Enhancement of Closed-Loop Cognitive Stress Regulation Using Supervised Control Architectures

Azgomi, Hamid Fekri; Faghih, Rose T.

doi:10.1109/ojemb.2022.3143686

Cited by 8 publications

(7 citation statements)

References 61 publications

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“…In the future, we plan to develop an improved deep breath detection algorithm (specifically, those responsible for the SCR generation in SC data) by collecting a large dataset and utilizing a data-driven machine learning approach. In conclusion, this study is an important step towards the implementation of SC signal-based ANS activation detection [11]- [13], [46], arousal estimation [5], [14], [15], [53], and the corresponding control design for an effective mobile brain-machine interface architecture for emotional stress management [16], [59]- [61].…”

Section: Discussionmentioning

confidence: 99%

Skin Conductance Response Artifact Reduction: Leveraging Accelerometer Noise Reference and Deep Breath Detection

Amin,

Alam,

Khazaei

et al. 2024

IEEE Access

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Goal: Electrodermal activity (EDA) shows a significant correlation with activation of the autonomic nervous system (ANS) activation. Regular ambulatory monitoring via wearables and consequent inference of ANS activation has a wide range of applications tracking mental health. The real-world implementation of a closed-loop system to regulate one's emotional state to improve their mental wellbeing requires an accurate and reliable estimation of ANS activation in ambulatory settings. Challenge: However, the presence of motion artifacts in skin conductance (SC) data collected in ambulatory settings makes the analysis for such estimation unreliable. Methods: We propose a multi-rate adaptive filtering scheme to reduce motion artifacts in SC data that utilizes three-axis accelerometer data. We investigate four types of linear and nonlinear adaptive filters. We use both simulated and experimental data to investigate the performance of adaptive filters. Furthermore, we utilize the respiration signal to identify the probability of respiration-induced SC artifacts. Next, we use a Bayesian filter-based deconvolution approach to identify SC responses (SCRs) induced by underlying arousal events and deep breaths. Finally, we propose to use the respiration signal to separate the artifacts in SC due to deep breaths. Results: Our results show that linear finite impulse response least squares recursive filters perform best among the four types of adaptive filters studied. We draw this conclusion by obtaining receiver operating characteristics of event-related SCRs detection with deconvolution after artifact reduction with different adaptive filters. Moreover, for all of our simulated and experimental datasets investigated in this study, we observe that the recursive least-squares filter always provides stable results. Additionally, our results show our ability to detect respiration-induced SCRs and the corresponding activation of ANS. Conclusion: The evaluation of adaptive filters shows the potential to utilize reference signals for successful artifact modeling and reduction. Significance: Effective artifact reduction will lead to reliable ANS activation monitoring and consequent robust implementation of a closed-loop wearable machine interface architecture to eventually improve one's mental health.

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Section: Discussionmentioning

confidence: 99%

Skin Conductance Response Artifact Reduction: Leveraging Accelerometer Noise Reference and Deep Breath Detection

Amin,

Alam,

Khazaei

et al. 2024

IEEE Access

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“…In addition to these changes in EEG signal recorded from the muse headband, we analyzed skin conductance signal (as a valid indicator of the internal arousal state 17 , 83 , 89 – 91 ) collected by Empatica wristband. By utilizing skin conductance signals, applying a deconvolution algorithm to extract underlying neural impulses, and employing a state-space approach to relate the underlying neural impulses to the hidden arousal state, we estimated the internal arousal state (Fig.…”

Section: Discussionmentioning

confidence: 99%

“…To estimate the internal arousal state, we analyze the skin conductance signal collected via Empatica E4 wristbands. By applying a deconvolution algorithm and inferring underlying neural impulses, we establish a marked point process Bayesian filter to estimate hidden cognitive arousal state 83 , 91 . For the purpose of statistical analysis, we conducted a one-way analysis of variance (ANOVA) to test for differences in behavioral data, physiological signals, and all derived metrics.…”

