Boreom Lee

3.7k total citations
89 papers, 2.7k citations indexed

About

Boreom Lee is a scholar working on Cognitive Neuroscience, Biomedical Engineering and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Boreom Lee has authored 89 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 50 papers in Cognitive Neuroscience, 27 papers in Biomedical Engineering and 19 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Boreom Lee's work include EEG and Brain-Computer Interfaces (37 papers), Functional Brain Connectivity Studies (19 papers) and Neural dynamics and brain function (16 papers). Boreom Lee is often cited by papers focused on EEG and Brain-Computer Interfaces (37 papers), Functional Brain Connectivity Studies (19 papers) and Neural dynamics and brain function (16 papers). Boreom Lee collaborates with scholars based in South Korea, Canada and United States. Boreom Lee's co-authors include Muhammad Naveed Iqbal Qureshi, Jong‐In Kim, Dongrae Cho, Jooyoung Oh, Beomjun Min, Nguyen Thanh Duc, Minsung Choi, Jae Hyuk Shin, Kwang Jin Lee and Hang Joon Jo and has published in prestigious journals such as PLoS ONE, NeuroImage and Scientific Reports.

In The Last Decade

Boreom Lee

89 papers receiving 2.6k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Boreom Lee South Korea 29 1.2k 568 460 394 373 89 2.7k
Yingchun Zhang China 37 2.1k 1.7× 919 1.6× 315 0.7× 367 0.9× 719 1.9× 350 4.8k
Javier Escudero United Kingdom 36 2.7k 2.2× 985 1.7× 701 1.5× 413 1.0× 240 0.6× 165 4.8k
Robert J. Sclabassi United States 37 1.2k 1.0× 767 1.4× 400 0.9× 262 0.7× 176 0.5× 278 4.7k
Tong Boon Tang Malaysia 28 891 0.8× 723 1.3× 524 1.1× 326 0.8× 757 2.0× 205 3.4k
Alexandros T. Tzallas Greece 31 1.9k 1.6× 455 0.8× 588 1.3× 617 1.6× 167 0.4× 173 3.8k
Aydın Akan Türkiye 25 1.0k 0.9× 480 0.8× 460 1.0× 360 0.9× 201 0.5× 249 2.5k
Nadia Mammone Italy 28 1.8k 1.5× 201 0.4× 443 1.0× 343 0.9× 170 0.5× 86 2.6k
N. Sriraam India 23 1.7k 1.4× 298 0.5× 541 1.2× 423 1.1× 128 0.3× 139 2.6k
Malek Adjouadi United States 31 1.2k 1.0× 222 0.4× 165 0.4× 399 1.0× 407 1.1× 263 3.0k
Huiguang He China 35 2.4k 2.0× 182 0.3× 189 0.4× 485 1.2× 658 1.8× 180 3.9k

Countries citing papers authored by Boreom Lee

Since Specialization
Citations

This map shows the geographic impact of Boreom 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 Boreom Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Boreom Lee more than expected).

Fields of papers citing papers by Boreom Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Boreom 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 Boreom Lee. The network helps show where Boreom Lee may publish in the future.

