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Park H, Jun SC. Connectivity study on resting-state EEG between motor imagery BCI-literate and BCI-illiterate groups. J Neural Eng 2024; 21:046042. [PMID: 38986469 DOI: 10.1088/1741-2552/ad6187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 07/10/2024] [Indexed: 07/12/2024]
Abstract
Objective.Although motor imagery-based brain-computer interface (MI-BCI) holds significant potential, its practical application faces challenges such as BCI-illiteracy. To mitigate this issue, researchers have attempted to predict BCI-illiteracy by using the resting state, as this was found to be associated with BCI performance. As connectivity's significance in neuroscience has grown, BCI researchers have applied connectivity to it. However, the issues of connectivity have not been considered fully. First, although various connectivity metrics exist, only some have been used to predict BCI-illiteracy. This is problematic because each metric has a distinct hypothesis and perspective to estimate connectivity, resulting in different outcomes according to the metric. Second, the frequency range affects the connectivity estimation. In addition, it is still unknown whether each metric has its own optimal frequency range. Third, the way that estimating connectivity may vary depending upon the dataset has not been investigated. Meanwhile, we still do not know a great deal about how the resting state electroencephalography (EEG) network differs between BCI-literacy and -illiteracy.Approach.To address the issues above, we analyzed three large public EEG datasets using three functional connectivity and three effective connectivity metrics by employing diverse graph theory measures. Our analysis revealed that the appropriate frequency range to predict BCI-illiteracy varies depending upon the metric. The alpha range was found to be suitable for the metrics of the frequency domain, while alpha + theta were found to be appropriate for multivariate Granger causality. The difference in network efficiency between BCI-literate and -illiterate groups was constant regardless of the metrics and datasets used. Although we observed that BCI-literacy had stronger connectivity, no other significant constructional differences were found.Significance.Based upon our findings, we predicted MI-BCI performance for the entire dataset. We discovered that combining several graph features could improve the prediction's accuracy.
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Affiliation(s)
- Hanjin Park
- AI Graduate School, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Sung Chan Jun
- AI Graduate School, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
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2
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Chen Z, Yang R, Huang M, Li F, Lu G, Wang Z. EEGProgress: A fast and lightweight progressive convolution architecture for EEG classification. Comput Biol Med 2024; 169:107901. [PMID: 38159400 DOI: 10.1016/j.compbiomed.2023.107901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 12/11/2023] [Accepted: 12/23/2023] [Indexed: 01/03/2024]
Abstract
Because of the intricate topological structure and connection of the human brain, extracting deep spatial features from electroencephalograph (EEG) signals is a challenging and time-consuming task. The extraction of topological spatial information plays a crucial role in EEG classification, and the architecture of the spatial convolution greatly affects the performance and complexity of convolutional neural network (CNN) based EEG classification models. In this study, a progressive convolution CNN architecture named EEGProgress is proposed, aiming to efficiently extract the topological spatial information of EEG signals from multi-scale levels (electrode, brain region, hemisphere, global) with superior speed. To achieve this, the raw EEG data is permuted using the empirical topological permutation rule, integrating the EEG data with numerous topological properties. Subsequently, the spatial features are extracted by a progressive feature extractor including prior, electrode, region, and hemisphere convolution blocks, progressively extracting the deep spatial features with reduced parameters and speed. Finally, the comparison and ablation experiments under both cross-subject and within-subject scenarios are conducted on a public dataset to verify the performance of the proposed EEGProgress and the effectiveness of the topological permutation. The results demonstrate the superior feature extraction ability of the proposed EEGProgress, with an average increase of 4.02% compared to other CNN-based EEG classification models under both cross-subject and within-subject scenarios. Furthermore, with the obtained average testing time, FLOPs, and parameters, the proposed EEGProgress outperforms other comparison models in terms of model complexity.
