{"id":392,"date":"2025-07-31T19:54:56","date_gmt":"2025-07-31T23:54:56","guid":{"rendered":"https:\/\/blogs.cs.umbc.edu\/choalab\/?page_id=392"},"modified":"2025-09-20T13:22:58","modified_gmt":"2025-09-20T17:22:58","slug":"brain-imaging-eeg-fmri","status":"publish","type":"page","link":"https:\/\/blogs.cs.umbc.edu\/choalab\/brain-imaging-eeg-fmri\/","title":{"rendered":"Brain Imaging – EEG fMRI"},"content":{"rendered":"\n

EEG References<\/h3>\n\n\n\n
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  1. Gupta, D., Du, X., Summerfelt, A., Hong, L. E., & Choa, F.-S. (2023). Brain connectivity signature extractions from TMS invoked EEGs. Sensors, 23<\/em>, 4078. https:\/\/doi.org\/10.3390\/s23084078<\/a><\/li>\n\n\n\n
  2. Gupta, D., Summerfelt, A., Luzhansky, J., Li, D., Hong, E., & Choa, F.-S. (2021). LEDA-Localized-EEG Dynamics Analyzer: a MATLAB-Based Innovative Toolbox for Analysis of EEG Source Dynamics. Journal of Signal Processing Systems, 93<\/em>, 951\u2013964. http:\/\/dx.doi.org\/10.1007\/s11265-020-01617-z<\/a><\/li>\n\n\n\n
  3. Meng, Q., Gupta, D., Wudenhe, A., Du, X., Hong, L. E., & Choa, F.-S. (2017). Three-dimensional EEG signal tracking for reproducible monitoring of self-contemplating imagination. Advances in Science, Technology and Engineering Systems Journal, 2<\/em>(3), 1634\u20131646. https:\/\/doi.org\/10.25046\/aj0203203<\/a><\/li>\n\n\n\n
  4. Gupta, D., Du, X., Velraj, J., Varanasi, S., Hong, E., & Choa, F.-S. (2022). Three-dimension EEG-based connectivity biomarkers for neurological disorder detections. In Cullum, B. M., McLamore, E. S., & Kiehl, D. (Eds.), Smart Biomedical and Physiological Sensor Technology XIV<\/em>. SPIE Proceedings Volume 12123.<\/li>\n\n\n\n
  5. Chowdhury, F. N., Sood, R., Nam, H., Lobo, M. K., & Choa, F.-S. (2019). Flexible polyimide-based 34-channel electrode arrays for mouse EEG measurement. SPIE Proceedings Volume 11020, Smart Biomedical and Physiological Sensor Technology XV<\/em>, 110200T. https:\/\/doi.org\/10.1117\/12.2518830<\/a><\/li>\n\n\n\n
  6. Gupta, D., Du, X., Hong, E., & Choa, F.-S. (2019). Neural Connectivity Analysis by using 3-D TMS-EEG with Source Localization and Sliding Window Coherence Techniques. SPIE Proceedings: Signal Processing, Sensor\/Information Fusion, and Target Recognition XXVIII<\/em>. https:\/\/doi.org\/10.1117\/12.2519065<\/a><\/li>\n\n\n\n
  7. Gupta, D., Du, X., Hong, L., & Choa, F.-S. (2019). TMS-EEG based Source Localized Connectivity Signature Extraction by using Unsupervised Machine Learning. 9th International IEEE\/EMBS Conference on Neural Engineering (NER)<\/em>, San Francisco, CA. https:\/\/doi.org\/10.1109\/NER.2019.8716962<\/a><\/li>\n\n\n\n
  8. Wudenhe, A., Meng, Q., & Choa, F.-S. (2016). Three-Dimensional EEG Signal Tracking for Reproducible Brain Activity Monitoring. IEEE Signal Processing in Medicine and Biology Symposium<\/em>, Paper P01.08, Temple University, Philadelphia, PA. https:\/\/doi.org\/10.1109\/spmb.2016.7846869<\/a><\/li>\n\n\n\n
  9. Meng, Q., Hong, E., & Choa, F.-S. (2016). Electroencephalograph (EEG) Study on Self-Contemplating Image Formation. Proc. SPIE 9863, Smart Biomedical and Physiological Sensor Technology XIII<\/em>, 98630X. https:\/\/doi.org\/10.1117\/12.2225132<\/a><\/li>\n\n\n\n
  10. Meng, Q., Hong, E., & Choa, F.-S. (2015). Electroencephalograph (EEG) study of brain bistable illusion. SPIE DSS 9487<\/em>. https:\/\/doi.org\/10.1117\/12.2177520<\/a><\/li>\n\n\n\n
  11. Meng, Q., Choa, F.-S., Hong, E., Wang, Z., & Islam, M. (2014). Control channels in the brain and their influence on brain executive functions. SPIE Defense, Security, and Sensing Conference<\/em>, Paper 9107-42. https:\/\/doi.org\/10.1117\/12.2050147<\/a><\/li>\n<\/ol>\n\n\n\n
    \n\n\n\n

