Emotion recognition from physiological signals

Emotion recognition is one of the great challenges in human-human and human-computer interaction. Accurate emotion recognition would allow computers to recognize human emotions and therefore react accordingly. In this paper, an approach for emotion recognition based on physiological signals is proposed. Six basic emotions: joy, sadness, fear, disgust, neutrality and amusement are analysed using physiological signals. These emotions are induced through the presentation of International Affecting Picture System (IAPS) pictures to the subjects. The physiological signals of interest in this analysis are: electromyogram signal (EMG), respiratory volume (RV), skin temperature (SKT), skin conductance (SKC), blood volume pulse (BVP) and heart rate (HR). These are selected to extract characteristic parameters, which will be used for classifying the emotions. The SVM (support vector machine) technique is used for classifying these parameters. The experimental results show that the proposed methodology provides in general a recognition rate of 85% for different emotional states.     

Emotion recognition from physiological signals

Emotion recognition is one of the great challenges in human–human and human–computer interaction. Accurate emotion recognition would allow computers to recognize human emotions and therefore react accordingly. In this paper, an approach for emotion recognition based on physiological signals is proposed. Six basic emotions: joy, sadness, fear, disgust, neutrality and amusement are analysed using physiological signals. These emotions are induced through the presentation of International Affecting Picture System (IAPS) pictures to the subjects. The physiological signals of interest in this analysis are: electromyogram signal (EMG), respiratory volume (RV), skin temperature (SKT), skin conductance (SKC), blood volume pulse (BVP) and heart rate (HR). These are selected to extract characteristic parameters, which will be used for classifying the emotions. The SVM (support vector machine) technique is used for classifying these parameters. The experimental results show that the proposed methodology provides in general a recognition rate of 85% for different emotional states.     

Emotion recognition system using short-term monitoring of physiological signals

A physiological signal-based emotion recognition system is reported. The system was developed to operate as a user-independent system, based on physiological signal databases obtained from multiple subjects. The input signals were electrocardiogram, skin temperature variation and electrodermal activity, all of which were acquired without much discomfort from the body surface, and can reflect the influence of emotion on the autonomic nervous system. The system consisted of preprocessing, feature extraction and pattern classification stages. Preprocessing and feature extraction methods were devised so that emotion-specific characteristics could be extracted from short-segment signals. Although the features were carefully extracted, their distribution formed a classification problem, with large overlap among clusters and large variance within clusters. A support vector machine was adopted as a pattern classifier to resolve this difficulty. Correct-classification ratios for 50 subjects were 78.4% and 61.8%, for the recognition of three and four categories, respectively.     

基于体表生理信号的呼吸功能监测仪研制

目的：研制一款基于体表心电、膈肌电和胸阻抗信号的呼吸功能监测仪原理样机,可在家庭、医疗急救等场合实现对呼吸功能的持续监测。方法：以STM32F411VET6单片机开发系统为平台,用一对Ag/AgCl电极作为高频激励信号的输出和心电、胸阻抗信号的检测电极,另一对Ag/AgCl电极作为膈肌电信号检测电极,两对电极同时检测心电、膈肌电和胸阻抗信号。系统硬件主要包括心电信号检测电路、胸阻抗信号检测电路、膈肌电信号检测电路、恒流源激励电路以及微控制器。系统采用12 V可充电锂电池供电,模拟信号通过单片机A/D转换成数字信号,通过SDIO接口存储于SD卡。在完成样机制作和性能测试之后,采集13例因呼吸功能障碍实施机械通气患者和13例健康成年人的信号,计算15个与呼吸功能相关的参数,比较机械通气患者与健康对照组参数之间的差异,验证了呼吸功能监测仪的可靠性。结果：样机采集信号的信噪比>10 dB、共模抑制比>80 dB,样机漏电流<30μA。机械通气患者的吸气时间、呼气时间、潮气量、胸阻抗峰峰值、胸阻抗1 s变化量、膈肌电低频功率、膈肌电高频功率、高频比低频、膈肌放电面积、膈肌放电时...     

Segmentation of depth-EEG seizure signals: Method based on a physiological parameter and comparative study

The analysis of stereoelectroencephalographic (intracerebral recording) signals provides information on the electrical activity of brain structures implied in epileptic seizures. A simple nonparametric adaptive segmentation method, based on a physiologically relevant parameter, is presented and compared with three methods reported in the literature. The comparative frame allows us to objectively test methods for their performances on the same basis. Results show that the proposed method is robust with respect to the types of change studied and easier to conduct, even if it is less accurate about the estimation of instants of change than another method presented in this study. Signals are segmented throughout the duration of seizures without parameter readjustment and generate instants of change in accordance with those interactively delimited by the clinician.     