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Affiliation(s)
- Zhige Chen
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China; School of Electrical Engineering, Electronics and Computer Science, University of Liverpool, Liverpool L69 3BX, United Kingdom
| | - Rui Yang
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China
| | - Mengjie Huang
- Design School, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China.
| | - Fumin Li
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China; School of Electrical Engineering, Electronics and Computer Science, University of Liverpool, Liverpool L69 3BX, United Kingdom
| | - Guoping Lu
- School of Electrical Engineering, Nantong University, Nantong 226019, China
| | - Zidong Wang
- Department of Computer Science, Brunel University London, Uxbridge, Middlesex UB8 3PH, United Kingdom
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Rakhmatulin I, Dao MS, Nassibi A, Mandic D. Exploring Convolutional Neural Network Architectures for EEG Feature Extraction. SENSORS (BASEL, SWITZERLAND) 2024; 24:877. [PMID: 38339594 PMCID: PMC10856895 DOI: 10.3390/s24030877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 01/12/2024] [Accepted: 01/20/2024] [Indexed: 02/12/2024]
Abstract
The main purpose of this paper is to provide information on how to create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was to understand the primary aspects of creating and fine-tuning CNNs for various application scenarios. We considered the characteristics of EEG signals, coupled with an exploration of various signal processing and data preparation techniques. These techniques include noise reduction, filtering, encoding, decoding, and dimension reduction, among others. In addition, we conduct an in-depth analysis of well-known CNN architectures, categorizing them into four distinct groups: standard implementation, recurrent convolutional, decoder architecture, and combined architecture. This paper further offers a comprehensive evaluation of these architectures, covering accuracy metrics, hyperparameters, and an appendix that contains a table outlining the parameters of commonly used CNN architectures for feature extraction from EEG signals.
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Affiliation(s)
- Ildar Rakhmatulin
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (A.N.)
| | - Minh-Son Dao
- National Institute of Information and Communications Technology (NICT), Tokyo 184-0015, Japan
| | - Amir Nassibi
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (A.N.)
| | - Danilo Mandic
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (A.N.)
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Barnova K, Mikolasova M, Kahankova RV, Jaros R, Kawala-Sterniuk A, Snasel V, Mirjalili S, Pelc M, Martinek R. Implementation of artificial intelligence and machine learning-based methods in brain-computer interaction. Comput Biol Med 2023; 163:107135. [PMID: 37329623 DOI: 10.1016/j.compbiomed.2023.107135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 05/13/2023] [Accepted: 06/04/2023] [Indexed: 06/19/2023]
Abstract
Brain-computer interfaces are used for direct two-way communication between the human brain and the computer. Brain signals contain valuable information about the mental state and brain activity of the examined subject. However, due to their non-stationarity and susceptibility to various types of interference, their processing, analysis and interpretation are challenging. For these reasons, the research in the field of brain-computer interfaces is focused on the implementation of artificial intelligence, especially in five main areas: calibration, noise suppression, communication, mental condition estimation, and motor imagery. The use of algorithms based on artificial intelligence and machine learning has proven to be very promising in these application domains, especially due to their ability to predict and learn from previous experience. Therefore, their implementation within medical technologies can contribute to more accurate information about the mental state of subjects, alleviate the consequences of serious diseases or improve the quality of life of disabled patients.
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Affiliation(s)
- Katerina Barnova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Martina Mikolasova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Radana Vilimkova Kahankova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia
| | - Rene Jaros
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Poland.
| | - Vaclav Snasel
- Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Australia.
| | - Mariusz Pelc
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Poland; School of Computing and Mathematical Sciences, University of Greenwich, London, UK.
| | - Radek Martinek
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia; Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Poland.