    fMRI References<\/h3>\n\n\n\n
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    1. Allen, J. D., Varanasi, S., Han, F., Hong, L. E., & Choa, F.-S. (2024). Functional Connectivity Biomarker Extraction for Schizophrenia Based on Energy Landscape Machine Learning Techniques. Sensors, 24<\/em>(23), 7742. https:\/\/doi.org\/10.3390\/s24237742<\/a><\/li>\n\n\n\n
    2. Varanasi, S., Tuli, R., Han, F., Chen, R., & Choa, F.-S. (2023). Age Related Functional Connectivity Signature Extraction Using Energy-Based Machine Learning Techniques. Sensors, 23<\/em>(3), 1603. https:\/\/doi.org\/10.3390\/s23031603<\/a><\/li>\n\n\n\n
    3. Varanasi, S., Zhai, T., Gu, H., Yang, Y., & Choa, F.-S. (2024). Functional connectivity differences between cocaine users and healthy controls: an fMRI study. In Cullum, B. M., McLamore, E. S., & Kiehl, D. (Eds.), Smart Biomedical and Physiological Sensor Technology XXI<\/em> (p. 9). SPIE. https:\/\/doi.org\/10.1117\/12.3013689<\/a><\/li>\n\n\n\n
    4. Varanasi, S., Zhai, T., Gu, H., Yang, Y., & Choa, F.-S. (2024). Extracting functional connectivity signatures in substance use disorder using energy landscape analysis. In Cullum, B. M., McLamore, E. S., & Kiehl, D. (Eds.), Smart Biomedical and Physiological Sensor Technology XXI<\/em> (p. 8). SPIE. https:\/\/doi.org\/10.1117\/12.3013694<\/a><\/li>\n\n\n\n
    5. Allen, J. D., Xia, L., Hong, L. E., & Choa, F.-S. (2024). Exploring connections between auditory hallucinations and language model structures and functions. In Cullum, B. M., McLamore, E. S., & Kiehl, D. (Eds.), Smart Biomedical and Physiological Sensor Technology XXI<\/em> (p. 10). SPIE. https:\/\/doi.org\/10.1117\/12.3013964<\/a><\/li>\n\n\n\n
    6. Allen, J. D., Xia, L., Hong, L. E., & Choa, F.-S. (2024). Exploring connections between auditory hallucinations and language model structures and functions. Proc. SPIE 13059, Smart Biomedical and Physiological Sensor Technology XXI<\/em>, 130590A. https:\/\/doi.org\/10.1117\/12.3013964<\/a><\/li>\n\n\n\n
    7. Allen, J., Varanasi, S., Chen, R., Hong, E., & Choa, F.-S. (2023). Observing Brain Most Visited Common Band Connectivity States from fMRI Resting State Studies. IEEE 11th International IEEE\/EMBS Conference on Neural Engineering (NER)<\/em>, 1\u20134. https:\/\/doi.org\/10.1109\/ner52421.2023.10123853<\/a><\/li>\n\n\n\n
    8. Allen, J. D., Varanasi, S., Hong, E., & Choa, F.-S. (2022). Flexible energy landscape analysis of functional connectivity through region bundling. In Grewe, L. L., Blasch, E. P., & Kadar, I. (Eds.), Signal Processing, Sensor\/Information Fusion, and Target Recognition XXXI<\/em>. SPIE Proceedings Volume 12122. http:\/\/dx.doi.org\/10.1117\/12.2619126<\/a><\/li>\n\n\n\n
    9. Varanasi, S., Allen, J. D., Chen, R., Sahoo, K. P., Patra, A., & Choa, F.-S. (2022). Energy landscape analysis based sliding window studies of brain dynamics in young and old subjects. In Grewe, L. L., Blasch, E. P., & Kadar, I. (Eds.), Signal Processing, Sensor\/Information Fusion, and Target Recognition XXXI<\/em>. Proc. SPIE 12122, 1212212. http:\/\/dx.doi.org\/10.1117\/12.2618951<\/a><\/li>\n\n\n\n
    10. Udall, J., & Choa, F.-S. (2022). Comparing energy levels in brain regions of interest in ADHD subjects. In Cullum, B. M., McLamore, E. S., & Kiehl, D. (Eds.), Smart Biomedical and Physiological Sensor Technology XIX<\/em>. SPIE Proceedings Volume 12123, 1212305. http:\/\/dx.doi.org\/10.1117\/12.2618207<\/a><\/li>\n\n\n\n
    11. Varanasi, S., Allen, J., Chen, R., & Choa, F.-S. (2021). Sliding window study of brain connectivity dynamics based on Energy Landscape analysis. SPIE Proceedings Volume 11756, Signal Processing, Sensor\/Information Fusion, and Target Recognition XXX<\/em>, 117560Z. https:\/\/doi.org\/10.1117\/12.2588048<\/a><\/li>\n\n\n\n
    12. Allen, J. D., Varanasi, S., Hong, E., & Choa, F.-S. (2021). Energy landscape analysis of fMRI data from schizophrenic and healthy subjects. SPIE Proceedings Volume 11756, Signal Processing, Sensor\/Information Fusion, and Target Recognition XXX<\/em>, 1175610. https:\/\/doi.org\/10.1117\/12.2588046<\/a><\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

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