Mental stress assessment using simultaneous measurement of EEG and fNIRS

Al-Shargie, Fares; Kiguchi, Masashi; Badruddin, Nasreen; Dass, Sarat C.; Hani, Ahmad Fadzil Mohammad; Tang, Tong Boon

doi:10.1364/boe.7.003882

Cited by 221 publications

(188 citation statements)

References 102 publications

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“…Under fusion of both modalities, the study reported 98% classification accuracy indicating that fNIRS and EEG modality complement each other in obtaining features that highly correlated with mental stress. This improvement is in line with our previous stress studies [21][22][23][24][25][26][27][28][29][30][31][32].…”

Section: B Eegsupporting

confidence: 92%

Fusion of fNIRS and EEG Signals: Mental Stress Study

Al-Shargie¹

2019

Preprint

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Fusion of Functional Near infrared Spectroscopy (fNIRS) and Electroencephalograph (EEG) is a novel approach. This study aims in improving the detection rate of mental stress using the complementary nature of fNIRS and EEG neuroimaging modality. Simultaneous measurements of fNIRS and EEG signals were conducted on 12 subjects while solving arithmetic problems under two different conditions (control and stress). The stress in this work was based on arithmetic task difficulty, time pressure and negative feedback of individual performance. The study demonstrated significant reduction in the concentration of oxygenated hemoglobin (p=0.0032) and alpha rhythm power (p=0.0213) on the PFC under stress condition. Specifically, the right PFC and dorsolateral PFC were highly sensitive to mental stress. Using support vector machine (SVM), the mean detection rate of mental stress was calculated as 91%, 95% and 98% using fNIRS, EEG and fusion of fNIRS and EEG signals respectively.

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Section: B Eegsupporting

confidence: 92%

Fusion of fNIRS and EEG Signals: Mental Stress Study

Al-Shargie¹

2019

Preprint

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“…Experimental evidence that clinical depression involves hyperactivity in depressive circuit loops comes from a study by Mazarati et al, who found that when the level of neuronal excitability was experimentally increased by repeated subconvulsive stimulation of the brain, the laboratory animals began to demonstrate depressive-like behavior [23]. This observation, taken together with the observation that depressive symptoms in susceptible individuals commonly develop in association with the circuit-specific stimulatory effects of severe or recurrent psychosocial stress [117], is compelling evidence that clinical depression is a manifestation of hyperactivity in depressive brain circuitry. Lending further support to this hypothesis is the observation that the increased vulnerability to depression that is fueled by persistent psychosocial stress persists for about the same length of time as an experimentally-induced kindling effect [118].…”

Section: Experimental Evidencementioning

confidence: 99%

The Multi-Circuit Neuronal Hyperexcitability Hypothesis of Psychiatric Disorders

Binder¹

2019

AJCEM

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After nearly a century of intensive research and clinical investigation, the pathophysiology of psychiatric disorders remains an enigma. Short of a clear understanding of how psychiatric symptoms are produced, the various cognitive, emotional, and behavior patterns that characterize psychiatric disorders continue to be grouped into syndromes and treated accordingly. The weakness of this approach is that the treatment is administered without a clear understanding of what pathological process is being treated. Moreover, the symptoms of most psychiatric disorders are frequently changing and melding into one another. This leads to diagnostic confusion, medication stacking, and poor treatment outcomes, all of which erode patient trust and perpetuate the stigma of mental illness. In this seminal report, a new hypothesis on the pathophysiology of psychiatric disorders will be presented based on multidisciplinary evidence that nearly all psychiatric disorders and their functional comorbidities are rooted in a single, shared, neurophysiological abnormality. The report will then trace that abnormality to its molecular roots and introduce a new paradigm through which the many faces of mental illness can be understood and uniformly treated. The Multi-Circuit Neuronal Hyperexcitability hypothesis of psychiatric disorders posits that an inherent hyperexcitability of the neurological system is at the root of mental illness and provides a precise, functionallyspecific framework that eliminates diagnostic confusion, informs a unified treatment approach, and helps remove the long-held stigma of mental illness. + Itemized comparison showing that the same conditions and chemicals that increase the potential for seizures (upper section) also increase the potential for psychiatric symptoms, and the same conditions and chemicals that decrease the potential for seizures (lower section) also decrease the potential for psychiatric symptoms.

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“…That is, many non-stressful epochs were wrongly identified as stressful epochs, limiting the utility of HRV in this context (55,56). Several recent studies have attempted to combine different physiological measures to improve the ability of an algorithm to identify stress states (24,25,29,33,(38)(39)(40)(41)(42). By combining select features from these physiological modalities using a support vector machine, we achieved high sensitivity (91%) and specificity (100%)-indeed more than that achieve using any independent measure alone (Supplementary Table 2).…”

Section: Identifying Stressful States and Non-stressful Statesmentioning

confidence: 99%

“…While biochemical assays are invasive, expensive, not amenable to continuous measurements, and vulnerable to diurnal cycles (18)(19)(20)(21), electrophysiological measures are non-invasive and more suitable for continuous monitoring. Measures like galvanic skin response (GSR) (22)(23)(24)(25)(26)(27)(28)(29), heart rate (HR) (24)(25)(26)28,30), heart rate variability (HRV) (27,31,32), and electroencephalography (EEG) (33)(34)(35)(36) are increasingly being used to assess stress response. The relationship between these physiological measures and stress response, and both state and trait anxiety, however, remains poorly understood (37).…”

Section: Introductionmentioning

confidence: 99%

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A machine learning approach to identifying objective biomarkers of anxiety and stress

Ramakrishnan

Pardes²,

Lynch³

et al. 2019

Preprint

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Anxiety and stress-related disorders are highly prevalent and debilitating conditions that impose an enormous burden on society. Sensitive measurements that can enable early diagnosis could mitigate suffering and potentially prevent onset of these conditions. Self-reports, however, are intrusive and vulnerable to biases that can conceal the true internal state.Physiological responses, on the other hand, manifest spontaneously and can be monitored continuously, providing potential objective biomarkers for anxiety and stress. Recent studies have shown that algorithms trained on physiological measurements can predict stress states with high accuracy. Whether these predictive algorithms generalize to untested situations and participants, however, remains unclear. Further, whether biomarkers of momentary stress indicate trait anxiety -a vulnerability foreshadowing development of anxiety and mood disorders -remains unknown. To address these gaps, we monitored skin conductance, heart rate, heart rate variability and EEG in 39 participants experiencing physical and social stress and compared these measures to non-stressful periods of talking, rest, and playing a simple video game. Self-report measures were obtained periodically throughout the experiment. A support vector machine trained on physiological measurements identified stress conditions with ~96% accuracy. A decision tree that optimally combined physiological and self-report measures identified individuals with high trait anxiety with ~84% accuracy. Individuals with high trait anxiety also displayed high baseline state anxiety but a muted physiological response to acute stressors. Overall, these results demonstrate the potential for using machine learning tools to identify objective biomarkers useful for diagnosing and monitoring mental health conditions like anxiety and depression.

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Mental stress assessment using simultaneous measurement of EEG and fNIRS

Cited by 221 publications

References 102 publications

Fusion of fNIRS and EEG Signals: Mental Stress Study

Fusion of fNIRS and EEG Signals: Mental Stress Study

The Multi-Circuit Neuronal Hyperexcitability Hypothesis of Psychiatric Disorders

A machine learning approach to identifying objective biomarkers of anxiety and stress

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