document) [9] Renard Y, Lotte F, Gibert G, Congedo M, Maby E, Delannoy V, Bertrand O, Lécuyer A (2010) OpenViBE: An Open-Source Software Platform to Design, Test and Use Brain-Computer Interfaces in Real and Virtual Environments. PRESENCE : Teleoperators and Virtual Environments 19(1), 35-53. (hal.archives-ouvertes.fr/hal-00477153/document)