The detection and classification of the mental state elicited by humor from EEG patterns

Ramaraju, Sriharsha; Izzidien, Ahmed; Roula, M.A.

doi:10.1109/embc.2015.7318648

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…Bin Yunus, Jasmy concluded that the results obtained by independent component analysis (ICA) could provide the most accurate result for classifying emotional states in brain activity than other methods [14]. Sriharsha Ramaraju used EEG in analyzing brain activity of autism children and measure asymmetry in frontal EEG activity which is associated with motivational approach and avoidance tendencies [15]. S Koelstra created a DEAP (Database for Emotion Analysis using Physiological Signals) database and analyzes brain activity by providing different stimulus.…”

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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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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“…Thus, investigations using neurophysiological records such as electrophysiology (EEG) and functional magnetic resonance imaging (fMRI) should be used in order to develop affective interfaces. Using EEG data, for example, different studies using a subject-independent design combined several features and classifiers to predict the affect experienced by new participants, achieving accuracies between 70 and 95% [9][10][11][12]. Using fMRI data, accuracy varied from 60% to 80% depending on the number of voxels (from 2000 to 4000) included as predictors [13].…”

Section: Introductionmentioning

confidence: 99%

Subject-independent decoding of affective states using functional near-infrared spectroscopy

et al. 2021

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Affective decoding is the inference of human emotional states using brain signal measurements. This approach is crucial to develop new therapeutic approaches for psychiatric rehabilitation, such as affective neurofeedback protocols. To reduce the training duration and optimize the clinical outputs, an ideal clinical neurofeedback could be trained using data from an independent group of volunteers before being used by new patients. Here, we investigated if this subject-independent design of affective decoding can be achieved using functional near-infrared spectroscopy (fNIRS) signals from frontal and occipital areas. For this purpose, a linear discriminant analysis classifier was first trained in a dataset (49 participants, 24.65±3.23 years) and then tested in a completely independent one (20 participants, 24.00±3.92 years). Significant balanced accuracies between classes were found for positive vs. negative (64.50 ± 12.03%, p<0.01) and negative vs. neutral (68.25 ± 12.97%, p<0.01) affective states discrimination during a reactive block consisting in viewing affective-loaded images. For an active block, in which volunteers were instructed to recollect personal affective experiences, significant accuracy was found for positive vs. neutral affect classification (71.25 ± 18.02%, p<0.01). In this last case, only three fNIRS channels were enough to discriminate between neutral and positive affective states. Although more research is needed, for example focusing on better combinations of features and classifiers, our results highlight fNIRS as a possible technique for subject-independent affective decoding, reaching significant classification accuracies of emotional states using only a few but biologically relevant features.

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“…Thus, investigations using neurophysiological records such as electrophysiology (EEG) and functional magnetic resonance imaging (fMRI) should foster the development of affective interfaces. Using EEG data, for example, different studies using a subjectindependent design combined several features and classifiers to predict the affect experienced by new participants, achieving accuracies between 70 and 95% (Lin et al, 2010, Bozhkov and Georgieva, 2014, Georgieva et al, 2015, Ramaraju et al, 2015. Using fMRI data, accuracy varied from 60% to 80% depending on the number of voxels (from 2000 to 4000) included as predictors (Baucom et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Subject-independent decoding of affective states using functional near-infrared spectroscopy

Trambaiolli

Tossato

Cravo

et al. 2017

Preprint

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Multivariate brain decoding (MBD) can be applied to estimate mental states using brain signal measurements. In the best scenario, a MBD model should be trained in a first set of volunteers and then validated in a new and independent dataset. Here, we aimed to evaluate whether functional near-infrared spectroscopy (fNIRS) signals from frontal and occipital areas provide enough information to discriminate affective states. For this purpose, a linear discriminant analysis classifier was trained in a first database (49 participants, 24.65±3.23 years) and tested in an independent database (20 participants, 24.00±3.92 years). Significant accuracies were found for positive vs. negative (64.50±12.44%, p<0.01) and negative vs. neutral (67.75±14.45%, p<0.01) affect during a passive elicitation condition, consisting in viewing pre-validated images with emotional content. For the active elicitation condition, in which volunteers were instructed to recollect personal affective experiences, significant accuracy was found for positive vs. neutral affect (71.00±17.93%, p<0.01). In this last case, only three fNIRS channels were sufficient to discriminate between those affective states: two positioned over the left ventrolateral prefrontal area and one over the right lateral orbitofrontal cortex. In conclusion, our results show that fNIRS is a feasible technique for intersubject affective decoding, reaching significant classification accuracies using a few and biologically consistent features.

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The detection and classification of the mental state elicited by humor from EEG patterns

Cited by 5 publications

References 23 publications

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

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

Subject-independent decoding of affective states using functional near-infrared spectroscopy

Subject-independent decoding of affective states using functional near-infrared spectroscopy

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