MAMEM Phase I Dataset - A dataset for multimodal human-computer interaction using biosignals and eye tracking information

This dataset combines multimodal biosignals and eye tracking information gathered under a human-computer interaction framework. The dataset was developed in the vein of the MAMEM project that aims to endow people with motor disabilities with the ability to edit and author multimedia content through mental commands and gaze activity. The dataset includes EEG, eye-tracking, and physiological (GSR and Heart rate) signals along with demographic, clinical and behavioral data collected from 36 individuals (18 able-bodied and 18 motor-impaired). Data were collected during the interaction with specifically designed interface for web browsing and multimedia content manipulation and during imaginary movement tasks. Alongside these data we also include evaluation reports both from the subjects and the experimenters as far as the experimental procedure and collected dataset are concerned. We believe that the presented dataset will contribute towards the development and evaluation of modern human-computer interaction systems that would foster the integration of people with severe motor impairments back into society.<br>Please use the following citation: Nikolopoulos, Spiros, Georgiadis, Kostas, Kalaganis, Fotis, Liaros, Georgios, Lazarou, Ioulietta, Adam, Katerina, Papazoglou – Chalikias, Anastasios, Chatzilari, Elisavet , Oikonomou, Vangelis P., Petrantonakis, Panagiotis C., Kompatsiaris, Ioannis, Kumar, Chandan, Menges, Raphael, Staab, Steffen, Müller, Daniel, Sengupta, Korok, Bostantjopoulou, Sevasti, Zoe, Katsarou , Zeilig, Gabi, Plotnik, Meir, Gottlieb, Amihai, Fountoukidou, Sofia, Ham, Jaap, Athanasiou, Dimitrios, Mariakaki, Agnes, Comanducci, Dario, Sabatini, Edoardo, Nistico, Walter &amp; Plank, Markus. (2017). The MAMEM Project - A dataset for multimodal human-computer interaction using biosignals and eye tracking information. Zenodo. http://doi.org/10.5281/zenodo.834154<br>Read/analyze using the following software:https://github.com/MAMEM/eeg-processing-toolbox<br><br>本数据集整合了在人机交互框架下采集的多模态生物信号与眼动追踪信息。本数据集依托MAMEM项目框架开发，该项目旨在帮助运动障碍人群通过意念指令与注视操作完成多媒体内容的编辑与创作。本数据集包含从36名受试者（18名健康个体与18名运动障碍患者）采集得到的脑电图（Electroencephalogram，EEG）、眼动追踪信号、生理信号（皮肤电反应（Galvanic Skin Response，GSR）与心率（Heart Rate）），以及人口统计学、临床与行为学数据。数据采集场景包括受试者与专为网页浏览及多媒体内容操作设计的交互界面进行交互的过程，以及想象运动任务过程。此外，本数据集还包含受试者与实验者针对实验流程与采集所得数据集的评估报告。我们相信，本数据集将助力现代人机交互系统的研发与评估，进而推动重度运动障碍人群重新融入社会。 请采用以下引用格式： Nikolopoulos, Spiros、Georgiadis, Kostas、Kalaganis, Fotis、Liaros, Georgios、Lazarou, Ioulietta、Adam, Katerina、Papazoglou–Chalikias, Anastasios、Chatzilari, Elisavet、Oikonomou, Vangelis P.、Petrantonakis, Panagiotis C.、Kompatsiaris, Ioannis、Kumar, Chandan、Menges, Raphael、Staab, Steffen、Müller, Daniel、Sengupta, Korok、Bostantjopoulou, Sevasti、Zoe, Katsarou、Zeilig, Gabi、Plotnik, Meir、Gottlieb, Amihai、Fountoukidou, Sofia、Ham, Jaap、Athanasiou, Dimitrios、Mariakaki, Agnes、Comanducci, Dario、Sabatini, Edoardo、Nistico, Walter 与 Plank, Markus. (2017). MAMEM项目——基于生物信号与眼动追踪信息的多模态人机交互数据集. Zenodo. https://doi.org/10.5281/zenodo.834154 可通过以下软件读取与分析本数据集：https://github.com/MAMEM/eeg-processing-toolbox