Section: Methodsmentioning

confidence: 99%

Regulation of brain cognitive states through auditory, gustatory, and olfactory stimulation with wearable monitoring

Azgomi

Branco

Amin

et al. 2023

Sci Rep

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Inspired by advances in wearable technologies, we design and perform human-subject experiments. We aim to investigate the effects of applying safe actuation (i.e., auditory, gustatory, and olfactory) for the purpose of regulating cognitive arousal and enhancing the performance states. In two proposed experiments, subjects are asked to perform a working memory experiment called n-back tasks. Next, we incorporate listening to different types of music, drinking coffee, and smelling perfume as safe actuators. We employ signal processing methods to seamlessly infer participants’ brain cognitive states. The results demonstrate the effectiveness of the proposed safe actuation in regulating the arousal state and enhancing performance levels. Employing only wearable devices for human monitoring and using safe actuation intervention are the key components of the proposed experiments. Our dataset fills the existing gap of the lack of publicly available datasets for the self-management of internal brain states using wearable devices and safe everyday actuators. This dataset enables further machine learning and system identification investigations to facilitate future smart work environments. This would lead us to the ultimate idea of developing practical automated personalized closed-loop architectures for managing internal brain states and enhancing the quality of life.

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“…This would provide one method of selecting the hyper-parameters. A second approach would be to embed the proposed state estimator within a larger control loop for regulating arousal or energy [85]- [87]. An objective criterion could be set to maintain a patient's arousal levels or energy within a pre-defined range.…”

Section: E Model Parameter Selectionmentioning

confidence: 99%

A Bayesian Filtering Approach for Tracking Sympathetic Arousal and Cortisol-Related Energy From Marked Point Process and Continuous-Valued Observations

Wickramasuriya,

Khazaei,

Kiani

et al. 2023

IEEE Access

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Multiple state variables governed by internal processes within the human body remain unobserved. On a number of occasions, these states are linked to point process bioelectric and biochemical observations coupled together with continuous-valued variables. These observations provide a means to estimate the latent states of interest. We develop a state-space method to estimate unobserved sympathetic arousal and energy production states from skin conductance and cortisol data respectively, comprising of a marked point process and a continuous-valued observation. The method involves Bayesian filtering applied within an expectation-maximization (EM) framework for state estimation and model parameter recovery. Results are evaluated on both simulated and experimental data. On experimental skin conductance data, high arousal levels are generally detected during cognitive stress periods and lower values are detected during relaxation. Results are also in conformity with general physiology for cortisol data. On separate experimental data, skin conductance-based estimates are validated/cross-checked with functional Near Infrared Spectroscopy features. Estimation is also performed on heart rate and skin conductance data to illustrate the method's wider applicability. We also compare the method with earlier approaches. We show how it outperforms a previous method for cortisol-based energy estimation, and its superiority to earlier methods for estimating sympathetic arousal. The EM approach is thus able to estimate latent physiological states within the body from point process bioelectric and biochemical phenomena. The method could be applied in wearable monitoring and automated closed-loop therapy delivery for patients diagnosed with certain types of neuropsychiatric or hormone disorders.INDEX TERMS Biomedical signal processing, state-space methods, estimation, emotion recognition, Kalman filters I. INTRODUCTIONS EVERAL internal state variables maintained by physiological processes within the human body remain largely unobserved. Nevertheless, they are often tied to more easilymeasured changes in biolelectric and biochemical phenomena that can be used for their estimation. For instance, energy

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Enhancement of Closed-Loop Cognitive Stress Regulation Using Supervised Control Architectures

Cited by 8 publications

References 61 publications

Skin Conductance Response Artifact Reduction: Leveraging Accelerometer Noise Reference and Deep Breath Detection

Skin Conductance Response Artifact Reduction: Leveraging Accelerometer Noise Reference and Deep Breath Detection

Regulation of brain cognitive states through auditory, gustatory, and olfactory stimulation with wearable monitoring

A Bayesian Filtering Approach for Tracking Sympathetic Arousal and Cortisol-Related Energy From Marked Point Process and Continuous-Valued Observations

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