Assessing the Effectiveness of Automated Emotion Recognition in Adults and Children for Clinical Investigation

Recent success stories in automated object or face recognition, partly fuelled by deep learning artificial neural network (ANN) architectures, has led to the advancement of biometric research platforms and, to some extent, the resurrection of Artificial Intelligence (AI). In line with this general trend, inter-disciplinary approaches have taken place to automate the recognition of emotions in adults or children for the benefit of various applications such as identification of children emotions prior to a clinical investigation. Within this context, it turns out that automating emotion recognition is far from being straight forward with several challenges arising for both science(e.g., methodology underpinned by psychology) and technology (e.g., iMotions biometric research platform). In this paper, we present a methodology, experiment and interesting findings, which raise the following research questions for the recognition of emotions and attention in humans: a) adequacy of well-established techniques such as the International Affective Picture System (IAPS), b) adequacy of state-of-the-art biometric research platforms, c) the extent to which emotional responses may be different among children or adults. Our findings and first attempts to answer some of these research questions, are all based on a mixed sample of adults and children, who took part in the experiment resulting into a statistical analysis of numerous variables. These are related with, both automatically and interactively, captured responses of participants to a sample of IAPS pictures

Emotions in context: examining pervasive affective sensing systems, applications, and analyses

Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; “sensing”, “analysis”, and “application”. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing

On the analysis of EEG power, frequency and asymmetry in Parkinson's disease during emotion processing

Objective: While Parkinson’s disease (PD) has traditionally been described as a movement disorder, there is growing evidence of disruption in emotion information processing associated with the disease. The aim of this study was to investigate whether there are specific electroencephalographic (EEG) characteristics that discriminate PD patients and normal controls during emotion information processing. Method: EEG recordings from 14 scalp sites were collected from 20 PD patients and 30 age-matched normal controls. Multimodal (audio-visual) stimuli were presented to evoke specific targeted emotional states such as happiness, sadness, fear, anger, surprise and disgust. Absolute and relative power, frequency and asymmetry measures derived from spectrally analyzed EEGs were subjected to repeated ANOVA measures for group comparisons as well as to discriminate function analysis to examine their utility as classification indices. In addition, subjective ratings were obtained for the used emotional stimuli. Results: Behaviorally, PD patients showed no impairments in emotion recognition as measured by subjective ratings. Compared with normal controls, PD patients evidenced smaller overall relative delta, theta, alpha and beta power, and at bilateral anterior regions smaller absolute theta, alpha, and beta power and higher mean total spectrum frequency across different emotional states. Inter-hemispheric theta, alpha, and beta power asymmetry index differences were noted, with controls exhibiting greater right than left hemisphere activation. Whereas intra-hemispheric alpha power asymmetry reduction was exhibited in patients bilaterally at all regions. Discriminant analysis correctly classified 95.0% of the patients and controls during emotional stimuli. Conclusion: These distributed spectral powers in different frequency bands might provide meaningful information about emotional processing in PD patients

Automatic Measurement of Affect in Dimensional and Continuous Spaces: Why, What, and How?

This paper aims to give a brief overview of the current state-of-the-art in automatic measurement of affect signals in dimensional and continuous spaces (a continuous scale from -1 to +1) by seeking answers to the following questions: i) why has the field shifted towards dimensional and continuous interpretations of affective displays recorded in real-world settings? ii) what are the affect dimensions used, and the affect signals measured? and iii) how has the current automatic measurement technology been developed, and how can we advance the field

Detection of emotions in Parkinson's disease using higher order spectral features from brain's electrical activity

Non-motor symptoms in Parkinson's disease (PD) involving cognition and emotion have been progressively receiving more attention in recent times. Electroencephalogram (EEG) signals, being an activity of central nervous system, can reflect the underlying true emotional state of a person. This paper presents a computational framework for classifying PD patients compared to healthy controls (HC) using emotional information from the brain's electrical activity

A Dual-Modality Emotion Recognition System of EEG and Facial Images and its Application in Educational Scene

With the development of computer science, people's interactions with computers or through computers have become more frequent. Some human-computer interactions or human-to-human interactions that are often seen in daily life: online chat, online banking services, facial recognition functions, etc. Only through text messaging, however, can the effect of information transfer be reduced to around 30% of the original. Communication becomes truly efficient when we can see one other's reactions and feel each other's emotions. This issue is especially noticeable in the educational field. Offline teaching is a classic teaching style in which teachers may determine a student's present emotional state based on their expressions and alter teaching methods accordingly. With the advancement of computers and the impact of Covid-19, an increasing number of schools and educational institutions are exploring employing online or video-based instruction. In such circumstances, it is difficult for teachers to get feedback from students. Therefore, an emotion recognition method is proposed in this thesis that can be used for educational scenarios, which can help teachers quantify the emotional state of students in class and be used to guide teachers in exploring or adjusting teaching methods. Text, physiological signals, gestures, facial photographs, and other data types are commonly used for emotion recognition. Data collection for facial images emotion recognition is particularly convenient and fast among them, although there is a problem that people may subjectively conceal true emotions, resulting in inaccurate recognition results. Emotion recognition based on EEG waves can compensate for this drawback. Taking into account the aforementioned issues, this thesis first employs the SVM-PCA to classify emotions in EEG data, then employs the deep-CNN to classify the emotions of the subject's facial images. Finally, the D-S evidence theory is used for fusing and analyzing the two classification results and obtains the final emotion recognition accuracy of 92%. The specific research content of this thesis is as follows: 1) The background of emotion recognition systems used in teaching scenarios is discussed, as well as the use of various single modality systems for emotion recognition. 2) Detailed analysis of EEG emotion recognition based on SVM. The theory of EEG signal generation, frequency band characteristics, and emotional dimensions is introduced. The EEG signal is first filtered and processed with artifact removal. The processed EEG signal is then used for feature extraction using wavelet transforms. It is finally fed into the proposed SVM-PCA for emotion recognition and the accuracy is 64%. 3) Using the proposed deep-CNN to recognize emotions in facial images. Firstly, the Adaboost algorithm is used to detect and intercept the face area in the image, and the gray level balance is performed on the captured image. Then the preprocessed images are trained and tested using the deep-CNN, and the average accuracy is 88%. 4) Fusion method based on decision-making layer. The data fusion at the decision level is carried out with the results of EEG emotion recognition and facial expression emotion recognition. The final dual-modality emotion recognition results and system accuracy of 92% are obtained using D-S evidence theory. 5) The dual-modality emotion recognition system's data collection approach is designed. Based on the process, the actual data in the educational scene is collected and analyzed. The final accuracy of the dual-modality system is 82%. Teachers can use the emotion recognition results as a guide and reference to improve their teaching efficacy

The Effects of Parental Behavior on Infants' Neural Processing of Emotion Expressions

Infants become sensitive to emotion expressions early in the 1st year and such sensitivity is likely crucial for social development and adaptation. Social interactions with primary caregivers may play a key role in the development of this complex ability. This study aimed to investigate how variations in parenting behavior affect infants' neural responses to emotional faces. Event-related potentials (ERPs) to emotional faces were recorded from 40 healthy 7-month-old infants (24 males). Parental behavior was assessed and coded using the Emotional Availability Scales during free-play interaction. Sensitive parenting was associated with increased amplitudes to positive facial expressions on the face-sensitive ERP component, the negative central. Findings are discussed in relation to the interactive mechanisms influencing how infants neurally encode positive emotions