Anytime multipurpose emotion recognition from EEG data using a Liquid State Machine based framework

Recent technological advances in machine learning offer the possibility of decoding complex datasets and discern latent patterns. In this study, we adopt Liquid State Machines (LSM) to recognize the emotional state of an individual based on EEG data. LSM were applied to a previously validated EEG dataset where subjects view a battery of emotional film clips and then rate their degree of emotion during each film based on valence, arousal, and liking levels. We introduce LSM as a model for an automatic feature extraction and prediction from raw EEG with potential extension to a wider range of applications. We also elaborate on how to exploit the separation property in LSM to build a multipurpose and anytime recognition framework, where we used one trained model to predict valence, arousal and liking levels at different durations of the input. Our simulations showed that the LSM-based framework achieve outstanding results in comparison with other works using different emotion prediction scenarios with cross validation.     

大腿截肢患者的残肢肌电运动识别

目的研究利用大腿残肢肌电信号进行下肢运动模式识别的方法,探讨肌电信号控制下肢假肢的可能性。方法采集15名大腿截肢者残肢侧股直肌、股外侧肌、阔筋膜张肌、股二头肌、半腱肌、臀大肌6块肌肉的表面肌电信号,提取肌电信号的6种时域、频域特征,利用支持向量机对平地行走、上楼梯、下楼梯、坐下、起立5种下肢运动模式进行识别。结果利用残肢肌电信号可以实现5种下肢运动模式的在线识别,对同一受试者同次测试数据识别率为94%,同一受试者的多次混合数据识别率为85%,对不同受试者混合数据识别率为74%。通过特征优化,仅利用3块肌肉的2个特征,对同一受试者的同次测试数据识别率仍可达92%。对平地行走、上楼梯、下楼梯3种动作的识别,同一受试者同次测试数据识别率为100%,同一受试者的多次混合数据识别率为98.33%,对不同受试者混合数据识别率为93.33%。结论仅仅利用残肢肌电信号能够实现运动意图的在线识别,通过对同一患者使用前的多次数据训练,有望达到较高的识别率。研究结果为肌电运动识别用于下肢假肢控制奠定了基础。     

Effects of sleep loss on emotion recognition: a dissociation between face and word stimuli

Short-term sleep deprivation, or extended wakefulness, adversely affects cognitive functions and behavior. However, scarce research has addressed the effects of sleep deprivation (SD) on emotional processing. In this study, we investigated the impact of reduced vigilance due to moderate sleep deprivation on the ability to recognize emotional expressions of faces and emotional content of words. Participants remained awake for 24 h and performed the tasks in two sessions, one in which they were not affected by sleep loss (baseline; BSL), and other affected by SD, according to a counterbalanced sequence. Tasks were carried out twice at 10:00 and 4:00 am, or at 12:00 and 6:00 am. In both tasks, participants had to respond to the emotional valence of the target stimulus: negative, positive, or neutral. The results showed that in the word task, sleep deprivation impaired recognition irrespective of the emotional valence of words. However, sleep deprivation impaired recognition of emotional face expressions mainly when they showed a neutral expression. Emotional face expressions were less affected by the sleep loss, but positive faces were more resistant than negative faces to the detrimental effect of sleep deprivation. The differential effects of sleep deprivation on recognition of the different emotional stimuli are indicative of emotional facial expressions being stronger emotional stimuli than emotional laden words. This dissociation may be attributed to the more automatic sensory encoding of emotional facial content.     

Handling non-negativity in deconvolution of physiological signals: a nonlinear stochastic approach

A stochastic interpretation of Tikhonov regularization has been recently proposed to attack some open problems of deconvolution when dealing with physiological systems, i.e., in addition to ill-conditioning, infrequent and nonuniform sampling and necessity of having credible confidence intervals. However, the possible violation of the non-negativity constraint cannot be dealt with on firm statistical grounds, since the model of the unknown signal is compatible with negative realizations. In this paper, we propose a new model of the unknown input which excludes negative values. The model is embedded within a Bayesian estimation framework to calculate, by resorting to a Markov chain Monte Carlo algorithm, a nonlinear estimate of the unknown input given by its a posteriori expected value. Applications to simulated and real hormone secretion/pharmacokinetic problems are presented which show that this nonlinear approach is more accurate than the linear one. In addition, more realistic confidence intervals are obtained. © 2002 Biomedical Engineering Society. PAC2002: 8710+e, 0250Ga     

