Intelligent emotion recognition system using brain signals (EEG)

Harischandra, Janani; Perera, M.U.S.

doi:10.1109/iecbes.2012.6498050

Cited by 9 publications

(4 citation statements)

References 3 publications

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“…Most of the existing techniques for inferring affective states from electrophysiological signals are based on a small number of discrete states e.g., (Harischandra and Perera, 2012;Mavridou et al, 2017). But the amount of distinct affective states that can be detected using this approach is limited.…”

Section: Discussionmentioning

confidence: 99%

“…This theory describes emotions in terms of twenty-two categories and assumes a clear distinction between each category. This approach is compatible with existing emotion recognition algorithms because these are usually based on categorizing emotions (e.g., Harischandra and Perera, 2012;Mavridou et al, 2017). According to the OCC theory (Ortony et al, 1988), the first step in an emotional response is the perception of the situation.…”

Section: Emotion Theoriesmentioning

confidence: 96%

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Affective Visualization in Virtual Reality: An Integrative Review

Pinilla

García

Raffe

et al. 2021

Front. Virtual Real.

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A cluster of research in Affective Computing suggests that it is possible to infer some characteristics of users’ affective states by analyzing their electrophysiological activity in real-time. However, it is not clear how to use the information extracted from electrophysiological signals to create visual representations of the affective states of Virtual Reality (VR) users. Visualization of users’ affective states in VR can lead to biofeedback therapies for mental health care. Understanding how to visualize affective states in VR requires an interdisciplinary approach that integrates psychology, electrophysiology, and audio-visual design. Therefore, this review aims to integrate previous studies from these fields to understand how to develop virtual environments that can automatically create visual representations of users’ affective states. The manuscript addresses this challenge in four sections: First, theories related to emotion and affect are summarized. Second, evidence suggesting that visual and sound cues tend to be associated with affective states are discussed. Third, some of the available methods for assessing affect are described. The fourth and final section contains five practical considerations for the development of virtual reality environments for affect visualization.

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Section: Discussionmentioning

confidence: 99%

Section: Emotion Theoriesmentioning

confidence: 96%

Affective Visualization in Virtual Reality: An Integrative Review

Pinilla

García

Raffe

et al. 2021

Front. Virtual Real.

View full text Add to dashboard Cite

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“…J Harischandra, MUS Perera prepared a system which enables severely disabled as well as able users to interact with the system using eye movement in order to respond to detected emotion. The solution can be used to detect emotions of motor disabled people and provision a means of communication; also it is a learning tool for trainee neurologists [12]. According to Jing Fan, the classification results were promising, with over 80% accuracy in classifying engagement and mental workload, and over 75% accuracy in classifying emotional states [13].…”

Section: Literature Surveymentioning

confidence: 99%

Novel Approach for Emotion Detection and Stabilizing Mental State by Using Machine Learning Techniques

Kimmatkar

Babu

2021

Computers

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The aim of this research study is to detect emotional state by processing electroencephalography (EEG) signals and test effect of meditation music therapy to stabilize mental state. This study is useful to identify 12 subtle emotions angry (annoying, angry, nervous), calm (calm, peaceful, relaxed), happy (excited, happy, pleased), sad (sleepy, bored, sad). A total 120 emotion signals were collected by using Emotive 14 channel EEG headset. Emotions are elicited by using three types of stimulus thoughts, audio and video. The system is trained by using captured database of emotion signals which include 30 signals of each emotion class. A total of 24 features were extracted by performing Chirplet transform. Band power is ranked as the prominent feature. The multimodel approach of classifier is used to classify emotions. Classification accuracy is tested for K-nearest neighbor (KNN), convolutional neural network (CNN), recurrent neural network (RNN) and deep neural network (DNN) classifiers. The system is tested to detect emotions of intellectually disable people. Meditation music therapy is used to stable mental state. It is found that it changed emotions of both intellectually disabled and normal participants from the annoying state to the relaxed state. A 75% positive transformation of mental state is obtained in the participants by using music therapy. This research study presents a novel approach for detailed analysis of brain EEG signals for emotion detection and stabilize mental state.

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“…Various studies have addressed the detection and tracking of facial landmarks including the iris and pupil which has various applications-particularly, eye gaze estimation for human-machine interfaces. The control of assistive devices for disability [2], driver safety improvements [3,4], the design of diagnostic tools for brain diseases [5], cognitive research [6], automated deception detection system (ADDS) [7], and academic performance analysis [8] are some examples of such applications.…”

Section: Introductionmentioning

confidence: 99%

Pupil Localisation and Eye Centre Estimation Using Machine Learning and Computer Vision

Khan

Hussain

Kuru

et al. 2020

Sensors

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Various methods have been used to estimate the pupil location within an image or a real-time video frame in many fields. However, these methods lack the performance specifically in low-resolution images and varying background conditions. We propose a coarse-to-fine pupil localisation method using a composite of machine learning and image processing algorithms. First, a pre-trained model is employed for the facial landmark identification to extract the desired eye frames within the input image. Then, we use multi-stage convolution to find the optimal horizontal and vertical coordinates of the pupil within the identified eye frames. For this purpose, we define an adaptive kernel to deal with the varying resolution and size of input images. Furthermore, a dynamic threshold is calculated recursively for reliable identification of the best-matched candidate. We evaluated our method using various statistical and standard metrics along with a standardised distance metric that we introduce for the first time in this study. The proposed method outperforms previous works in terms of accuracy and reliability when benchmarked on multiple standard datasets. The work has diverse artificial intelligence and industrial applications including human computer interfaces, emotion recognition, psychological profiling, healthcare, and automated deception detection.

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Intelligent emotion recognition system using brain signals (EEG)

Cited by 9 publications

References 3 publications

Affective Visualization in Virtual Reality: An Integrative Review

Affective Visualization in Virtual Reality: An Integrative Review

Novel Approach for Emotion Detection and Stabilizing Mental State by Using Machine Learning Techniques

Pupil Localisation and Eye Centre Estimation Using Machine Learning and Computer Vision

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