EEG Error Related Potential signal using an atari-based maze game - BCI ErrP Dataset

This dataset contains EEG error-related potential signals elicited by humans while observing an AI agent play an atari-based maze game. We designed and developed an experimental protocol, where a machine agent plays a computer game, while a human silently observes (and assesses) the actions taken by the machine agent. These implicit human reactions are captured by placing raw electrodes on the scalp of the human brain in the form of EEG potentials. The electrode cap (BIOPAC CAP-100C) was attached with the OpenBCI Cyton platform, which was further connected to a desktop machine over the wireless channel. We used OpenViBE software to record the human EEG data. We recruited a total of 12 human subjects (mean age 26.8 with standard deviation of 1.92, 4 female) using standard procedures with their consent. For each subject-game pair, the experimental duration was less than 15 minutes. The agent took action every 1.5 seconds during the experiment and made an incorrect move with the probability of 0.2. The implicit brain response from the subjects was then used to accelerate the learning rate of a reinforcement learning agent [1]. For a more detailed description of the study and the results, please refer to [1] Game description: Maze is a 2-D navigational game, where the agent has to reach a fixed target (shown with a plus symbol). The screen is divided into 10x10 square blocks. The action space consists of four directional movements. The only reward here is the result of the episode, i.e., win or lose. If an agent moves, but hits a wall, a quick blinking of the agent is displayed, to render the action taken by the agent. References: [1] Xu, Duo, Mohit Agarwal, Ekansh Gupta, Faramarz Fekri, and Raghupathy Sivakumar. "Accelerating Reinforcement Learning using EEG-based implicit human feedback." Neurocomputing 460 (2021): 139-153. Acknowledgments: This work was supported in part by the National Science Foundation under grants CPS-1837369 and the Wayne J. Holman Endowed Chair.

本数据集收录了人类在观察人工智能代理进行基于 Atari 游戏的迷宫游戏时引发的 EEG 错误相关电位信号。本研究团队精心设计并开发了实验方案，其中机器代理参与计算机游戏，而人类则进行无声的观察（并评估）机器代理所采取的行动。通过在人类大脑头皮上放置原始电极，捕捉到这些人类的无意识反应，形成 EEG 电位。电极帽（BIOPAC CAP-100C）连接至 OpenBCI Cyton 平台，该平台随后通过无线信道与桌面机器相连。本研究使用 OpenViBE 软件记录人类 EEG 数据。招募了共计 12 名人类受试者（平均年龄 26.8 岁，标准差为 1.92，其中女性 4 名），并按照标准程序获得其同意。对于每位受试者与游戏配对，实验时长不超过 15 分钟。代理在实验过程中每 1.5 秒执行一次动作，以 0.2 的概率执行错误动作。随后，受试者的无意识大脑反应被用于加速强化学习代理的学习率 [1]。关于研究及其结果的更详细信息，请参阅 [1]。游戏描述：迷宫是一款二维导航游戏，代理必须达到一个固定的目标（以加号表示）。屏幕被划分为 10x10 的方块。唯一的奖励是游戏回合的结局，即胜利或失败。若代理移动时触碰到墙壁，屏幕上将显示代理快速闪烁，以显示代理所采取的行动。参考文献：[1] Xu, Duo, Mohit Agarwal, Ekansh Gupta, Faramarz Fekri, and Raghupathy Sivakumar. "加速使用基于 EEG 的无意识人类反馈的强化学习." Neurocomputing 460 (2021): 139-153. 致谢：本研究部分资金由美国国家科学基金会提供，项目编号 CPS-1837369，以及 Wayne J. Holman 捐赠基金支持。