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DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf Devices

机译：梦想家：通过EEG和来自无线低成本的现成设备的EEG和ECG信号的数据库

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Stamos Katsigiannis; Naeem Ramzan;
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1. Emotion Detection Using EEG and ECG Signals from Wearable Textile Devices for Elderly People [J] . ZENG Fangmeng, LIN Yitao, SIRIARAYA Panote, Journal of textile engineering . 2020,第6期

机译：情感检测使用eEG和ECG信号从可穿戴纺织设备用于老年人
2. Database for an emotion recognition system based on EEG signals and various computer games - GAMEEMO [J] . Alakus Talha Burak, Gonen Murat, Turkoglu Ibrahim Biomedical signal processing and control . 2020,第Jula期

机译：基于EEG信号和各种计算机游戏的情感识别系统数据库 - Gameemo
3. Development of emotion recognition interface using complex EEG/ECG bio-signal for interactive contents [J] . Shin Dongmin, Shin Dongil, Shin Dongkyoo Multimedia Tools and Applications . 2017,第9期

机译：使用复杂的EEG / ECG生物信号开发交互式内容的情感识别界面
4. Automatic Emotion Recognition Based on EEG and ECG Signals While Listening to Quranic Recitation Compared with Listening to Music [C] . Sabaa Ahmed Yahya Al-Galal, Imad Fakhri Taha Alshaikhli, Abdul Wahab Bin Abdul Rahman International Conference on Information and Communication Technology for the Muslim World . 2016

机译：与听音乐相比，听古兰经时基于EEG和ECG信号的自动情感识别
5. Design of a recognition system for monitoring the depth of anesthesia, based on the autoregressive modeling and neural network analysis of the EEG signals. [D] . Sharma, Ashutosh. 1993

机译：基于EEG信号的自回归建模和神经网络分析，设计用于监测麻醉深度的识别系统。
6. Pattern Recognition of Cognitive Load Using EEG and ECG Signals [O] . Ronglong Xiong, Fanmeng Kong, Xuehong Yang, 2020

机译：使用EEG和ECG信号进行认知负荷的模式识别
7. EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed TarekShahriar Abstract—An EMD-chaos based approach is proposed todiscriminate EEG signals corresponding to healthy persons,and epileptic patients during seizure-free intervals and seizureattacks. An electroencephalogram (EEG) is first empiricallydecomposed to intrinsic mode functions (IMFs). The nonlineardynamics of these IMFs are quantified in terms of the largestLyapunov exponent (LLE) and correlation dimension (CD).This chaotic analysis in EMD domain is applied to a large groupof EEG signals corresponding to healthy persons as well asepileptic patients (both with and without seizure attacks). It isshown that the values of the obtained LLE and CD exhibitfeatures by which EEG for seizure attacks can be clearlydistinguished from other EEG signals in the EMD domain.Thus, the proposed approach may aid researchers in developingeffective techniques to predict seizure activities. Index Terms—Electroencephalogram (EEG), empiricalmode decomposition (EMD), largest Lyapunov exponent (LLE),correlation dimension (CD), epileptic seizures. The Authors are with the Electrical and Electronic EngineeringDepartment, Bangladesh University of Engineering and Technology,Dhaka-1000, Bangladesh (e-mail: imamul@eee.buet.ac.bd) PDF Cite: S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed Tarek Shahriar, "EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain," International Journal of Computer and Electrical Engineering vol. 4, no. 3, pp. 326-330, 2012. PREVIOUS PAPER Perception of Emotions Using Constructive Learningthrough Speech NEXT PAPER Physical Layer Impairments Aware OVPN Connection Selection Mechanisms Copyright © 2008-2013. International Association of Computer Science and Information Technology Press (IACSIT Press) [O] . S. M. Shafiul Alam, Syed TarekShahriar 2012

机译：EEG信号在EMD域S. S. Shafiul Alam，S中的非线性动力学使用非线性动力学。 M. Shafiul Alam，Aurangozeb和Syed Tarekshahriar摘要 - 基于EMD Chaos的方法，提出了对应于健康人的EEG信号，癫痫发作期间的癫痫患者和Seizureattacks。脑电图（EEG）首先被凭经上分解为内在模式功能（IMF）。这些IMF的非线性动力学在最大范围的指数（LLE）和相关尺寸（CD）方面是量化的。本域中的混沌分析应用于与健康人相对应的大型脑电图（Asepileptic患者）（两者都有癫痫发作）。因此，所获得的LLE和CD表展的价值可以从EMD领域的其他EEG信号中清晰地区分脑电图的表达展示。本拟议的方法可以帮助研究人员以预测癫痫发作的癫痫发作技术。索引术语 - 脑电图（EEG），仿真态分解（EMD），最大的Lyapunov指数（LLE），相关维度（CD），癫痫发作。作者与电气电子和电子工程公司，孟加拉国工程和技术大学，孟加拉国达卡 - 1000（电子邮件：imamul@eee.buet.ac.bd）pdf cite：s. m. shafiul Alam，s。 M. Shafiul Alam，Aurangozeb和Syed Tarek Shahriar，“EEG信号歧视在EMD领域的非线性动态，”计算机电气工程卷国际杂志。 4，不。 3，pp。326-330,2012，上一篇论文对情绪的看法，使用建设性的学习言论下一篇论文物理层障碍意识到OVPN连接选择机制版权所有©2008-2013。国际计算机科学与信息技术协会出版社（IACSIT Press）

DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf Devices

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