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Mwata-Velu T, Niyonsaba-Sebigunda E, Avina-Cervantes JG, Ruiz-Pinales J, Velu-A-Gulenga N, Alonso-Ramírez AA. Motor Imagery Multi-Tasks Classification for BCIs Using the NVIDIA Jetson TX2 Board and the EEGNet Network. SENSORS (BASEL, SWITZERLAND) 2023; 23:4164. [PMID: 37112504 PMCID: PMC10145994 DOI: 10.3390/s23084164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 04/15/2023] [Accepted: 04/17/2023] [Indexed: 06/19/2023]
Abstract
Nowadays, Brain-Computer Interfaces (BCIs) still captivate large interest because of multiple advantages offered in numerous domains, explicitly assisting people with motor disabilities in communicating with the surrounding environment. However, challenges of portability, instantaneous processing time, and accurate data processing remain for numerous BCI system setups. This work implements an embedded multi-tasks classifier based on motor imagery using the EEGNet network integrated into the NVIDIA Jetson TX2 card. Therefore, two strategies are developed to select the most discriminant channels. The former uses the accuracy based-classifier criterion, while the latter evaluates electrode mutual information to form discriminant channel subsets. Next, the EEGNet network is implemented to classify discriminant channel signals. Additionally, a cyclic learning algorithm is implemented at the software level to accelerate the model learning convergence and fully profit from the NJT2 hardware resources. Finally, motor imagery Electroencephalogram (EEG) signals provided by HaLT's public benchmark were used, in addition to the k-fold cross-validation method. Average accuracies of 83.7% and 81.3% were achieved by classifying EEG signals per subject and motor imagery task, respectively. Each task was processed with an average latency of 48.7 ms. This framework offers an alternative for online EEG-BCI systems' requirements, dealing with short processing times and reliable classification accuracy.
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Affiliation(s)
- Tat’y Mwata-Velu
- Centro de Investigación en Computación, Instituto Politécnico Nacional (CIC-IPN), Avenida Juan de Dios Bátiz Esquina Miguel Othón de Mendizábal Colonia Nueva Industrial Vallejo, Alcaldía Gustavo A. Madero, Ciudad de Mexico C.P. 07738, Mexico
- Institut Supérieur Pédagogique Technique de Kinshasa (I.S.P.T.-KIN), Av. de la Science 5, Gombe, Kinshasa 3287, Democratic Republic of the Congo; (E.N.-S.); (J.R.-P.)
- Telematics and Digital Signal Processing Research Groups (CAs), Department of Electronics Engineering, Universidad de Guanajuato, Salamanca 36885, Mexico;
| | - Edson Niyonsaba-Sebigunda
- Institut Supérieur Pédagogique Technique de Kinshasa (I.S.P.T.-KIN), Av. de la Science 5, Gombe, Kinshasa 3287, Democratic Republic of the Congo; (E.N.-S.); (J.R.-P.)
| | - Juan Gabriel Avina-Cervantes
- Telematics and Digital Signal Processing Research Groups (CAs), Department of Electronics Engineering, Universidad de Guanajuato, Salamanca 36885, Mexico;
| | - Jose Ruiz-Pinales
- Institut Supérieur Pédagogique Technique de Kinshasa (I.S.P.T.-KIN), Av. de la Science 5, Gombe, Kinshasa 3287, Democratic Republic of the Congo; (E.N.-S.); (J.R.-P.)
| | - Narcisse Velu-A-Gulenga
- Institut Supérieur Pédagogique de Kikwit (I.S.P. KIKWIT), Av Nzundu 2, Com. Lukolela, Kikwit 8211, Democratic Republic of the Congo
| | - Adán Antonio Alonso-Ramírez
- Instituto Tecnológico Nacional de México en Celaya (TecNM-Celaya), Av. Antonio García Cubas Pte 600, Celaya C.P. 38010, Guanajuato, Mexico;
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Ganjalizadeh V, Meena GG, Stott MA, Hawkins AR, Schmidt H. Machine learning at the edge for AI-enabled multiplexed pathogen detection. Sci Rep 2023; 13:4744. [PMID: 36959357 PMCID: PMC10034896 DOI: 10.1038/s41598-023-31694-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 03/15/2023] [Indexed: 03/25/2023] Open
Abstract
Multiplexed detection of biomarkers in real-time is crucial for sensitive and accurate diagnosis at the point of use. This scenario poses tremendous challenges for detection and identification of signals of varying shape and quality at the edge of the signal-to-noise limit. Here, we demonstrate a robust target identification scheme that utilizes a Deep Neural Network (DNN) for multiplex detection of single particles and molecular biomarkers. The model combines fast wavelet particle detection with Short-Time Fourier Transform analysis, followed by DNN identification on an AI-specific edge device (Google Coral Dev board). The approach is validated using multi-spot optical excitation of Klebsiella Pneumoniae bacterial nucleic acids flowing through an optofluidic waveguide chip that produces fluorescence signals of varying amplitude, duration, and quality. Amplification-free 3× multiplexing in real-time is demonstrated with excellent specificity, sensitivity, and a classification accuracy of 99.8%. These results show that a minimalistic DNN design optimized for mobile devices provides a robust framework for accurate pathogen detection using compact, low-cost diagnostic devices.