Co-authorship network of co-authors of Boreom Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Boreom Lee. A scholar is included among the top collaborators of Boreom 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 Boreom Lee. Boreom 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.
Cho, Dongrae & Boreom Lee. (2023). Automatic sleep-stage classification based on residual unit and attention networks using directed transfer function of electroencephalogram signals. Biomedical Signal Processing and Control. 88. 105679–105679. 7 indexed citations
2.
3.
Khan, Anwar A. & Boreom Lee. (2023). DeepGene Transformer: Transformer for the gene expression-based classification of cancer subtypes. Expert Systems with Applications. 226. 120047–120047. 24 indexed citations
4.
Kim, Hesun Erin, Jae‐Jin Kim, Jeong‐Ho Seok, et al.. (2023). Differential relationship of observer-rated and self-rated depression and anxiety scales with heart rate variability features. Frontiers in Psychiatry. 14. 1124550–1124550. 3 indexed citations
5.
Lee, Yong‐Moon, Boreom Lee, Nam-Hoon Cho, & Jae Hyun Park. (2023). Beyond the Microscope: A Technological Overture for Cervical Cancer Detection. Diagnostics. 13(19). 3079–3079. 8 indexed citations
6.
Kim, Kyungwon, Nguyen Thanh Duc, Minsung Choi, & Boreom Lee. (2021). EEG microstate features according to performance on a mental arithmetic task. Scientific Reports. 11(1). 343–343. 50 indexed citations
7.
Lee, Boreom, et al.. (2019). Online compressive covariance sensing. Signal Processing. 162. 1–9. 11 indexed citations
8.
9.
Qureshi, Muhammad Naveed Iqbal, et al.. (2019). Evaluation of Functional Decline in Alzheimer’s Dementia Using 3D Deep Learning and Group ICA for rs-fMRI Measurements. Frontiers in Aging Neuroscience. 11. 8–8. 37 indexed citations
10.
Duc, Nguyen Thanh, Seungjun Ryu, Muhammad Naveed Iqbal Qureshi, et al.. (2019). 3D-Deep Learning Based Automatic Diagnosis of Alzheimer’s Disease with Joint MMSE Prediction Using Resting-State fMRI. Neuroinformatics. 18(1). 71–86. 149 indexed citations
11.
Cho, Dongrae & Boreom Lee. (2017). Optimized automatic sleep stage classification using the normalized mutual information feature selection (NMIFS) method. PubMed. 2017. 3094–3097. 2 indexed citations
12.
Lee, Boreom, et al.. (2017). Development of automatic retinal vessel segmentation method in fundus images via convolutional neural networks. PubMed. 2017. 681–684. 19 indexed citations
13.
Jeon, Taegyun, et al.. (2016). Robust detection of heartbeats using association models from blood pressure and EEG signals. BioMedical Engineering OnLine. 15(1). 7–7. 6 indexed citations
14.
Qureshi, Muhammad Naveed Iqbal, Beomjun Min, Hang Joon Jo, & Boreom Lee. (2016). Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study. PLoS ONE. 11(8). e0160697–e0160697. 79 indexed citations
15.
Cho, Dongrae, Beomjun Min, Jong‐In Kim, & Boreom Lee. (2016). EEG-Based Prediction of Epileptic Seizures Using Phase Synchronization Elicited from Noise-Assisted Multivariate Empirical Mode Decomposition. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 25(8). 1309–1318. 113 indexed citations
16.
Park, Sangsu, Myonglae Chu, Jong‐In Kim, et al.. (2015). Electronic system with memristive synapses for pattern recognition. Scientific Reports. 5(1). 10123–10123. 154 indexed citations
17.
Lee, Daeseok, Jaesung Park, Kibong Moon, et al.. (2015). Oxide based nanoscale analog synapse device for neural signal recognition system. 4.7.1–4.7.4. 40 indexed citations
18.
Shin, Jae Hyuk, et al.. (2011). Detection of Abnormal Living Patterns for Elderly Living Alone Using Support Vector Data Description. IEEE Transactions on Information Technology in Biomedicine. 15(3). 438–448. 101 indexed citations
19.
Lee, Boreom, Ji Young Park, Wi Hoon Jung, et al.. (2010). White matter neuroplastic changes in long-term trained players of the game of “Baduk” (GO): A voxel-based diffusion-tensor imaging study. NeuroImage. 52(1). 9–19. 77 indexed citations
20.
Baek, Hyun Jae, Ko Keun Kim, Jung Soo Kim, Boreom Lee, & Kwang Suk Park. (2009). Enhancing the estimation of blood pressure using pulse arrival time and two confounding factors. Physiological Measurement. 31(2). 145–157. 46 indexed citations

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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