Investigating Neurofeedback Protocols for Stress Mitigation: A Comparative Analysis of Different Stimulus Contents

Hafeez, Yasir; Ali, Syed Saad Azhar; Mumtaz, Wajid; Moinuddin, Muhammad; Adil, Syed Hasan; Al‐Saggaf, Ubaid M.; Yasin, Mohd Azhar Bin Mohd; Malik, Aamir Saeed

doi:10.1109/access.2019.2944202

Cited by 13 publications

(34 citation statements)

References 69 publications

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“…It is reported in the literature that negative emotions and withdrawal behaviours [37][38][39][40] affect the prefrontal alpha asymmetry. Several researchers have used AAS value for stress assessment [40][41][42][43]. In general, a low value of alpha asymmetry indicates that the participant is in stress [5,19].…”

Section: Description Of Datasetmentioning

confidence: 99%

Real-Time Stress Assessment Using Sliding Window Based Convolutional Neural Network

Naqvi

Ali

Yahya

et al. 2020

Sensors

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Mental stress has been identified as a significant cause of several bodily disorders, such as depression, hypertension, neural and cardiovascular abnormalities. Conventional stress assessment methods are highly subjective and tedious and tend to lack accuracy. Machine-learning (ML)-based computer-aided diagnosis systems can be used to assess the mental state with reasonable accuracy, but they require offline processing and feature extraction, rendering them unsuitable for real-time applications. This paper presents a real-time mental stress assessment approach based on convolutional neural networks (CNNs). The CNN-based approach afforded real-time mental stress assessment with an accuracy as high as 96%, the sensitivity of 95%, and specificity of 97%. The proposed approach is compared with state-of-the-art ML techniques in terms of accuracy, time utilisation, and quality of features.

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Section: Description Of Datasetmentioning

confidence: 99%

Real-Time Stress Assessment Using Sliding Window Based Convolutional Neural Network

Naqvi

Ali

Yahya

et al. 2020

Sensors

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“…Rapid advancement in sensor technologies has allowed simpler and cheaper setup that integrates EEG with the VR technology. It has been used in various settings such as healthcare [ 9 , 10 , 11 , 12 ], oil production [ 13 , 14 ], workplace [ 15 ] and education [ 16 ]. With the integration of EEG, researchers have studied various brain functions such as attention [ 17 , 18 ], cognitive load [ 19 , 20 ], motor intention and planning [ 11 ], emotional processing [ 21 , 22 ], sense of immersion [ 23 , 24 ], engagement [ 25 ] and relaxation [ 26 , 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

Workplace Mental State Monitoring during VR-Based Training for Offshore Environment

Hasan

Sulaiman

Ashykin

et al. 2021

Sensors

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Adults are constantly exposed to stressful conditions at their workplace, and this can lead to decreased job performance followed by detrimental clinical health problems. Advancement of sensor technologies has allowed the electroencephalography (EEG) devices to be portable and used in real-time to monitor mental health. However, real-time monitoring is not often practical in workplace environments with complex operations such as kindergarten, firefighting and offshore facilities. Integrating the EEG with virtual reality (VR) that emulates workplace conditions can be a tool to assess and monitor mental health of adults within their working environment. This paper evaluates the mental states induced when performing a stressful task in a VR-based offshore environment. The theta, alpha and beta frequency bands are analysed to assess changes in mental states due to physical discomfort, stress and concentration. During the VR trials, mental states of discomfort and disorientation are observed with the drop of theta activity, whilst the stress induced from the conditional tasks is reflected in the changes of low-alpha and high-beta activities. The deflection of frontal alpha asymmetry from negative to positive direction reflects the learning effects from emotion-focus to problem-solving strategies adopted to accomplish the VR task. This study highlights the need for an integrated VR-EEG system in workplace settings as a tool to monitor and assess mental health of working adults.

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“…Thus, neurofeedback can train a person to intervene against dysfunctional neural signals and generate stable signals to facilitates improvement in health conditions without any external intervention [4]. Neurofeedback training can have several applications for the treatment of psychiatric illnesses including depression [5], and stress [6]- [8]. Neurofeedback may also help to improve the overall stability and communication of neural networks between and within the hemispheres [4].…”

Section: Introductionmentioning

confidence: 99%

“…Neurofeedback may also help to improve the overall stability and communication of neural networks between and within the hemispheres [4]. Therefore, neurofeedback is being used focusing on alpha asymmetry [8] in the frontal regions of the brain can produce positive results for stress reduction [9], [10]. The efficacy of neurofeedback is subjective in its definition [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Development of Enhanced Stimulus Content to Improve the Treatment Efficacy of EEG–Based Frontal Alpha Asymmetry Neurofeedback for Stress Mitigation

Hafeez

Ali²,

Hasan

et al. 2021

IEEE Access

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The neurofeedback stimulus content has direct implications for the efficacy of the psychophysiological applications for neurofeedback modality. In particular, enhancements of neurofeedback stimulus content can facilitate improvements in the efficacy of neurofeedback applications in clinical practice. To further elaborate on this aspect, this study introduced systematic enhancements in neurofeedback stimulus content by developing enhanced neurofeedback stimulus content for stress mitigation. The enhancements included the automatic selection of color of neurofeebdack stimulus content environment and instruction messages, as well as, the adaptive selection of threshold of quantitative electroencephalogram (QEEG) features, such as frontal alpha power and frontal alpha asymmetry. The enhancements were based on the outcomes from previous research on the selection of neurofeedback stimulus content for stress mitigation. The improvement in the efficacy of neurofeedback stimulus content was measured statistically by comparing the QEEG and topographic maps. In this study, electroencephalogram data from 20 participants were acquired during multiple sessions of neurofeedback. Analysis of variance and a post hoc test were used to verify the improvement in the efficacy of the neurofeedback application for stress mitigation after the enhancements of the neurofeedback stimulus content; a t-test was used to verify the statistical significance of the stress mitigation by the neurofeedback p < 0.05. The results indicate that the enhancement of the developed neurofeedback stimulus content facilitated stress mitigation during the early sessions of neurofeedback. In conclusion, the efficacy of neurofeedback can be improved using the developed stimulus content with enhancements.

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Investigating Neurofeedback Protocols for Stress Mitigation: A Comparative Analysis of Different Stimulus Contents

Cited by 13 publications

References 69 publications

Real-Time Stress Assessment Using Sliding Window Based Convolutional Neural Network

Real-Time Stress Assessment Using Sliding Window Based Convolutional Neural Network

Workplace Mental State Monitoring during VR-Based Training for Offshore Environment

Development of Enhanced Stimulus Content to Improve the Treatment Efficacy of EEG–Based Frontal Alpha Asymmetry Neurofeedback for Stress Mitigation

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