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

Yuvaraj, Rajamanickam; Murugappan, M.; Ibrahim, Norlinah Mohamed; Sundaraj, Kenneth; Omar, Mohammad Iqbal; Mohamad, Khairiyah; Palaniappan, Ramaswamy

doi:10.1016/j.bspc.2014.07.005

Cited by 74 publications

(56 citation statements)

References 66 publications

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“…Research findings on the reason for this are inconclusive. Nine articles discussed the idea of using EEG-based emotion detection to provide assistance, monitoring, assessment, and diagnosis of Parkinson's disease in patients [47,106,107,114,[118][119][120][121][122].…”

Section: Othermentioning

confidence: 99%

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

et al. 2017

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Abstract:Recent developments and studies in brain-computer interface (BCI) technologies have facilitated emotion detection and classification. Many BCI studies have sought to investigate, detect, and recognize participants' emotional affective states. The applied domains for these studies are varied, and include such fields as communication, education, entertainment, and medicine. To understand trends in electroencephalography (EEG)-based emotion recognition system research and to provide practitioners and researchers with insights into and future directions for emotion recognition systems, this study set out to review published articles on emotion detection, recognition, and classification. The study also reviews current and future trends and discusses how these trends may impact researchers and practitioners alike. We reviewed 285 articles, of which 160 were refereed journal articles that were published since the inception of affective computing research. The articles were classified based on a scheme consisting of two categories: research orientation and domains/applications. Our results show considerable growth of EEG-based emotion detection journal publications. This growth reflects an increased research interest in EEG-based emotion detection as a salient and legitimate research area. Such factors as the proliferation of wireless EEG devices, advances in computational intelligence techniques, and machine learning spurred this growth.

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Section: Othermentioning

confidence: 99%

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

et al. 2017

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“…In order to recognize the depth of rail corrugation easily, bispectrum features need to be extracted. ree amplitude features of bispectrum [21]:…”

Section: Shock and Vibrationmentioning

confidence: 99%

Rail Corrugation Detection of High‐Speed Railway Using Wheel Dynamic Responses

Shi

2019

Shock and Vibration

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Rail corrugation often occurs on the high-speed railway, which will affect ride comfort and even the train operation safety in severe condition. Detection of rail corrugation wavelength and depth is absolutely essential for maintenance and safety. A novel method using wheel vibration acceleration is proposed in this paper, in which ensemble empirical mode decomposition (EEMD) is employed to estimate the wavelength, and bispectrum features are extracted to recognize the depth with support vector machine (SVM). Firstly, a vehicle-track coupling model considering the rail corrugation of high-speed railway is established to calculate the wheel vibration acceleration. Secondly, the estimation algorithm of wavelength is studied by analyzing the main frequency with EEMD. The optimal parameters of EEMD are selected according to the orthogonal coefficient of decomposition results and the distribution of the extreme points of signal. The depth detection is transformed to a classification problem with SVM. Bispectrum features, which are extracted from the reconstructed signal using the high-frequency components of wheel vibration acceleration, combining with train speed and corrugation wavelength are input into SVM to recognize the rail corrugation depth. Finally, numerical simulation is carried out to verify the accuracy of the proposed estimation method. The simulation results show that the proposed detection algorithm can accurately identify rail corrugation, the estimation error of rail corrugation wavelength is less than 0.25%, and the classification accuracy of rail corrugation depth is more than 99%.

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“…In this systematic review, we reported an author (Yuvaraj) who has worked in the emotional field with 4 studies (Yuvaraj, Murugappan, Mohamed Ibrahim, et al, 2014) . In these studies, the emotional stimuli was caused by emotions such us sadness, happiness, fear, anger and surprise.…”

Section: Emotional Eventmentioning

confidence: 99%

Review of prodromal symptoms in Parkinson's Disease detected by MRI, EEG And microbiome

Echeverri¹,

Vayá²,

Mateo³

et al. 2019

Preprint

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Context: Parkinson’s disease (PD) is catalogued as a disorder that causes motor symptoms; the evidence of literature shows the PD starts with non-motor signs, which can be detected in prodromal phases. These previous phases can be analyzed and studied through magnetic resonance images (MRI), electroencephalography (EEG) and microbiome.Objective: To systematically review the areas of the brain and brain-gut axis which affect in early Parkinson’s disease that can possibly be visualized and analyzed by MRI, EEG and the microbiome.Evidence acquisition: Pubmed and Embase databases were used until July 30, 2018 as to search for early Parkinson’s disease at its earliest non-motor symptoms stage by using MRI, EEG, and microbiome. The search was performed according to the requirements of a systematic review. In order to identify reports, we evaluated them following the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) criteria. Evidence synthesis: MRI and EEG have provided the advances to find features for PD over the last decade. Those techniques identify motor symptoms on substantia nigra where the patient shows a dopamine deficiency. However, over recent years, researchers have found that PD has prodromal phases, that is, PD is not simply a neurodegenerative disorder characterized by the dysfunction of dopaminergic. Thus, high field MRI, event-related potential (ERP) and microbiota data shows a significant change on the brain cortex, white and grey matter, the extrapyramidal system, brain signals and the gut.Conclusion: The structural MRI is a useful technique in detecting the stages of motor symptoms on the substantia nigra in patients with PD. The use of magnetic resonance as an early detector requires a high magnetic field, as to identify the areas which diagnose that the patient could be in the premotor stages. On the other hand, EEG performed well in detecting PD features. Furthermore, microbiome sequencing might include the classification of bacterial families that could help to detect PD in its prodromal phase. Thus, the combination of all these techniques can support the possibility of diagnosing PD in its very early stages.

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

Cited by 74 publications

References 66 publications

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

Rail Corrugation Detection of High‐Speed Railway Using Wheel Dynamic Responses

Review of prodromal symptoms in Parkinson's Disease detected by MRI, EEG And microbiome

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