Investigation of human trust by identifying stimulated brain regions using electroencephalogram

Oh, Seeung; Seong, Younho; Yi, Sun; Park, Sangsung

doi:10.1016/j.icte.2022.01.002

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…It verifies that empathy strategy could actually affect patients’ emotions from a neuroscience perspective. Similarly, the result shows a high positive correlation between frontal alpha and trust, which is consistent with Oh et al’s (2022) results. Frontal alpha power correlates with strategy NPIm more than other strategies.…”

Section: Discussionsupporting

confidence: 89%

“…In terms of trust, Wu et al (2021) revealed that in repeated interactions, decisions to trust strangers (i.e., identification-based trust) activated the ventral striatum ( Wu et al, 2021 ). Oh et al (2022) state that frontal lobes in the alpha wave have active connectivity in trust. Through EEG measurements, we could verify the function of strategies from a neuroscience perspective.…”

Section: Introductionmentioning

confidence: 99%

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Neuroscience on breaking bad news: Effects of physicians’ response on patient emotion and trust

Song

Xiu²,

Li³

et al. 2022

Front. Psychol.

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BackgroundThe outbreak of COVID-19, due to restrictions on patients’ access to hospitals, makes patient mental health a severe problem to solve, especially for cancer patients. Delivering bad news has become one of the abilities that physicians need to improve. Former research has proposed communication strategies like SPIKES to respond to patients’ emotions. However, existing strategies lack systematic and structural responses to different cues and concerns of patients.ObjectiveThis study aims to investigate whether and how the response styles of information delivery, empathy, and authority affect patient emotions and trust in order to present a structural response system. Furthermore, we explore the correlation between strategies and EEG markers to moderate emotions and trust.MethodsThis research selects different scenarios and strategies in the context of breast cancer and performs two experiments. First, we performed a behavioral experiment with 93 medical students and 15 breast cancer patients. Moreover, an EEG experiment with 53 students via video stimuli was conducted to explore the moderate function between strategies and emotions/trust. We use time-frequency analysis and the repeated measure ANOVA method to explore the association between strategy and EEG components. Furthermore, we perform a GLM method to investigate the relationship between EEG components and patient emotion and trust.ResultsFor the first time, this study proposes the strategy matrix. The response strategies NPIm and NRIa play important roles in this system. In behavioral experiments, information delivery, empathy, and authority strategy significantly affect emotions and trust. The scenario is significant as a moderator. In the EEG experiment, strategy NPIm has more correlation with parietal alpha power than other strategies, and parietal alpha power has a significant effect on emotions, which verifies that empathy-related cerebral activities affect emotions and trust.ConclusionAccording to the strategy matrix, physicians could apply strategy ERIa in most scenarios, and strategy NRIa in many scenarios, which means information provision is significant when it comes to responding to patients’ cues and concerns. The most important strategy that physicians need to avoid is the authority strategy. Refusing to respond to patients’ cues and concerns may cause their dislike. Moreover, through the EEG experiment, we verify that empathy affects emotions and trust from a neuroscience perspective and propose parietal alpha and frontal alpha as neuro-markers to moderate emotions and trust. Physicians could adjust strategies through these EEG markers.

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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Neuroscience on breaking bad news: Effects of physicians’ response on patient emotion and trust

Song

Xiu²,

Li³

et al. 2022

Front. Psychol.

View full text Add to dashboard Cite

show abstract

“…It records the electrical signals produced by neurons in the brain using an electroencephalograph and provides valuable insights into brain activity. The resulting EEG signal contains information on the frequency and amplitude of the brain activity measured at different locations on the scalp [2], as depicted in Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

Modeling EEG Signals for Mental Confusion Using DNN and LSTM With Custom Attention Layer

Ganiga,

Kim,

Tulluri

et al. 2023

IEEE Access

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This study explored the impact of confusion on concentration and cognition, emphasizing the importance of detecting and preventing confusion from enhancing learning outcomes. By leveraging electroencephalogram (EEG) data, we proposed a novel deep learning model that uses long short-term memory (LSTM) networks to predict confusion levels in online massive open courses (MOOCs). LSTM's ability to model sequential data such as EEG signals has been harnessed to capture long-term dependencies and temporal dynamics effectively. To enhance pattern detection, we incorporated probabilistic features from machine learning (ML) models. By training them on the same dataset, we utilized their predictions as additional features for the deep learning model. Thereby, the neural network could make more informed decisions and improve its ability to detect and analyze EEG data patterns. Using LSTM and probabilistic features, our model effectively captured temporal dependencies, enabling an accurate online assessment of student perplexity to identify moments of confusion. The integration of attention mechanisms further enhanced the focus on critical EEG features, providing valuable insights into students' cognitive states during online learning. We evaluated our approach by comparing the deep-learning model trained on the original dataset with that trained on feature-engineered data using K-fold cross-validation. Preliminary testing showed that the proposed DNN + LSTM model, which incorporates probabilistic features and a custom attention layer, achieves high accuracy in identifying moments of confusion among MOOC students. This study advances the EEG data analysis, leading to a better understanding of confusion patterns and supports personalized interventions for online education platforms.

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A Human-Machine Trust Evaluation Method for High-Speed Train Drivers Based on Multi-Modal Physiological Information

Li,

Liang,

Niu

et al. 2024

International Journal of Human–Computer Interaction

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Investigation of human trust by identifying stimulated brain regions using electroencephalogram

Cited by 7 publications

References 28 publications

Neuroscience on breaking bad news: Effects of physicians’ response on patient emotion and trust

Neuroscience on breaking bad news: Effects of physicians’ response on patient emotion and trust

Modeling EEG Signals for Mental Confusion Using DNN and LSTM With Custom Attention Layer

A Human-Machine Trust Evaluation Method for High-Speed Train Drivers Based on Multi-Modal Physiological Information

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