Effects of Daily Stress in Mental State Classification

Park, Soyeon; Dong, Suh-Yeon

doi:10.1109/access.2020.3035799

Cited by 9 publications

(4 citation statements)

References 50 publications

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“…Especially, the feature extraction stages have a significant impact on the classification performances such as accuracy. Thus, we adopted the framework according to Park et al 37 to investigate the possibility of detecting impulse buying behavior and non-impulse buying behavior. This framework extracted five features: signal mean, signal variance, signal slope, signal kurtosis, and signal skewness.…”

Section: Methodsmentioning

confidence: 99%

Brain–computer interface to predict impulse buying behavior using functional near-infrared spectroscopy

Bak

Jeong

Yeu

et al. 2022

Sci Rep

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As the rate of vaccination against COVID-19 is increasing, demand for overseas travel is also increasing. Despite people’s preference for duty-free shopping, previous studies reported that duty-free shopping increases impulse buying behavior. There are also self-reported tools to measure their impulse buying behavior, but it has the disadvantage of relying on the human memory and perception. Therefore, we propose a Brain–Computer Interface (BCI)-based brain signal processing methodology to supplement these limitations and to reduce ambiguity and conjecture of data. To achieve this goal, we focused on the brain’s prefrontal cortex (PFC) activity, which supervises human decision-making and is closely related to impulse buying behavior. The PFC activation is observed by recording signals using a functional near-infrared spectroscopy (fNIRS) while inducing impulse buying behavior in virtual computing environments. We found that impulse buying behaviors were not only higher in online duty-free shops than in online regular stores, but the fNIRS signals were also different on the two sites. We also achieved an average accuracy of 93.78% in detecting impulse buying patterns using a support vector machine. These results were identical to the people's self-reported responses. This study provides evidence as a potential biomarker for detecting impulse buying behavior with fNIRS.

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Section: Methodsmentioning

confidence: 99%

Brain–computer interface to predict impulse buying behavior using functional near-infrared spectroscopy

Bak

Jeong

Yeu

et al. 2022

Sci Rep

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“…Accordingly, we adopted the framework for feature extraction used by Park and Dong. [ 37 ] and then calculated 6 time-domain features (mean, variance, kurtosis, skewness, slop, and area) to extract information across data [ 38 ]. We denote mean, variance, kurtosis, skewness, slope, and area as SM, SV, KR, SK, SS, and SA, respectively.…”

Section: Methodsmentioning

confidence: 99%

Forecasting Unplanned Purchase Behavior under Buy-One Get-One-Free Promotions Using Functional Near-Infrared Spectroscopy

Bak

Yeu

Jeong

2022

Computational Intelligence and Neuroscience

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It is very important for consumers to recognize their wrong shopping habits such as unplanned purchase behavior (UPB). The traditional methods used for measuring the UPB in qualitative and quantitative studies have some drawbacks because of human perception and memory. We proposed a UPB identification methodology applied with the brain-computer interface technique using a support vector machine (SVM) along with a functional near-infrared spectroscopy (fNIRS). Hemodynamic signals and behavioral data were collected from 33 subjects by performing Task 1 which included the Buy-One-Get-One-Free (BOGOF) and Task 2 which excluded the BOGOF condition. The acquired data were calculated with 6 time-domain features and then classified them using SVM with 10-cross validations. Thereafter, we evaluated whether the results were reliable using the area under the receiver operating characteristic curve (AUC). As a result, we achieved average accuracy greater than 94%, which is reliable because of the AUC values above 0.97. We found that the UPB brain activity was more relevant to Task 1 with the BOGOF condition than with Task 2 in the prefrontal cortex. UPBs were sufficiently derived from self-reported measurement, indicating that the subjects perceived increased impulsivity in the BOGOF condition. Therefore, this study improves the detection and understanding of UPB as a path for a computer-aided detection perspective for rating the severity of UPBs.

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“…Thus, multivariate machine learning techniques, which show great promise in disease modeling and therapeutic discovery in psychiatry, have been used as a complement to traditional methods [24]. In previous studies [25][26][27], support vector machine (SVM), linear discriminant analysis (LDA), and decision trees have been applied on fNIRS data to identify specific mental and cognitive tasks for diagnosis or treatment. In the majority of these studies, the statistical characteristics of active channel signals corresponding to the brain region in each trial, such as average, peak, slope, and kurtosis values, can be used as classification features.…”

Section: Introductionmentioning

confidence: 99%

“…In the majority of these studies, the statistical characteristics of active channel signals corresponding to the brain region in each trial, such as average, peak, slope, and kurtosis values, can be used as classification features. For instance, Woo et al [25] used LDA to distinguish the difference in fNIRS signals under different stress states with a classification result reaching 76.67%, whereas Park et al [26] used SVM to deal with the same problem and achieved 87% classification accuracy. These results are promising, but exploration by machine learning still has some limitations.…”

Section: Introductionmentioning

confidence: 99%

Joint-Channel-Connectivity-Based Feature Selection and Classification on fNIRS for Stress Detection in Decision-Making

Huang

Mai

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Stress is one of the contributing factors affecting decision-making. Therefore, early stress recognition is essential to improve clinicians' decision-making performance. Functional near-infrared spectroscopy (fNIRS) has shown great potential in detecting stress. However, the majority of previous studies only used fNIRS features at the individual level for classification without considering the correlations among channels corresponding to the brain, which may provide distinguishing features. Hence, this study proposes a novel joint-channelconnectivity-based feature selection and classification algorithm for fNIRS to detect stress in decision-making. Specifically, this approach integrates feature selection and classifier modeling into a sparse model, where intra-and inter-channel regularizers are designed to explore potential correlations among channels to obtain discriminating features. In this paper, we simulated the decision-making of medical students under stress through the Trier Social Stress Test and the Balloon Analog Risk Task and recorded their cerebral hemodynamic alterations by fNIRS device.Experimental results illustrated that our method with the accuracy of 0.961 is superior to other machine learning methods. Additionally, the stress correlation and connectivity of brain regions calculated by feature selection have been confirmed in previous studies, which validates the effectiveness of our method and helps optimize the channel settings of fNIRS. This work was the first attempt to utilize a sparse model that simultaneously considers the sparsity of features and the correlation of brain regions for stress detection and obtained an admirable classification performance. Thus, the proposed model might be a useful tool for medical personnel to automatically detect stress in clinical decision-making situations.

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Effects of Daily Stress in Mental State Classification

Cited by 9 publications

References 50 publications

Brain–computer interface to predict impulse buying behavior using functional near-infrared spectroscopy

Brain–computer interface to predict impulse buying behavior using functional near-infrared spectroscopy

Forecasting Unplanned Purchase Behavior under Buy-One Get-One-Free Promotions Using Functional Near-Infrared Spectroscopy

Joint-Channel-Connectivity-Based Feature Selection and Classification on fNIRS for Stress Detection in Decision-Making

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