@article{kim_choo_park_park_nam_jung_lee_2023, title={Designing an XAI interface for BCI experts: A contextual design for pragmatic explanation interface based on domain knowledge in a specific context}, volume={174}, ISSN={["1095-9300"]}, DOI={10.1016/j.ijhcs.2023.103009}, abstractNote={Domain experts utilize a decision-support system depending on an artificial intelligence (AI) algorithm. Likewise, researchers in brain-computer interface (BCI) have recently employed deep learning (DL) algorithms for decoding and analyzing neural signals. Despite its outstanding performance, the BCI technology with the DLs has pointed out that it has a potential problem of low transparency due to algorithmic complexity of the models. On this problem, explainable artificial intelligence (XAI) can be a solution to make an AI algorithm and its decisions more interpretable. However, the explanation from the XAI has been emphasized that it should be designed corresponding with the user's different expectations which are contextually variable. Thus, our study aims to propose an explanation interface for the BCI expert under Pragmatism structuralizing an explanation with scientific knowledge in a contrastive manner. For this work, we conduct a contextual design process with five BCI experts, specifically conducting a contextual inquiry and work modeling to extract design requirements from their expertise in their work environment; next, designing and evaluating an interactive prototype of the explanation interface. The results indicated that our prototype has the advantages of increasing contextual understanding and intuitive interface design. Yet, there were also challenges on the explanation for novice users without prior knowledge on the XAI and objective understanding of the AI model with enough interpretability. This study contributes to providing a theoretical framework based on Pragmatism and designing a user-centered XAI system for domain experts in a specific context.}, journal={INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES}, author={Kim, Sangyeon and Choo, Sanghyun and Park, Donghyun and Park, Hoonseok and Nam, Chang S. and Jung, Jae-Yoon and Lee, Sangwon}, year={2023}, month={Jun} }
 @article{choo_park_kim_park_jung_lee_nam_2023, title={Effectiveness of multi-task deep learning framework for EEG-based emotion and context recognition}, volume={227}, ISSN={["1873-6793"]}, DOI={10.1016/j.eswa.2023.120348}, abstractNote={Studies have investigated electroencephalogram (EEG)-based emotion recognition using hand-crafted EEG features (e.g., differential entropy) or the annotated emotion categories without any additional emotion factors (e.g., context). The effectiveness of raw EEG-based emotion recognition remains for further investigation. In this study, we investigated the effectiveness of multi-task learning (MTL) for raw EEG-based convolutional neural networks (CNNs) in emotion recognition with auxiliary context information. Thirty subjects participated in this study, where their brain signals were collected when watching six types of emotion images (social/nonsocial-fear, social/nonsocial-sad, and social/nonsocial-neutral). For the MTL architecture, we utilized temporal and spatial filtering layers from raw EEG-based CNNs as shared and task-specific layers for emotion and context classification tasks. Subject-dependent classifications and five repeated five-fold cross-validation were performed to test the classification accuracy for all comparison models. Our results showed that (1) the MTL classifier had a significantly higher classification accuracy and improved the performance of the single-task learnings (STLs) for both emotion and context, and (2) the ShallowConvNet was the best network architecture among the considered CNNs for the MTL with statistically significant improvement to the raw EEG-based STLs. This shows that the MTL can be a promising method for emotion recognition in utilizing the raw EEG-based CNN classifiers and emphasizes the importance of considering context information.}, journal={EXPERT SYSTEMS WITH APPLICATIONS}, author={Choo, Sanghyun and Park, Hoonseok and Kim, Sangyeon and Park, Donghyun and Jung, Jae-Yoon and Lee, Sangwon and Nam, Chang S.}, year={2023}, month={Oct} }
 @article{choo_nam_2022, title={Detecting Human Trust Calibration in Automation: A Convolutional Neural Network Approach}, volume={1}, ISSN={["2168-2305"]}, url={https://doi.org/10.1109/THMS.2021.3137015}, DOI={10.1109/THMS.2021.3137015}, abstractNote={There is a general lack of studies that are aimed at monitoring and detecting an operator's trust calibration, even though detecting someone's adjusted trust towards automation is essential to prevent misuse and disuse of automation. The goal of this article is to propose a convolutional neural network (CNN) based framework to estimate operators’ trust levels and detect their trust calibration in automation using image features of electroencephalogram (EEG) signals preserving temporal, spectral, and spatial information. Thirteen participants performed a set of automated Air Force multiattribute task battery tasks that differed in reliability (High/Low) and credibility (High/Low) levels. The proposed framework was compared with three machine learning methods—naïve bayes, support vector machine, multilayer perceptron—in terms of accuracy, sensitivity, and specificity of trust estimation and detection of trust calibration. Results of this article showed that the proposed framework had the highest performance of both trust estimation and detection of trust calibration in automation compared to the other comparison methods. This indicates that the proposed framework using the CNN classifier with the image-based EEG features could be an applicable model for estimating multilevel trust and detecting trust calibration during human-automation interaction. Also, it can help to prevent disuse and misuse of automation by estimating operators’ trust levels and monitoring their trust calibration in automation.}, number={4}, journal={IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Choo, Sanghyun and Nam, Chang}, year={2022}, month={Jan} }
 @article{pugh_choo_leshin_lindquist_nam_2022, title={Emotion depends on context, culture and their interaction: evidence from effective connectivity}, volume={17}, ISSN={["1749-5024"]}, DOI={10.1093/scan/nsab092}, abstractNote={Abstract Situated models of emotion hypothesize that emotions are optimized for the context at hand, but most neuroimaging approaches ignore context. For the first time, we applied Granger causality (GC) analysis to determine how an emotion is affected by a person’s cultural background and situation. Electroencephalographic recordings were obtained from mainland Chinese (CHN) and US participants as they viewed and rated fearful and neutral images displaying either social or non-social contexts. Independent component analysis and GC analysis were applied to determine the epoch of peak effect for each condition and to identify sources and sinks among brain regions of interest. We found that source–sink couplings differed across culture, situation and culture × situation. Mainland CHN participants alone showed preference for an early-onset source–sink pairing with the supramarginal gyrus as a causal source, suggesting that, relative to US participants, CHN participants more strongly prioritized a scene’s social aspects in their response to fearful scenes. Our findings suggest that the neural representation of fear indeed varies according to both culture and situation and their interaction in ways that are consistent with norms instilled by cultural background.}, number={2}, journal={SOCIAL COGNITIVE AND AFFECTIVE NEUROSCIENCE}, author={Pugh, Zachary H. and Choo, Sanghyun and Leshin, Joseph C. and Lindquist, Kristen A. and Nam, Chang S.}, year={2022}, month={Feb}, pages={206–217} }
 @article{huang_choo_pugh_nam_2022, title={Evaluating Effective Connectivity of Trust in Human-Automation Interaction: A Dynamic Causal Modeling (DCM) Study}, volume={64}, ISSN={["1547-8181"]}, url={https://doi.org/10.1177/0018720820987443}, DOI={10.1177/0018720820987443}, abstractNote={Objective Using dynamic causal modeling (DCM), we examined how credibility and reliability affected the way brain regions exert causal influence over each other—effective connectivity (EC)—in the context of trust in automation. Background Multiple brain regions of the central executive network (CEN) and default mode network (DMN) have been implicated in trust judgment. However, the neural correlates of trust judgment are still relatively unexplored in terms of the directed information flow between brain regions. Method Sixteen participants observed the performance of four computer algorithms, which differed in credibility and reliability, of the system monitoring subtask of the Air Force Multi-Attribute Task Battery (AF-MATB). Using six brain regions of the CEN and DMN commonly identified to be activated in human trust, a total of 30 (forward, backward, and lateral) connection models were developed. Bayesian model averaging (BMA) was used to quantify the connectivity strength among the brain regions. Results Relative to the high trust condition, low trust showed unique presence of specific connections, greater connectivity strengths from the prefrontal cortex, and greater network complexity. High trust condition showed no backward connections. Conclusion Results indicated that trust and distrust can be two distinctive neural processes in human–automation interaction—distrust being a more complex network than trust, possibly due to the increased cognitive load. Application The causal architecture of distributed brain regions inferred using DCM can help not only in the design of a balanced human–automation interface design but also in the proper use of automation in real-life situations.}, number={6}, journal={HUMAN FACTORS}, publisher={SAGE Publications}, author={Huang, Jiali and Choo, Sanghyun and Pugh, Zachary H. and Nam, Chang S.}, year={2022}, month={Sep}, pages={1051–1069} }
