A cross-session motor imagery EEG dataset

# Pan2023 Dataset Documentation ## Abstract The Pan2023 dataset is a collection of electroencephalography (EEG) signals from 14 subjects performing motor imagery (MI) tasks across two sessions. The dataset aims to facilitate the study of cross-session variability in MI-EEG signals and to support the development of robust brain-computer interface (BCI) systems. ## Dataset Composition The dataset encompasses EEG recordings from 14 subjects, each participating in two sessions. The sessions involve MI tasks with visual cues for left-handed and right-handed movements. Data acquisition was performed using a Neuroscan SynAmps2 amplifier, equipped with 28 scalp electrodes following the international 10-20 system. The EEG signals were sampled at a frequency of 250Hz, with a band-pass filter applied from 0.01 to 200Hz to mitigate power line noise. The collected data is stored in Matlab format, labeled by subject and session number. ## Participants The participant cohort includes 14 individuals (five females), aged 22 to 25, with two reporting left-handedness. All subjects were screened for neurological and movement disorders, ensuring a healthy participant profile for the study. ## Experimental Paradigm Each experimental session comprised 120 trials, segmented into three distinct phases: Rest, Preparation, and Task. During the Rest Period (2 seconds), subjects were instructed to remain relaxed without engaging in mental tasks. The Preparation Period (1 second) involved a 'Ready' cue on the monitor, prompting subjects to focus and prepare for the upcoming MI task. The Task Period (4 seconds) required subjects to perform the MI task, visualizing the movement corresponding to the provided cues, either left or right-handed. This paradigm was designed to occur in a controlled, distraction-free environment. ## Data Acquisition and Preprocessing EEG signals were captured using a Neuroscan SynAmps2 amplifier and 28 scalp electrodes positioned per the 10-20 system. The sampling rate was set at 250Hz, and a band-pass filter from 0.01 to 200Hz was employed to exclude power line interference. The signals were archived in Matlab format, systematically named by subject and session identifiers. ## Data Structure The dataset's structure is encapsulated in a Matlab file, comprising a struct with the following components: - `data`: A 3D matrix (`[n_trials, n_channels, n_samples]`) containing the EEG signals. - `label`: A vector (`[n_trials]`) denoting each trial's label (1 for left-handed, 2 for right-handed movement). - `trial_info`: A struct detailing each trial's phase (1 for Rest, 2 for Preparation, 3 for Task), the visual cue (1 for left-handed, 2 for right-handed movement), and the subject's identifier.

# Pan2023数据集文档
## 摘要
本Pan2023数据集收录了14名受试者完成运动想象（motor imagery, MI）任务时采集的脑电图（electroencephalography, EEG）信号，实验共包含两次实验会话。本数据集旨在助力运动想象脑电图（MI-EEG）信号跨会话变异性的相关研究，并为鲁棒性脑机接口（brain-computer interface, BCI）系统的开发提供支撑。
## 数据集构成
本数据集包含14名受试者的脑电图记录，每名受试者参与两次实验会话。会话中受试者需完成伴随视觉提示的左右手运动想象任务。数据采集采用Neuroscan SynAmps2放大器，搭配28个按照国际10-20系统放置的头皮电极。脑电图信号采样率为250Hz，施加0.01~200Hz的带通滤波器以抑制工频噪声。采集所得数据以Matlab格式存储，按受试者与会话编号进行命名标注。
## 受试者群体
本次实验的受试者共14人（其中5名女性），年龄介于22至25岁之间，其中2人自述为左利手。所有受试者均经过神经与运动功能障碍筛查，确保参与本研究的受试者均为健康人群。
## 实验范式
每一次实验会话包含120个试次，分为三个明确阶段：休息期、准备期与任务期。休息期（时长2秒）：要求受试者保持放松，不得进行任何心理活动。准备期（时长1秒）：屏幕上将显示“准备就绪”提示，引导受试者集中注意力，为即将开展的运动想象任务做好准备。任务期（时长4秒）：受试者需根据视觉提示完成对应手的运动想象，即想象左手或右手的动作。本实验范式设置于受控且无干扰的环境中执行。
## 数据采集与预处理
脑电图信号采用Neuroscan SynAmps2放大器与按照10-20系统布局的28个头皮电极采集。采样率设置为250Hz，施加0.01~200Hz的带通滤波器以排除工频干扰。信号以Matlab格式归档，按受试者与会话标识符进行系统性命名。
## 数据结构
本数据集封装于Matlab文件中，包含以下结构体组件：
- `data`：三维矩阵（维度为`[n_trials, n_channels, n_samples]`），存储脑电图信号。
- `label`：一维向量（维度为`[n_trials]`），标注每个试次的类别（1代表左手运动想象，2代表右手运动想象）。
- `trial_info`：结构体，包含每个试次的阶段信息（1代表休息期，2代表准备期，3代表任务期）、视觉提示类型（1代表左手提示，2代表右手提示）以及受试者标识符。