A Comprehensive Study on Emotional State Analysis of Humans from EEG Signals

Emotion plays an important role in the daily life of man and is an important feature of human interaction. Because of its role of adaptation, it motivates people to respond quickly to stimuli in their environment to improve communication, learning and decision making. With the increasing role of the brain-computer interface (BCI) in user-computer interaction, automatic recognition of emotions has become an area of interest in the last decade. The recognition of emotions could be facial expression, gesture, speech and text and could be recorded in different ways, such as electroencephalogram (EEG), positron emission tomography (PET), magnetic resonance imaging (MRI), etc. In this research work, feature extraction feature reduction and classification of emotions have been evaluated on different methods to recognize and classify different emotional states such as fear, sad, frustrated, happy, pleasant and satisfied from inner emotion EEG signals.

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Radiology of the head and neck

10.1093/med/9780199682874.003.0218 ◽

2021 ◽

pp. 973-976

Author(s):

Ivan Zammit-Maempel

Keyword(s):

Head And Neck ◽

Imaging Techniques ◽

Cone Beam Ct ◽

Neck Mass ◽

Positron Emission ◽

Pet Ct ◽

Magnetic Resonance Imaging Mri ◽

Video Fluoroscopy

Various imaging techniques are used in the staging and follow-up of head and neck cancer and evaluating patients presenting with a neck mass. The workhorses in imaging the neck are ultrasonography, computed tomography (CT), and magnetic resonance imaging (MRI) with positron emission tomography CT (PET-CT) increasingly being requested. Plain radiographs, contrast studies, video fluoroscopy, angiography, and cone beam CT have limited but important roles. This chapter discusses the role of some of these modalities.

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EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities

Computational Intelligence and Neuroscience ◽

10.1155/2020/8875426 ◽

2020 ◽

Vol 2020 ◽

pp. 1-19

Author(s):

Nazmi Sofian Suhaimi ◽

James Mountstephens ◽

Jason Teo

Keyword(s):

Emotion Recognition ◽

State Of The Art ◽

Research Community ◽

Human Interaction ◽

Human Cognition ◽

Future Research ◽

Emotional States ◽

Human Beings ◽

Eeg Signals ◽

Recent Developments

Emotions are fundamental for human beings and play an important role in human cognition. Emotion is commonly associated with logical decision making, perception, human interaction, and to a certain extent, human intelligence itself. With the growing interest of the research community towards establishing some meaningful “emotional” interactions between humans and computers, the need for reliable and deployable solutions for the identification of human emotional states is required. Recent developments in using electroencephalography (EEG) for emotion recognition have garnered strong interest from the research community as the latest developments in consumer-grade wearable EEG solutions can provide a cheap, portable, and simple solution for identifying emotions. Since the last comprehensive review was conducted back from the years 2009 to 2016, this paper will update on the current progress of emotion recognition using EEG signals from 2016 to 2019. The focus on this state-of-the-art review focuses on the elements of emotion stimuli type and presentation approach, study size, EEG hardware, machine learning classifiers, and classification approach. From this state-of-the-art review, we suggest several future research opportunities including proposing a different approach in presenting the stimuli in the form of virtual reality (VR). To this end, an additional section devoted specifically to reviewing only VR studies within this research domain is presented as the motivation for this proposed new approach using VR as the stimuli presentation device. This review paper is intended to be useful for the research community working on emotion recognition using EEG signals as well as for those who are venturing into this field of research.

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Role of Imaging in Rectal Cancer

JMS SKIMS ◽

10.33883/jms.v15i1.102 ◽

2012 ◽

Vol 15 (1) ◽

pp. 4-6

Author(s):

Ajaz Ahmad Malik

Keyword(s):

Rectal Cancer ◽

Rectal Wall ◽

Preoperative Staging ◽

Positron Emission ◽

Adjacent Structures ◽

Tumor Involvement ◽

Magnetic Resonance Imaging Mri ◽

Optimal Treatment Strategy

THIS ARTICLE HAS NO ABSTRACT (FIRST 100 WORDS OF THE ARTICLE ARE DISPLAYED): Staging of rectal cancer is necessary to provide the optimal treatment strategy although proctoscopy or sigmoidoscopy with biopsy are diagnostic. This is achieved by locoregional assessment of the disease by various available radiological investigations. Staging information includes extent of tumor involvement of the rectal wall and adjacent structures, presence or absence of adjacent lymphadenopathy, and determination of distant metastasis. Several modalities exist for the preoperative staging of rectal cancer, like computed tomography (CT); magnetic resonance imaging (MRI) with traditional body, endorectal, or phasedarray coils; endorectal ultrasonography (ERUS) with rigid or flexible probes; and positron emission tomography (PET) with and without. JMS 2012;15(1):4-6.

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Automatic Prediction and Assessment of Treatment Response in Patients with Hodgkin’s Lymphoma Using a Whole-Body DW-MRI Based Approach

Diagnostics ◽

10.3390/diagnostics10090702 ◽

2020 ◽

Vol 10 (9) ◽

pp. 702

Author(s):

Valentina Brancato ◽

Marco Aiello ◽

Roberta Della Pepa ◽

Luca Basso ◽

Nunzia Garbino ◽

...