 @misc{nam_choo_huang_park_2020, title={Brain-to-Brain Neural Synchrony During Social Interactions: A Systematic Review on Hyperscanning Studies}, volume={10}, ISSN={["2076-3417"]}, url={https://doi.org/10.3390/app10196669}, DOI={10.3390/app10196669}, abstractNote={The aim of this study was to conduct a comprehensive review on hyperscanning research (measuring brain activity simultaneously from more than two people interacting) using an explicit systematic method, the preferred reporting items for systematic reviews and meta-analyses (PRISMA). Data were searched from IEEE Xplore, PubMed, Engineering Village, Web of Science and Scopus databases. Inclusion criteria were journal articles written in English from 2000 to 19 June 2019. A total of 126 empirical studies were screened out to address three specific questions regarding the neuroimaging method, the application domain, and the experiment paradigm. Results showed that the most used neuroimaging method with hyperscanning was magnetoencephalography/electroencephalography (MEG/EEG; 47%), and the least used neuroimaging method was hyper-transcranial Alternating Current Stimulation (tACS) (1%). Applications in cognition accounted for almost half the studies (48%), while educational applications accounted for less than 5% of the studies. Applications in decision-making tasks were the second most common (26%), shortly followed by applications in motor synchronization (23%). The findings from this systematic review that were based on documented, transparent and reproducible searches should help build cumulative knowledge and guide future research regarding inter-brain neural synchrony during social interactions, that is, hyperscanning research.}, number={19}, journal={APPLIED SCIENCES-BASEL}, publisher={MDPI AG}, author={Nam, Chang S. and Choo, Sanghyun and Huang, Jiali and Park, Jiyoung}, year={2020}, month={Oct} }
 @article{kim_jin_choo_nam_yun_2019, title={Designing of smart chair for monitoring of sitting posture using convolutional neural networks}, volume={53}, ISSN={["2514-9318"]}, DOI={10.1108/DTA-03-2018-0021}, abstractNote={
Purpose
Sitting in a chair is a typical act of modern people. Prolonged sitting and sitting with improper postures can lead to musculoskeletal disorders. Thus, there is a need for a sitting posture classification monitoring system that can predict a sitting posture. The purpose of this paper is to develop a system for classifying children’s sitting postures for the formation of correct postural habits.


Design/methodology/approach
For the data analysis, a pressure sensor of film type was installed on the seat of the chair, and image data of the postu.re were collected. A total of 26 children participated in the experiment and collected image data for a total of seven postures. The authors used convolutional neural networks (CNN) algorithm consisting of seven layers. In addition, to compare the accuracy of classification, artificial neural networks (ANN) technique, one of the machine learning techniques, was used.


Findings
The CNN algorithm was used for the sitting position classification and the average accuracy obtained by tenfold cross validation was 97.5 percent. The authors confirmed that classification accuracy through CNN algorithm is superior to conventional machine learning algorithms such as ANN and DNN. Through this study, we confirmed the applicability of the CNN-based algorithm that can be applied to the smart chair to support the correct posture in children.


Originality/value
This study successfully performed the posture classification of children using CNN technique, which has not been used in related studies. In addition, by focusing on children, we have expanded the scope of the related research area and expected to contribute to the early postural habits of children.
}, number={2}, journal={DATA TECHNOLOGIES AND APPLICATIONS}, author={Kim, Wonjoon and Jin, Byungki and Choo, Sanghyun and Nam, Chang S. and Yun, Myung Hwan}, year={2019}, month={Apr}, pages={142–155} }
 @article{choo_lee_2018, title={Learning Framework of Multimodal Gaussian-Bernoulli RBM Handling Real-value Input Data}, volume={275}, url={https://doi.org/10.1016/j.neucom.2017.10.018}, DOI={10.1016/j.neucom.2017.10.018}, abstractNote={The conventional Gaussian–Bernoulli restricted Boltzmann machine (GBRBM), which is a RBM model for processing real-valued data, presumes single Gaussian distribution for learning real numbers. However, a single distribution is not able to effectively reflect complex data in many cases of real applications. In order to overcome this limitation, Gaussian mixture model (GMM) based RBM is proposed. As a learning mechanism for the proposed model, an energy function handling multi-modal distribution is provided. Then, a memetic algorithm (MA) was applied in order to train the proposed framework more accurately in real-valued input data. In order to show the effectiveness of the proposed framework, the method is applied to image reconstructions. The experiments show that the proposed framework provides more valid results than the other RBM based models in reconstruction error. Through the experiment results, it is concluded that the proposed framework is able to apply real-valued input data extensively and reduce difficulties of learning parameters by capturing the characteristics of real-value input data using GMM.}, number={1}, journal={Neurocomputing}, author={Choo, Sanghyun and Lee, Hyunsoo}, year={2018}, month={Jan}, pages={1813–1822} }