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Affiliation(s)
- Vahid Ganjalizadeh
- School of Engineering, University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA, 95064, USA
| | - Gopikrishnan G Meena
- School of Engineering, University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA, 95064, USA
| | - Matthew A Stott
- Electrical and Computer Engineering Department, Brigham Young University, Provo, UT, 84602, USA
| | - Aaron R Hawkins
- Electrical and Computer Engineering Department, Brigham Young University, Provo, UT, 84602, USA
| | - Holger Schmidt
- School of Engineering, University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA, 95064, USA.
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Exploiting Asymmetric EEG Signals with EFD in Deep Learning Domain for Robust BCI. Symmetry (Basel) 2022. [DOI: 10.3390/sym14122677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Motor imagery (MI) is a domineering paradigm in brain–computer interface (BCI) composition, personifying the imaginary limb motion into digital commandments for neural rehabilitation and automation exertions, while many researchers fathomed myriad solutions for asymmetric MI EEG signals classification, the existence of a robust, non-complex, and subject-invariant system is far-reaching. Thereupon, we put forward an MI EEG segregation pipeline in the deep-learning domain in an effort to curtail the existing limitations. Our method amalgamates multiscale principal component analysis (MSPCA), a novel empirical Fourier decomposition (EFD) signal resolution method with Hilbert transform (HT), followed by four pre-trained convolutional neural networks for automatic feature estimation and segregation. The conceived architecture is validated upon three binary class datasets: IVa, IVb from BCI Competition III, GigaDB from the GigaScience repository, and one tertiary class dataset V from BCI competition III. The average 10-fold outcomes capitulate 98.63%, 96.33%, and 89.96%, the highest classification accuracy for the aforesaid datasets accordingly using the AlexNet CNN model in a subject-dependent context, while in subject-independent cases, the highest success score was 97.69%, outperforming the contemporary studies by a fair margin. Further experiments such as the resolution scale of EFD, comparison with other signal decomposition (SD) methods, deep feature extraction, and classification with machine learning methods also accredits the supremacy of our proposed EEG signal processing pipeline. The overall findings imply that pre-trained models are reliable in identifying EEG signals due to their capacity to maintain the time-frequency structure of EEG signals, non-complex architecture, and their potential for robust classification performance.
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Wang C, Wu Y, Wang C, Zhu Y, Wang C, Niu Y, Shao Z, Gao X, Zhao Z, Yu Y. MI-EEG classification using Shannon complex wavelet and convolutional neural networks. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Thapa BR, Tangarife DR, Bae J. Kernel Temporal Differences for EEG-based Reinforcement Learning Brain Machine Interfaces. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:3327-3333. [PMID: 36086236 DOI: 10.1109/embc48229.2022.9871862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Kernel temporal differences (KTD) (λ) algorithm integrated in Q-learning (Q-KTD) has shown its applicability and feasibility for reinforcement learning brain machine interfaces (RLBMIs). RLBMI with its unique learning strategy based on trial-error allows continuous learning and adaptation in BMIs. Q-KTD has shown good performance in both open and closed-loop experiments for finding a proper mapping from neural intention to control commands of an external device. However, previous studies have been limited to intracortical BMIs where monkey's firing rates from primary motor cortex were used as inputs to the neural decoder. This study provides the first attempt to investigate Q-KTD algorithm's applicability in EEG-based RLBMIs. Two different publicly available EEG data sets are considered, we refer to them as Data set A and Data set B. EEG motor imagery tasks are integrated in a single step center-out reaching task, and we observe the open-loop RLBMI experiments reach 100% average success rates after sufficient learning experience. Data set A converges after approximately 20 epochs for raw features and Data set B shows convergence after approximately 40 epochs for both raw and Fourier transform features. Although there still exist challenges to overcome in EEG-based RLBMI using Q-KTD, including increasing the learning speed, and optimization of a continuously growing number of kernel units, the results encourage further investigation of Q-KTD in closed-loop RLBMIs using EEG. Clinical Relevance- This study supports feasibility of noninvasive EEG-based RLBMI implementations and addresses benefits and challenges of RLBMI using EEG.