Keyword(s):

Treatment Response ◽

Hodgkin’S Lymphoma ◽

Hodgkin's Lymphoma ◽

Response To Treatment ◽

Whole Body ◽

Positron Emission ◽

End Of Treatment ◽

Tumor Load ◽

Magnetic Resonance Imaging Mri

The lack of validation and standardization represents the main drawback for a clear role of whole-body diffusion weighted imaging (WB-DWI) for prediction and assessment of treatment response in Hodgkin’s lymphoma (HL). We explored the reliability of an automatic approach based on the WB-DWI technique for prediction and assessment of response to treatment in patients with HL. The study included 20 HL patients, who had whole-body positron emission tomography (PET)/ magnetic resonance Imaging (MRI) performed before, during and after chemotherapy. Using the syngo.via MR Total Tumor Load tool, we automatically extracted values of diffusion volume (DV) and its associated histogram features by WB-DWI images, and evaluated their utility in predicting and assessing interim and end-of-treatment (EOT) response. The Mann–Whitney test followed by receiver operator characteristic (ROC) analysis was performed between features and their inter-time point percentage differences for patients having a complete or partial treatment response, revealing that several WB-DWI associated features allowed for prediction of interim response and both prediction and assessment of EOT response. Our proposed method offers huge advantages in terms of saving time and work, enabling clinicians to draw conclusions relating to HL treatment response in a fully automatic way, and encloses, also, all DWI advantages compared to PET/ computed tomography (CT).

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Scatter Artifact with Ga-68-PSMA-11 PET: Severity Reduced With Furosemide Diuresis and Improved Scatter Correction

Molecular Imaging ◽

10.1177/1536012118811741 ◽

2018 ◽

Vol 17 ◽

pp. 153601211881174 ◽

Cited By ~ 2

Author(s):

Courtney Lawhn-Heath ◽

Robert R. Flavell ◽

David E. Korenchan ◽

Timothy Deller ◽

Spencer Lake ◽

...

Keyword(s):

Scatter Correction ◽

Emission Tomography ◽

Whole Body ◽

Correction Algorithm ◽

Resonance Imaging ◽

Positron Emission ◽

Psma Pet ◽

Magnetic Resonance Imaging Mri ◽

Specific Membrane

Purpose: To assess the utility of furosemide diuresis and the role of an improved scatter correction algorithm in reducing scatter artifact severity on Ga-68- Prostate-specific membrane antigen (PSMA)-11 positron emission tomography (PET). Materials and Methods: A total of 139 patients underwent Ga-68-PSMA-11 PET imaging for prostate cancer: 47 non-time-of-flight (non-TOF) PET/computed tomography, 51 PET/magnetic resonance imaging (MRI) using the standard TOF scatter correction algorithm, and 41 PET/MRI using an improved TOF scatter correction algorithm. Whole-body PET acquisitions were subdivided into 3 regions: around kidneys; between kidneys and bladder; and around bladder. The images were reviewed, and scatter artifact severity was rated using a Likert-type scale. Results: The worst scatter occurred when using non-TOF scatter correction without furosemide, where 42.1% of patients demonstrated severe scatter artifacts in 1 or more regions. Improved TOF scatter correction resulted in the smallest percentage of studies with severe scatter (6.5%). Scatter ratings by region were lowest using improved TOF scatter correction. Furosemide reduced mean scatter severity when using non-TOF and standard TOF. Conclusions: Both furosemide and scatter correction algorithm play a role in reducing scatter in PSMA PET. Improved TOF scatter correction resulted in the lowest scatter severity.

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Neuroimaging in Migraine—Recent Advances and Perspectives for the Future

US Neurology ◽

10.17925/usn.2010.06.01.82 ◽

2010 ◽

Vol 06 (01) ◽

pp. 82

Author(s):

Brian K Day ◽

David W Dodick ◽

Todd J Schwedt ◽

◽

...

Keyword(s):

White Matter ◽

White Matter Lesions ◽

Medical Decision ◽

Positron Emission ◽

Magnetic Resonance Imaging Mri ◽

The Individual ◽

Sites Of Action ◽

Functional Connectivity Mri ◽

Neuroimaging Techniques

Migraine is a very common disorder that imposes substantial individual and societal costs. A better understanding of migraine mechanisms may lead to the development of new therapies and thus improve the management of migraine patients. Magnetic resonance imaging (MRI) techniques and positron emission tomography (PET) have revolutionized our understanding of migraine pathophysiology as a primary central nervous system (CNS) disorder, advanced the search for a central migraine generator, clarified the role of cortical spreading depression (CSD) and central sensitization in the pathogenesis of migraine, and revealed some potential sites of action of migraine medications. Structural imaging has shed light on relationships between migraine and stroke, white matter lesions, iron deposition, microstructural brain damage, and other gray and white matter aberrations. Emerging neuroimaging techniques, such as arterial spin labeling (ASL) and functional connectivity MRI (fcMRI), are beginning to provide further evidence of functional brain alterations in migraine patients. Ultimately, it is hoped that advanced neuroimaging will benefit the individual migraine patient by enhancing our diagnostic abilities, allowing for development of better treatments and serving as an important tool in medical decision-making.