基于脑电信号的抑郁情绪倾向研究

目的对在校大学生的脑电信号(electroencephalography,EEG)进行研究,以期找到对抑郁情绪倾向预测具有可行的高识别率的情绪特征。方法首先通过14导脑电设备采集19位高校大学生(女6、男13)的脑电信号并对其进行贝克抑郁量表的测试,按测试结果将其分为实验组和正常组。然后使用Eeglab工具包对采集到的数据进行预处理,得到干净的脑电信号。最后采用事件相关频谱扰动(ERSP)对其进行时频分析,以讨论在时间-频率域内的能量变化与抑郁情绪倾向预测的关系。结果在负性图片刺激下,在Alpha、Beta波段均发现时间窗为50~150 ms、350~450 ms时正常组和实验组之间差异具有统计学意义。在正性图片刺激下,Alpha波段在时间窗为150~250 ms、350~450 ms时正常组和实验组之间差异具有统计学意义,Beta波段在时间窗为300~400 ms时差异具有统计学意义。结论在正负性图片的刺激下,Alpha、Beta波段的部分特殊时间段中存在可识别抑郁情绪倾向的特征值,可为今后研究抑郁情绪倾向性提供一定的参考依据。     

Misfortune may be a blessing in disguise: Fairness perception and emotion modulate decision making

Fairness perception and equality during social interactions frequently elicit affective arousal and affect decision making. By integrating the dictator game and a probabilistic gambling task, this study aimed to investigate the effects of a negative experience induced by perceived unfairness on decision making using behavioral, model fitting, and electrophysiological approaches. Participants were randomly assigned to the neutral, harsh, or kind groups, which consisted of various asset allocation scenarios to induce different levels of perceived unfairness. The monetary gain was subsequently considered the initial asset in a negatively rewarded, probabilistic gambling task in which the participants were instructed to maintain as much asset as possible. Our behavioral results indicated that the participants in the harsh group exhibited increased levels of negative emotions but retained greater total game scores than the participants in the other two groups. Parameter estimation of a reinforcement learning model using a Bayesian approach indicated that these participants were more loss aversive and consistent in decision making. Data from simultaneous ERP recordings further demonstrated that these participants exhibited larger feedback‐related negativity to unexpected outcomes in the gambling task, which suggests enhanced reward sensitivity and signaling of reward prediction error. Collectively, our study suggests that a negative experience may be an advantage in the modulation of reward‐based decision making.     

Effects of emotion and semantic relatedness on recognition memory: Behavioral and electrophysiological evidence

Some aspects of our memory are enhanced by emotion, whereas others can be unaffected or even hindered. Previous studies reported impaired associative memory of emotional content, an effect termed associative “emotional interference”. The current study used EEG and an associative recognition paradigm to investigate the cognitive and neural mechanisms associated with this effect. In two experiments, participants studied negative and neutral stimulus-pairs that were either semantically related or unrelated. In Experiment 1 emotions were relevant to the encoding task (valence judgment) whereas in Experiment 2 emotions were irrelevant (familiarity judgment). In a subsequent associative recognition test, EEG was recorded while participants discriminated between intact, rearranged, and new pairs. An associative emotional interference effect was observed in both experiments, but was attenuated for semantically related pairs in Experiment 1, where valence was relevant to the task. Moreover, a modulation of an early associative memory ERP component (300–550 ms) occurred for negative pairs when valence was task-relevant (Experiment 1), but for semantically related pairs when valence was irrelevant (Experiment 2). A later ERP component (550–800 ms) showed a more general pattern, and was observed in all experimental conditions. These results suggest that both valence and semantic relations can act as an organizing principle that promotes associative binding. Their ability to contribute to successful retrieval depends on specific task demands.     

