Brain functional connectivity patterns for emotional state classification in Parkinson’s disease patients without dementia

Yuvaraj, Rajamanickam; Murugappan, M.; Acharya, U. Rajendra; Adeli, Hojjat; Ibrahim, Norlinah Mohamed; Mesquita, Edgar

doi:10.1016/j.bbr.2015.10.036

Cited by 132 publications

(57 citation statements)

References 73 publications

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“…The advantages and limitation of this approach will not be addressed here, as they go beyond the scope of this paper. Our aim was here to highlight how the choice of the reference affects the estimation brain connectivity inferred from scalp EEG, which still remains a standard practice for many research or clinical applications (e.g., Carlino et al 2015, Herrera-Díaz et al 2015, van Straaten et al 2015, Ligeza et al 2016, Naro et al 2016, Wang et al 2016, Yuvaraj et al 2016.…”

Section: General Comments On Reference-free Approachesmentioning

confidence: 99%

Impact of the reference choice on scalp EEG connectivity estimation

et al. 2016

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Section: General Comments On Reference-free Approachesmentioning

confidence: 99%

Impact of the reference choice on scalp EEG connectivity estimation

et al. 2016

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“…However, only certain types of emotions can be recognized using EEG. Moreover, finding key emotional states to be recognized is mandatory; for example, six emotions were detected in [42,45,106,120,122,192], whereas in [108], a real-time EEG signal to classify happy and unhappy emotions was proposed, and in [113], a fear evaluation system was proposed. In our review, we found that most of the articles aim to detect unpleasant, pleasant, and neutral emotions, such as in [105,217], or positive, negative, and neutral emotions that are based on the valence-arousal dimensional emotion model, as in [159,206].…”

Section: Eeg Correlates Of Emotion (Signals)mentioning

confidence: 99%

“…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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“…[5][6][7] Neurological diseases may affect different levels of speech production, as phonation and articulation or tone [8][9][10][11][12] as well as improper speech planning and emotional impairment. 13 The present work is intended to explore the connections between neuromotor actions on certain articulation muscles and meaningful acoustical correlates, as speech formants in Parkinson's Disease (PD). The working hypothesis assumes that the kinematic properties of acoustic-phonetic variables derived from the first two speech formants (F 1 , F 2 ) are the direct consequence of the activity of articulation muscles (mainly the masseter), and that surface electromyographic (sEMG) signals 14,15 measured on this muscle might be highly correlated with the neuromotor actions governing muscle contractions.…”

Section: Introductionmentioning

confidence: 99%

Neuromechanical Modelling of Articulatory Movements from Surface Electromyography and Speech Formants

Gómez-Vilda

Gómez-Rodellar

Vicente

et al. 2019

Int. J. Neur. Syst.

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Speech articulation is produced by the movements of muscles in the larynx, pharynx, mouth and face. Therefore speech shows acoustic features as formants which are directly related with neuromotor actions of these muscles. The first two formants are strongly related with jaw and tongue muscular activity. Speech can be used as a simple and ubiquitous signal, easy to record and process, either locally or on e-Health platforms. This fact may open a wide set of applications in the study of functional grading and monitoring neurodegenerative diseases. A relevant question, in this sense, is how far speech correlates and neuromotor actions are related. This preliminary study is intended to find answers to this question by using surface electromyographic recordings on the masseter and the acoustic kinematics related with the first formant. It is shown in the study that relevant correlations can be found among the surface electromyographic activity (dynamic muscle behavior) and the positions and first derivatives of the first formant (kinematic variables related to vertical velocity and acceleration of the joint jaw and tongue biomechanical system). As an application example, it is shown that the probability density function associated to these kinematic variables is more sensitive than classical features as Vowel Space Area † †

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Brain functional connectivity patterns for emotional state classification in Parkinson’s disease patients without dementia

Cited by 132 publications

References 73 publications

Impact of the reference choice on scalp EEG connectivity estimation

Impact of the reference choice on scalp EEG connectivity estimation

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

Neuromechanical Modelling of Articulatory Movements from Surface Electromyography and Speech Formants

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