Eduard Bakštein

Ing. Eduard Bakstein, Ph.D.: researcher and head of the biomedical data analysis group at the National Institute of Mental Health and researcher at the compNeuroGroup, Dept. of Cybernetics at the FEE, CTU. Studied cybernetics and biomedical engineering at FEE CTU, where he also defended his PhD in 03/2017. [Full CV in PDF]


Research interests

  • Machine learning
  • Data analysis,
  • (Biomedical) signal processing
  • event detection and prediction.

Selected publications

Bakštein, E., Sieger, T., Novák, D., Růžička, F., & Jech, R. (2018). Automated Atlas Fitting for Deep Brain Stimulation Surgery Based on Microelectrode Neuronal Recordings. In Proceedings of the World Congress on Medical Physics and Biomedical Engineering 2018 (pp. 105–111). [PDF preprint]

Bakštein, E., Sieger, T., Wild, J., Novák, D., Schneider, J., Vostatek, P., Urgošík, D., Jech, R. (2017). Methods for automatic detection of artifacts in microelectrode recordings. In: Journal of Neuroscience Methods, 290, 39–51. [PDF, supplementary: PDFmatlab codes, data (18MB)]

Spaniel, F., Bakstein, E., Anyz, J., Hlinka, J., Sieger, T., Hrdlicka, J., Gornerova, N., Hoschl, C. (2016) Relapse in schizophrenia: definitively not a bolt from the blue. Neuroscience Letters, S0304-3940(16), 30265–8. [PDF preprint at researchgate]

Bakstein, E.; Sieger, T.; Novák, D.; Jech, R.  (2016) Probabilistic model of neuronal background activity in deep brain stimulation trajectories In: Information Technology in Bio- and Medical Informatics. Basel: Springer, 2016, pp. 97-111. LNCS 9832 [PDF preprint]

Bakstein, E., Schneider, J., Sieger, T., Novak, D., Wild, J., Jech, R. (2015)
Supervised segmentation of microelectrode recording artifacts using power spectral density,
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2015-November, pp. 1524-1527. [PDF preprint]

Carmen, C., Isasi, P., Warwick, K., Ruiz, W., Aziz, T., Stein, J., Bakstein, E. (2015): Resting tremor classification and detection in Parkinson's disease patients. Biomedical Signal Processing and Control. 2015, vol. 16, p. 88-97. ISSN 1746-8094. [PDF at researchgate]

Bakstein, E., Burgess, J., Warwick, K., Ruiz, V., Aziz, T.  (2012): Parkinsonian Tremor Identification with Multiple Local Field Potential Feature Classification. Journal of Neuroscience Methods. 2012, vol. 2, no. 209, p. 320-330. ISSN 0165-0270. [PDF preprint]