SSVEP-EEG data collection using Emotiv EPOC

The data acquisition process begins with capturing EEG signals from 20 healthy skilled volunteers who gave their written consent before performing the experiments. Each volunteer was asked to repeat an experiment for 10 times at different frequencies; each experiment was trigger by a visual stimulus.Each volunteer performed an experiment for each of the 10 visual stimuli frequencies (7, 9, 11 and 13). In each experiment the EEG signals generated in the 2 electrodes (LO, RO) of the occipital area was simultaneously recorded. It is important to note that the data acquisition equipment has a sampling rate of 128 samples per second, allowing to acquire 2500 samples, considering that each task has a duration of 19.5 seconds.⭐ When using this resource, please cite the original publication:Asanza, V., Avilés-Mendoza, K., Trivino-Gonzalez, H., Rosales-Uribe, F., Torres-Brunes, J., Loayza, F. R., ... & Tinoco-Egas, R. (2021). SSVEP-EEG Signal Classification based on Emotiv EPOC BCI and Raspberry Pi. IFAC-PapersOnLine, 54(15), 388-393.References:J. Fuentes-Gonzalez, A. Infante-Alarcón, V. Asanza and F. R. Loayza, "A 3D-Printed EEG based Prosthetic Arm," 2020 IEEE International Conference on E-health Networking, Application & Services (HEALTHCOM), Shenzhen, China, 2021, pp. 1-5, doi: 10.1109/HEALTHCOM49281.2021.9399035.V. Asanza, A. Constantine, S. Valarezo and E. Peláez, "Implementation of a Classification System of EEG Signals Based on FPGA," 2020 Seventh International Conference on eDemocracy & eGovernment (ICEDEG), Buenos Aires, Argentina, 2020, pp. 87-92, doi: 10.1109/ICEDEG48599.2020.9096752.C. Cedeño Z., J. Cordova-Garcia, V. Asanza A., R. Ponguillo and L. Muñoz M., "k-NN-Based EMG Recognition for Gestures Communication with Limited Hardware Resources," 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Leicester, United Kingdom, 2019, pp. 812-817.Asanza, V., Peláez, E., Loayza, F., Mesa, I., Díaz, J., & Valarezo, E. (2018, October). EMG Signal Processing with Clustering Algorithms for motor gesture Tasks. In 2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM) (pp. 1-6). IEEE.Asanza, V., Pelaez, E., & Loayza, F. (2017, October). Supervised pattern recognition techniques for detecting motor intention of lower limbs in subjects with cerebral palsy. In Ecuador Technical Chapters Meeting (ETCM), 2017 IEEE (pp. 1-5). IEEE.Asanza, V., Pelaez, E., & Loayza, F. (2017, October). EEG signal clustering for motor and imaginary motor tasks on hands and feet. In Ecuador Technical Chapters Meeting (ETCM), 2017 IEEE (pp. 1-5). IEEE.Asanza, V., Ochoa, K., Sacarelo, C., Salazar, C., Loayza, F., Vaca, C., & Peláez, E. (2016, October). Clustering of EEG occipital signals using k-means. In Ecuador Technical Chapters Meeting (ETCM), IEEE (pp. 1-5). IEEE.

数据采集过程始于从20名经过筛选的健康志愿者中捕捉脑电图（EEG）信号，这些志愿者在实验开始前均已签署书面同意书。每位志愿者被要求在不同频率下重复进行10次实验；每次实验均由视觉刺激触发。每位志愿者在10种不同的视觉刺激频率（7、9、11和13赫兹）下各进行一次实验。在每次实验中，枕叶区域中的2个电极（LO、RO）产生的脑电图信号被同时记录。值得注意的是，数据采集设备具有每秒128个样本的采样率，这允许在考虑每个任务持续19.5秒的情况下，采集到2500个样本。在使用本资源时，请引用原始出版物：Asanza, V.，Avilés-Mendoza, K.，Trivino-Gonzalez, H.，Rosales-Uribe, F.，Torres-Brunes, J.，Loayza, F. R. 等. (2021). 基于 Emotiv EPOC BCI 和 Raspberry Pi 的 SSVEP-EEG 信号分类. IFAC-PapersOnLine, 54(15), 388-393. 参考文献：J. Fuentes-Gonzalez，A. Infante-Alarcón，V. Asanza 和 F. R. Loayza. "一种基于 3D 打印 EEG 的假肢手臂.