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Positron Emission Tomography Imaging of Meningioma in Clinical Practice

Neurosurgery ◽

10.1227/neu.0b013e31823bcd87 ◽

2011 ◽

Vol 70 (4) ◽

pp. 1033-1042 ◽

Cited By ~ 24

Author(s):

Jan Frederick Cornelius ◽

Karl Josef Langen ◽

Gabriele Stoffels ◽

Daniel Hänggi ◽

Michael Sabel ◽

...

Keyword(s):

Positron Emission Tomography ◽

Pet Imaging ◽

Positron Emission Tomography Imaging ◽

Emission Tomography ◽

Imaging Modalities ◽

Future Research ◽

Study Protocols ◽

Positron Emission ◽

Magnetic Resonance Imaging Mri

Abstract Meningiomas represent about 20% of intracranial tumors and are the most frequent nonglial primary brain tumors. Diagnosis is based on computed tomography (CT) and magnetic resonance imaging (MRI). Mainstays of therapy are surgery and radiotherapy. Adjuvant chemotherapy is tested in clinical trials of phase II. Patients are followed clinically by imaging. However, classical imaging modalities such as CT and MRI have limitations. Hence, we need supplementary imaging tools. Molecular imaging modalities, especially positron emission tomography (PET), represent promising new instruments that are able to characterize specific metabolic features. So far, these modalities have only been part of limited study protocols, and their impact on clinical routine management is still under investigation. It may be expected that their extended use will provide new aspects about meningioma imaging and biology. In the present article, we summarize PET imaging for meningiomas based on a thorough review of the literature. We discuss and illustrate the potential role of PET imaging in the clinical management of meningiomas. Finally, we indicate current limitations and outline directions for future research.

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EEG-based emotion recognition with deep convolutional neural networks

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2019-0306 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Mehmet Akif Ozdemir ◽

Murside Degirmenci ◽

Elif Izci ◽

Aydin Akan

Keyword(s):

Neural Networks ◽

Emotion Recognition ◽

Convolutional Neural Networks ◽

Spatial Information ◽

Emotional State ◽

Human Interaction ◽

Emotional States ◽

Eeg Signals ◽

Deep Convolutional Neural Networks ◽

Spectral Topology

AbstractThe emotional state of people plays a key role in physiological and behavioral human interaction. Emotional state analysis entails many fields such as neuroscience, cognitive sciences, and biomedical engineering because the parameters of interest contain the complex neuronal activities of the brain. Electroencephalogram (EEG) signals are processed to communicate brain signals with external systems and make predictions over emotional states. This paper proposes a novel method for emotion recognition based on deep convolutional neural networks (CNNs) that are used to classify Valence, Arousal, Dominance, and Liking emotional states. Hence, a novel approach is proposed for emotion recognition with time series of multi-channel EEG signals from a Database for Emotion Analysis and Using Physiological Signals (DEAP). We propose a new approach to emotional state estimation utilizing CNN-based classification of multi-spectral topology images obtained from EEG signals. In contrast to most of the EEG-based approaches that eliminate spatial information of EEG signals, converting EEG signals into a sequence of multi-spectral topology images, temporal, spectral, and spatial information of EEG signals are preserved. The deep recurrent convolutional network is trained to learn important representations from a sequence of three-channel topographical images. We have achieved test accuracy of 90.62% for negative and positive Valence, 86.13% for high and low Arousal, 88.48% for high and low Dominance, and finally 86.23% for like–unlike. The evaluations of this method on emotion recognition problem revealed significant improvements in the classification accuracy when compared with other studies using deep neural networks (DNNs) and one-dimensional CNNs.

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COVID-19–Associated Bone Marrow Necrosis—A Case Report

Indian Journal of Radiology and Imaging ◽

10.1055/s-0041-1735919 ◽

2021 ◽

Author(s):

Soumyadeep Ghosh ◽

Santosh S. Gupta ◽

Nirad Mehta ◽

Shanaz Khodaiji

Keyword(s):

Bone Marrow ◽

Phase Imaging ◽

Resonance Imaging ◽

Bone Marrow Necrosis ◽

Radiological Features ◽

Positron Emission ◽

Vertebral Bone Marrow ◽

Magnetic Resonance Imaging Mri ◽

Novel Coronavirus

AbstractWe report, herein, a rare case of vertebral bone marrow necrosis in a patient at 1-month post–novel coronavirus disease 2019 (COVID-19) pneumonia complicated with disseminated intravascular coagulation (DIC). The commonly observed radiological features on the imaging modalities like computed tomography (CT), magnetic resonance imaging (MRI), and 18-F fluorodeoxyglucose positron emission tomography (FDG PET) have been discussed here followed by a brief discussion on the role of in-phase and opposed-phase imaging in differentiating the disease from malignant infiltrative pathologies. Histopathological findings on bone marrow smear that confirm the diagnosis have also been illustrated.

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