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Abstract
OBJECTIVE Modeling the brain as a white box is vital for investigating the brain. However, the physical properties of the human brain are unclear. Therefore, BCI algorithms using EEG signals are generally a data-driven approach and generate a black- or gray-box model. This paper presents the first EEG-based BCI algorithm (EEG-BCI using Gang neurons, EEGG) decomposing the brain into some simple components with physical meaning and integrating recognition and analysis of brain activity. APPROACH Independent and interactive components of neurons or brain regions can fully describe the brain. This paper constructed a relation frame based on the independent and interactive compositions for intention recognition and analysis using a novel dendrite module of Gang neurons. A total of 4,906 EEG data of left- and right-hand motor imagery(MI) from 26 subjects were obtained from GigaDB. Firstly, this paper explored EEGG's classification performance by cross-subject accuracy. Secondly, this paper transformed the trained EEGG model into a relation spectrum expressing independent and interactive components of brain regions. Then, the relation spectrum was verified using the known ERD/ERS phenomenon. Finally, this paper explored the previously unreachable further BCI-based analysis of the brain. MAIN RESULTS (1) EEGG was more robust than typical "CSP+" algorithms for the low-quality data. (2) The relation spectrum showed the known ERD/ERS phenomenon. (3) Interestingly, EEGG showed that interactive components between brain regions suppressed ERD/ERS effects on classification. This means that generating fine hand intention needs more centralized activation in the brain. SIGNIFICANCE EEGG decomposed the biological EEG-intention system of this paper into the relation spectrum inheriting the Taylor series (in analogy with the data-driven but human-readable Fourier transform and frequency spectrum), which offers a novel frame for analysis of the brain.
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Chacon-Murguia MI, Rivas-Posada E. A CNN-based modular classification scheme for motor imagery using a novel EEG sampling protocol suitable for IoT healthcare systems. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06716-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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12
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Baek S, Yu H, Roh J, Lee J, Sohn I, Kim S, Park C. Effect of a Recliner Chair with Rocking Motions on Sleep Efficiency. SENSORS (BASEL, SWITZERLAND) 2021; 21:8214. [PMID: 34960304 PMCID: PMC8706869 DOI: 10.3390/s21248214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 11/29/2021] [Accepted: 11/30/2021] [Indexed: 11/16/2022]
Abstract
In this study, we analyze the effect of a recliner chair with rocking motions on sleep quality of naps using automated sleep scoring and spindle detection models. The quality of sleep corresponding to the two rocking motions was measured quantitatively and qualitatively. For the quantitative evaluation, we conducted a sleep parameter analysis based on the results of the estimated sleep stages obtained on the brainwave and spindle estimation, and a sleep survey assessment from the participants was analyzed for the qualitative evaluation. The analysis showed that sleep in the recliner chair with rocking motions positively increased the duration of the spindles and deep sleep stage, resulting in improved sleep quality.
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Affiliation(s)
- Suwhan Baek
- Department of Computer engineering, Kwangwoon University, Seoul 01897, Korea
| | - Hyunsoo Yu
- Department of Computer engineering, Kwangwoon University, Seoul 01897, Korea
| | - Jongryun Roh
- Digital Transformation RnD Department, Korea Institute of Industrial Technology, Ansan 15588, Korea
| | - Jungnyun Lee
- Digital Transformation RnD Department, Korea Institute of Industrial Technology, Ansan 15588, Korea
| | - Illsoo Sohn
- Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea
| | - Sayup Kim
- Digital Transformation RnD Department, Korea Institute of Industrial Technology, Ansan 15588, Korea
| | - Cheolsoo Park
- Department of Computer engineering, Kwangwoon University, Seoul 01897, Korea
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