Benchmark EEG data set for trust assessment for interactions with social robots

The data collection consisted of a game interaction with a small humanoid EZ-robot. The robot explains a word to the participant either through movements depicting the concept or by verbal description. Depending on their performance, participants could "earn" or loose candy as remuneration for their participation. The dataset comprises EEG (Electroencephalography) recordings from 21 participants, gathered using Emotiv headsets. Each participant's EEG data includes timestamps and measurements from 14 sensors placed across different regions of the scalp. The sensor labels in the header are as follows: EEG.AF3, EEG.F7, EEG.F3, EEG.FC5, EEG.T7, EEG.P7, EEG.O1, EEG.O2, EEG.P8, EEG.T8, EEG.FC6, EEG.F4, EEG.F8, EEG.AF4, and Time. The EEG data provides insights into the electrical activity of the brain, offering a window into cognitive processes and emotional responses during various activities or stimuli in the form of microvolt and with a frame rate of 128 Hz. The whole data set consists of 3651124 data points for each sensor, i.e. 173863 on average for each participant (min. 128505, max. 249631). Files are named after participant numbers starting with ID01. The data has to be pre-processed making use of the information given in the details.xlsx file that contains annotations corresponding to the EEG recordings. These annotations denote the timing of different phases related to trust across the participants' interactions. Each phase is delineated by a start time and an end time, representing distinct stages of the trust-building process. All the other data (timestamps) which are outside the start and end of each phase should be considered as breaks, e.g. filling out the questionnaires. The last element is the trust score for the given phase, which is calculated on the answers in an MDMT questionnaire. The following phases have been annotated: Trust Building: This phase involves friendly initial interactions for establishing trust between participants and the robot. Situational Awareness: This phase continues to build up trust by showing situation awareness of the robot, e.g. by complimenting on the participant's fashion choice. Transparency: Trust is maintained by increased openness and clarity in communicating about the robot's abilities. Trust Violation: Trust is compromised during this phase by deliberately misleading the participant and making it impossible to answer correctly. Trust Repair: The robot shows efforts to repair trust by apologizing for the behavior in the previous stage. If you work with the data, please cite one of the article given below.本数据集采集自参与者与小型类人EZ-机器人的游戏交互场景。机器人可通过动作演示概念或口头描述两种方式，向参与者讲解词汇。参与者可根据自身任务表现，获得或丢失糖果作为参与报酬。 本数据集包含21名参与者的脑电（Electroencephalography，EEG）记录数据，采集设备为Emotiv头戴式脑电采集仪。每名参与者的脑电数据包含时间戳信息，以及覆盖头皮不同脑区的14个传感器的测量值。头部文件中的传感器标签依次为：EEG.AF3、EEG.F7、EEG.F3、EEG.FC5、EEG.T7、EEG.P7、EEG.O1、EEG.O2、EEG.P8、EEG.T8、EEG.FC6、EEG.F4、EEG.F8、EEG.AF4以及Time。 脑电数据以微伏为单位，采样帧率为128Hz，可反映大脑的电活动，为了解参与者在各类任务或刺激下的认知过程与情绪反应提供观测窗口。全数据集单传感器共包含3651124个数据点，即每名参与者平均对应173863个数据点（最少128505个，最多249631个）。 数据文件以参与者编号命名，前缀为ID01。需借助details.xlsx文件中的信息对数据进行预处理，该文件包含与脑电记录对应的标注信息。这些标注用于标记参与者交互过程中与信任建立相关的不同阶段的时间节点，每个阶段由起始时间与结束时间界定，代表信任构建流程中的不同环节。所有处于各阶段起止时间之外的时间戳数据均视为休息时段，例如填写问卷阶段。最后一项数据为对应阶段的信任得分，该得分基于MDMT问卷的作答结果计算得出。 已标注的阶段如下： 1. 信任构建（Trust Building）：该阶段包含友好的初始交互，用于建立参与者与机器人之间的信任关系。 2. 情境感知（Situational Awareness）：该阶段通过展现机器人的情境感知能力（例如称赞参与者的穿搭）进一步建立信任。 3. 透明度（Transparency）：通过在沟通中提升机器人能力的开放性与清晰度，维持现有信任水平。 4. 信任违背（Trust Violation）：该阶段通过故意误导参与者、使其无法正确作答的方式，破坏已建立的信任。 5. 信任修复（Trust Repair）：机器人就前一阶段的行为致歉，展现出修复信任的努力。 若使用本数据集开展研究，请引用下述任意一篇相关论文。