Seung-Bo Lee

447 total citations
34 papers, 274 citations indexed

About

Seung-Bo Lee is a scholar working on Cognitive Neuroscience, Neurology and Cellular and Molecular Neuroscience. According to data from OpenAlex, Seung-Bo Lee has authored 34 papers receiving a total of 274 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Cognitive Neuroscience, 8 papers in Neurology and 7 papers in Cellular and Molecular Neuroscience. Recurrent topics in Seung-Bo Lee's work include EEG and Brain-Computer Interfaces (14 papers), Neuroscience and Neural Engineering (7 papers) and Advanced Memory and Neural Computing (4 papers). Seung-Bo Lee is often cited by papers focused on EEG and Brain-Computer Interfaces (14 papers), Neuroscience and Neural Engineering (7 papers) and Advanced Memory and Neural Computing (4 papers). Seung-Bo Lee collaborates with scholars based in South Korea, United States and United Kingdom. Seung-Bo Lee's co-authors include Dong‐Joo Kim, Hakseung Kim, Ji-Hoon Jeong, Seong‐Whan Lee, Marek Czosnyka, Kyung‐Il Park, Sang Kun Lee, Yunsik Son, Chang-Woo Lee and Frederick A. Zeiler and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Journal of neurosurgery.

In The Last Decade

Seung-Bo Lee

30 papers receiving 268 citations

Peers

Seung-Bo Lee
Anne Marie Guerguerian Canada
Alkinoos Athanasiou Greece
Anna Caterina Merzagora United States
Kevin Novak United States
Hakseung Kim South Korea
Pearce Korb United States
Bert Bonroy Belgium
Çağdaş Güdücü Türkiye
P. A. Karthick India
Anne Marie Guerguerian Canada
Seung-Bo Lee
Citations per year, relative to Seung-Bo Lee Seung-Bo Lee (= 1×) peers Anne Marie Guerguerian

Countries citing papers authored by Seung-Bo Lee

Since Specialization
Citations

This map shows the geographic impact of Seung-Bo Lee's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Seung-Bo Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Seung-Bo Lee more than expected).

Fields of papers citing papers by Seung-Bo Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Seung-Bo Lee. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Seung-Bo Lee. The network helps show where Seung-Bo Lee may publish in the future.

Co-authorship network of co-authors of Seung-Bo Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Seung-Bo Lee. A scholar is included among the top collaborators of Seung-Bo Lee based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Seung-Bo Lee. Seung-Bo Lee is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Oh, Mi‐Young, et al.. (2025). Compact machine learning model for perioperative stroke prediction prior to surgery: A retrospective cohort study. Scientific Reports. 15(1). 40871–40871.
2.
Kang, Minho, Seung-Bo Lee, Gi Jeong Cheon, et al.. (2025). Novel Bone Scan Features for Predicting Prognosis in Men With Bone Metastatic Prostate Cancer: A Retrospective Study. Journal of Korean Medical Science. 40(33). e206–e206.
3.
Lee, Seung-Bo, et al.. (2025). EEG Feature Extraction for Neurodegenerative Disease Diagnosis: A Review. 3(3). 1 indexed citations
4.
Lee, Sang Hyub, Seung-Bo Lee, Kwangsoo Kim, et al.. (2024). Machine learning-based prediction of pulmonary embolism to reduce unnecessary computed tomography scans in gastrointestinal cancer patients: a retrospective multicenter study. Scientific Reports. 14(1). 25359–25359. 2 indexed citations
5.
Sunwoo, Jun‐Sang, et al.. (2024). Increased coherence predicts medical refractoriness in patients with temporal lobe epilepsy on monotherapy. Scientific Reports. 14(1). 20530–20530. 4 indexed citations
6.
7.
Rhee, Tae‐Min, Hyung‐Kwan Kim, Seung-Bo Lee, et al.. (2024). Machine Learning-Based Discrimination of Cardiovascular Outcomes in Patients With Hypertrophic Cardiomyopathy. JACC Asia. 4(5). 375–386. 7 indexed citations
8.
Jung, Young Mi, Hyung‐Chul Lee, Tae Kyong Kim, et al.. (2024). Non-invasive prediction of massive transfusion during surgery using intraoperative hemodynamic monitoring data. Journal of Biomedical Informatics. 156. 104680–104680. 5 indexed citations
9.
Oh, Mi‐Young, Hee‐Soo Kim, Young Mi Jung, et al.. (2024). Machine Learning–Based Explainable Automated Nonlinear Computation Scoring System for Health Score and an Application for Prediction of Perioperative Stroke: Retrospective Study. Journal of Medical Internet Research. 27. e58021–e58021.
10.
Lee, Seung-Bo & Il‐Sung Jang. (2023). Menthol excites dural afferent neurons by inhibiting leak K+ conductance in rats. Neuroscience Letters. 813. 137427–137427. 4 indexed citations
11.
Park, Soyoung, Chang-Woo Lee, Seung-Bo Lee, & Ju‐Yeun Lee. (2023). Machine learning-based prediction model for emergency department visits using prescription information in community-dwelling non-cancer older adults. Scientific Reports. 13(1). 18887–18887. 4 indexed citations
12.
Bae, Ye Seul, et al.. (2023). Predicting Deterioration from Wearable Sensor Data in People with Mild COVID-19. Sensors. 23(23). 9597–9597. 1 indexed citations
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Ha, Eun Jin, Hee‐Soo Kim, Eun‐Young Park, et al.. (2022). Real‐Time Evaluation of Cerebral Autoregulation Based on Near‐Infrared Spectroscopy to Predict Clinical Outcome after Bypass Surgery in Moyamoya Disease. BioMed Research International. 2022(1). 3091660–3091660. 1 indexed citations
16.
Kim, Sungmin, et al.. (2022). Residual one-dimensional convolutional neural network for neuromuscular disorder classification from needle electromyography signals with explainability. Computer Methods and Programs in Biomedicine. 226. 107079–107079. 11 indexed citations
17.
Lee, Seung-Bo, Chang-Woo Lee, Ye Seul Bae, et al.. (2022). Detection of Depression and Suicide Risk Based on Text From Clinical Interviews Using Machine Learning: Possibility of a New Objective Diagnostic Marker. Frontiers in Psychiatry. 13. 801301–801301. 16 indexed citations
18.
Lee, Chang-Woo, et al.. (2022). A machine learning approach for predicting suicidal ideation in post stroke patients. Scientific Reports. 12(1). 15906–15906. 10 indexed citations
19.
Lee, Seung-Bo, et al.. (2022). Predicting Parkinson's disease using gradient boosting decision tree models with electroencephalography signals. Parkinsonism & Related Disorders. 95. 77–85. 42 indexed citations
20.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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2026