EEG/PPG effective connectivity fusion for analyzing deception in interview

Kohan, Marzieh Daneshi; Nasrabadi, Ali Motie; Shamsollahi, Mohammad Bagher; Sharifi, Ali

doi:10.1007/s11760-019-01622-1

Cited by 9 publications

(10 citation statements)

References 24 publications

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“…Physical stress is induced by cold pressor task [70], [71], rope skipping [25], fatiguing exercise [22], [56], handgrip task [44], [75]. Cognition is evaluated using go/no go task [45], surgical task [74], audio stimuli [29], images [67], [69], visual stimuli [41], film clips [77], reading comic strips [73], audio visual stimuli [43], gamming [57], and deceptive task [28]. Sleep based task is also done in some cases namely [21], [27], [30], [34], [35], [49], [59], [60], [66].…”

Section: ) Comparison Of Various Types Of Applicationsmentioning

confidence: 99%

“…Some of the important parameter extracted for statistical analysis are brain heart interplay coupling coefficient [48], [70], [71], directed transfer function [28], [37], [58], partially directed coherence [28], [67], modulation index [21], [24], [39], [63], slow oscillations -heart rate intervals [49], multiscale fuzzy measure entropy [25], Wiener-Granger causality interactions [66], system in error range (SIE), time percentage for system in transition zone (SIT), absolute system error (ASE) [55], coherence index [22], clustering coefficient [22], local efficiency [22], global efficiency [22], characteristic path length [22], time-delayed maximal information coefficient [56], transfer spectral entropy [75], Granger causality [75], cortico-muscular coherence [44], cortico-cortical coher-ence [44], stress index [33], [61], cardiac vagal index [79], sympathetic index [62], [79], sympathovagal balance index [79], parasympathetic index [62], phase-amplitude coupling (PAC) profile [24].…”

Section: ) Statistical Parameter Extractionmentioning

confidence: 99%

“…Another type of popular psycho-physiological state for classification is cognition. Where various studies have classified between truth and lie [28], low, high, normal and very high cognitive task load level [55], [74], funny, not funny [73], low or high trust [57], [69], and High /low interest [77]. Stress related classification is done to know about stress level [22], [62], [72], [76].…”

Section: ) Classificationmentioning

confidence: 99%

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Exploring Central-Peripheral Nervous System Interaction Through Multimodal Biosignals: A Systematic Review

Banik,

Kumar,

Ganapathy

et al. 2024

IEEE Access

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The interaction between the central nervous system (CNS) and peripheral nervous system (PNS) governs various physiological functions, influences cognitive processes and emotional states. It is necessary to unravel the mechanisms governing the interaction between the brain and the body, enhancing our understanding of physical and mental well-being. Neuro-ergonomics-based human-computer interaction can be improved by comprehending the intricate interrelation between the CNS and PNS. Various studies have been explored using diverse methodologies to study CNS-PNS interaction in specific psychophysiological states, such as emotion, stress, or cognitive tasks. However, there is a need for a thorough, extensive, and systematic review covering diverse interaction forms, applications, and assessments. In this work, an attempt has been made to perform a systematic review that examines the interaction between the CNS and PNS across diverse psychophysiological states, focusing on varied physiological signals. For this, scientific repositories, namely Scopus, PubMed, Association for Computing Machinery, and Web of Science, are accessed. In total, 61 articles have been identified within the period of January 2008 to April 2023 for systematic review. The selected research articles are analyzed based on factors, namely subject information, stimulation modality, types of interactions between the brain and other organs, feature extraction techniques, classification methods, and statistical approaches. The evaluation of the existing literature indicates a scarcity of publicly available databases for CNS-PNS interaction and limited application of machine learning and deep learning-based advanced tools. Furthermore, this review underscores the urgent need for enhancements in several key areas including the development of a more refined psycho-physiological model, improved analysis techniques, and better electrode-surface interface technology. Additionally, there is a need for more research involving daily life activities, female-oriented studies, and privacy considerations. This review contributes to standardizing protocols, improves the diagnostic relevance of various instruments, and extracts more reliable biomarkers. The novelty of this study lies in guiding researchers to point out various issues and potential solutions for future research in the field of bio-signal-based CNS-PNS interaction. INDEX TERMS Central Nervous System (CNS), Peripheral Nervous System (PNS), Electroencephalogram (EEG), Brain-heart, Brain-skin, Brain-muscle, differentiation, classification I. INTRODUCTION T HE perception of human existence consists of thoughts, dreams, and memories. These phenomena are controlled by the interactions that take place between the human brain, body, and the external environment. Such interactions primarily rely on the transmission of information through neu-ral pathways. These pathways connect the brain to various sensors and actuators distributed throughout the human body [1]. The nervous system is remarkably intricate. It en...

