Negative emotion recognition from stimulated EEG signals

机译：从刺激的脑电信号中识别负性情绪

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This paper proposes a scheme of emotion recognition from audio-visually stimulated EEG (electroencephalography) signals, the choice of signal being influenced by the fact that these signals are the direct unaltered outcome of one's brain activity and hence cannot be voluntarily suppressed. The EEG signals have been recorded using the NEUROWIN EEG amplifier of Nasan Medicals with a sampling rate of 250Hz from electrodes positioned at F3, F4, Fp1 and Fp2, since they lie over the frontal and pre-frontal lobe. The Raw EEG signals obtained need to be processed and classified into different emotional categories, using various features and intelligent classification algorithms. Features from these signals have been extracted using wavelet transform, statistical parameters and Hjorth parameter estimation, which are then classified using linear support vector machine (LSVM) and k-nearest neighbour (kNN). These extracted features are classified into the two different negative emotion classes of sad and disgust, with an average classification accuracy of the sad emotion being 78.04% and disgust being 76.31%. With our objective of development of emotionally challenged machines and devices that could become compatible with the emotional state of the user and nullify the effects of negative emotions on their work performance; the proposed scheme takes us a step closer to realisation of the same.

机译：本文提出了一种从视听刺激的EEG（脑电图）信号中进行情绪识别的方案，该信号的选择受以下事实的影响：这些信号是人脑活动的直接不变结果，因此不能被自动抑制。脑电信号已使用Nasan Medicals的NEUROWIN脑电放大器进行了记录，采样频率为250Hz，来自位于F3，F4，Fp1和Fp2的电极，因为它们位于额叶和额叶前部。需要使用各种功能和智能分类算法对获得的原始EEG信号进行处理并将其分类为不同的情感类别。使用小波变换，统计参数和Hjorth参数估计从这些信号中提取特征，然后使用线性支持向量机（LSVM）和k最近邻（kNN）对它们进行分类。这些提取的特征被分为悲伤和厌恶两个不同的负面情绪类别，悲伤情绪的平均分类准确度为78.04％，厌恶的平均分类准确率为76.31％。我们的目标是开发能够与用户的情绪状态兼容并消除消极情绪对其工作绩效的影响的情感机器和设备；提出的方案使我们更接近于实现该方案。

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《2012 Third International Conference on Computing Communication amp; Networking Technologies.》|2012年|p.1- 8|共8页
会议地点 Coimbatore(IN);Coimbatore(IN)
作者
Singh Garima; Jati Arindam; Khasnobish Anwesha; Bhattacharyya Saugat; Konar Amit; Tibarewala D.N.; Janarthanan R.;
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Dept. of Electronics Telecommunication Engg., India;

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正文语种 eng
中图分类通信网;通信网;
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机译：来自多通道EEG信号的三维特征和卷积神经网络的情感识别
2. Emotion recognition using time-frequency ridges of EEG signals based on multivariate synchrosqueezing transform [J] . Mert Ahmet, Celik Hasan Huseyin Biomedizinische Technik . 2021,第4期

机译：基于多变量同步调节变换的EEG信号时频磁点的情感识别
3. Emotion Recognition of EEG Signals Based on the Ensemble Learning Method: AdaBoost [J] . Yu Chen, Rui Chang, Jifeng Guo Mathematical Problems in Engineering: Theory, Methods and Applications . 2021,第a期

机译：基于集合学习方法的EEG信号的情感识别：Adaboost
4. Negative emotion recognition from stimulated EEG signals [C] . Singh Garima, Jati Arindam, Khasnobish Anwesha, International Conference on Computing, Communication and Networking Technologies . 2012

机译：来自刺激的EEG信号的负面情绪识别
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. Dual-Threshold-Based Microstate Analysis on Characterizing Temporal Dynamics of Affective Process and Emotion Recognition From EEG Signals [O] . Jing Chen, Haifeng Li, Lin Ma, 2021

机译：基于双阈值的微溶液分析对脑电图的表征情感流程和情感识别的时间动态
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）

Negative emotion recognition from stimulated EEG signals

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