面向情绪脑电分析的增强型功率谱密度特征提取方法

针对功率谱密度在脑电情绪分析中存在特征单一且无法有效表示频率间差异的问题,提出一种增强型功率谱密度特征提取方法,实现对情绪的分析与差异显著性判断。该方法通过脑电信号的α频率功率谱密度得到功率谱密度图像,利用图像特征提取算法提取其颜色特征、纹理特征与相似性特征,再基于相关性准则剔除冗余特征,以差异显著性P值的最小平均值为目标,获得最终的特征子集,从而有效地融合了不同图像特征,最后对被试的情绪进行分析与差异显著性判断。试验结果表明,所提出的方法能够有效量化SEED数据集中被试的情绪差异;在自行设计情绪脑电试验中,与其他方法相比,利用所提出的方法得到的差异显著性值更小,证明了方法的可行性和有效性。     

An ECG simulator with a novel ECG profile for physiological signals

Abstract

This work introduces a low-cost open-source electrocardiography (ECG) simulator comprising both MATLAB software for signal generation and a dedicated circuit board for signal output via a commercial sound card. Synthetic, rate-dependent ECG simulation is based on third-order polynomials that are calculated in sections for the main waves and spikes, respectively. Besides the heart rate, the output profile is fully adjustable with respect to Einthoven lead signals I–III, the amplitudes of the individual ECG waves and spikes, as well as the constitution and intensity of common distortions. The underlying coefficients for the synthetic ECG profile are obtained experimentally by analysing recordings of 22 healthy individuals with heart rates in the range of 40–180 bpm. Eight of these recordings are selected to determine the coefficients for the polynomials (training set) while the remaining 14 serve as test set to evaluate their applicability and accuracy. Thereby, a mean correlation of 98.57% is found which is superior in comparison with a widely accepted rate-dependent ECG profile that is generated from square root and linear terms (correlation score: 91.46%). Although other use-cases are feasible, the focus of this work is the development of an ECG simulator for academic research and university education. Both the MATLAB source code and the circuit layout files are available in the online supplement stimulating further work on this topic.     

Continuous hand gesture surface electromyography recognition method based on empirical formula

目的实现连续手势动作表面肌电信号(surface electromyography,sEMG)的简单有效识别.方法 首先推导出测试信号属于手势动作模板的概率密度经验公式,通过数据处理实验确定公式参数,最后设计连续手势识别实验以测试该经验公式用于动作sEMG识别的效果.结果 推导出的经验公式在连续手势识别中获得了较好的识别结果,验证了该经验公式用于连续手势动作sEMG信号识别的有效性.结论 基于经验公式的方法为实现基于sEMG信号的连续手势识别提供了一种可行的解决方案.     

基于经验公式的连续手势动作表面肌电信号识别方法

目的实现连续手势动作表面肌电信号（surface electromyography,sEMG）的简单有效识别。方法首先推导出测试信号属于手势动作模板的概率密度经验公式,通过数据处理实验确定公式参数,最后设计连续手势识别实验以测试该经验公式用于动作sEMG识别的效果。结果推导出的经验公式在连续手势识别中获得了较好的识别结果,验证了该经验公式用于连续手势动作sEMG信号识别的有效性。结论基于经验公式的方法为实现基于sEMG信号的连续手势识别提供了一种可行的解决方案。     

The role of emotion in clinical decision making: an integrative literature review

Background

Traditionally, clinical decision making has been perceived as a purely rational and cognitive process. Recently, a number of authors have linked emotional intelligence (EI) to clinical decision making (CDM) and calls have been made for an increased focus on EI skills for clinicians. The objective of this integrative literature review was to identify and synthesise the empirical evidence for a role of emotion in CDM.

Methods

A systematic search of the bibliographic databases PubMed, PsychINFO, and CINAHL (EBSCO) was conducted to identify empirical studies of clinician populations. Search terms were focused to identify studies reporting clinician emotion OR clinician emotional intelligence OR emotional competence AND clinical decision making OR clinical reasoning.

Results

Twenty three papers were retained for synthesis. These represented empirical work from qualitative, quantitative, and mixed-methods approaches and comprised work with a focus on experienced emotion and on skills associated with emotional intelligence. The studies examined nurses (10), physicians (7), occupational therapists (1), physiotherapists (1), mixed clinician samples (3), and unspecified infectious disease experts (1). We identified two main themes in the context of clinical decision making: the subjective experience of emotion; and, the application of emotion and cognition in CDM. Sub-themes under the subjective experience of emotion were: emotional response to contextual pressures; emotional responses to others; and, intentional exclusion of emotion from CDM. Under the application of emotion and cognition in CDM, sub-themes were: compassionate emotional labour – responsiveness to patient emotion within CDM; interdisciplinary tension regarding the significance and meaning of emotion in CDM; and, emotion and moral judgement.

Conclusions

Clinicians’ experienced emotions can and do affect clinical decision making, although acknowledgement of that is far from universal. Importantly, this occurs in the in the absence of a clear theoretical framework and educational preparation may not reflect the importance of emotional competence to effective CDM.