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Section: ) Comparison Of Various Types Of Applicationsmentioning

confidence: 99%

Section: ) Statistical Parameter Extractionmentioning

confidence: 99%

Section: ) Classificationmentioning

confidence: 99%

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Exploring Central-Peripheral Nervous System Interaction Through Multimodal Biosignals: A Systematic Review

Banik,

Kumar,

Ganapathy

et al. 2024

IEEE Access

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“…This approach has gained interest in recent years 14–19 . Feature-based: this approach extracts EEG and heart-rate variability (HRV) features from continuous signals and examines associations between them. This has been done independently for EEG (often termed quantitative EEG or qEEG 20 ), ECG 21–23 , and PPG 24–26 . This approach presents promising applications by capturing both state-and trait-related variables.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 2.5. Scalp topographies illustrating the distribution of the main EEG features extracted byBrainBeats from the 64-channel EEG data, including spectral power in the delta (1-3 Hz), theta(3)(4)(5)(6)(7), alpha(8)(9)(10)(11)(12)(13), beta(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40) frequency bands, the individual alpha frequency (IAF), fuzzy entropy, fractal dimension, and alpha asymmetry. Notes: Greater fuzzy entropy values reflect higher complexity in predictability and regularity, whereas the fractal dimension reflects greater complexity in terms of geometry and fractal characteristics.…”

mentioning

confidence: 99%

BrainBeats, an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

Cannard

Wahbeh

Delorme

2023

Preprint

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The link between the cortical and cardiovascular systems is garnering increased attention due to its potential to offer valuable insights into brain and heart function coupling. Current joint analysis methodologies largely involve invasive or high-cost neuroimaging methods. EEG and ECG/PPG, however, provide non-invasive, cost-effective, and portable alternatives enabling broader data collection in both laboratory and clinical settings. However, the analysis of these biosignals is challenging for scalable applications due to their complex nature. Existing research and tools often lack consensus in processing and statistical methodologies, easy-to-use user interface, or batch processing capacity of large datasets, impeding reproducibility. A further void exists in standardized methods for EEG and heart-rate variability (HRV) feature extraction, undermining clinical diagnostics or the robustness of machine learning models. We introduce the BrainBeats toolbox in response to these challenges, an open-source EEGLAB plugin providing an suite of signal processing and feature-extraction functions adhering to current guidelines and recommendations. The toolbox integrates three main protocols: 1) Heartbeat-evoked potentials (HEP) and oscillations (HEO); 2) EEG and HRV feature extraction; 3) Automated removal of heart artifacts from EEG signals. Accompanied by sample data and guidance, BrainBeats aims to facilitate brain-heart interplay research and reproducibility. This open-source toolbox offers a valuable resource for clinicians and researchers studying brain-heart interactions and can be tailored to unique research needs.

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Brain Connectivity and Its Applications

2021

EEG Signal Processing and Machine Learning

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EEG/PPG effective connectivity fusion for analyzing deception in interview

Cited by 9 publications

References 24 publications

Exploring Central-Peripheral Nervous System Interaction Through Multimodal Biosignals: A Systematic Review

Exploring Central-Peripheral Nervous System Interaction Through Multimodal Biosignals: A Systematic Review

BrainBeats, an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

Brain Connectivity and Its Applications

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