Talks

 

Selected publications

 

Bakštein, E. (2016) Deep Brain Recordings in Parkinson’s Disease: Processing, Analysis and Fusion with Anatomical Models, doctoral thesis, Czech Technical Univeristy in Prague [PDF]

McWhinney, S. R., Hlinka, J., Bakstein, E., Dietze, L. M. F., Corkum, E. L. V., Abé, C., Alda, M., Alexander, N., Benedetti, F., Berk, M., Bøen, E., Bonnekoh, L. M., Boye, B., Brosch, K., Canales-Rodríguez, E. J., Cannon, D. M., Dannlowski, U., Demro, C., Diaz-Zuluaga, A., … Hajek, T. (2024). Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity. Human Brain Mapping, 45(8), 1–16. 

Varga, I., Bakstein, E., Gilmore, G., May, J., & Novak, D. (2024). Statistical segmentation model for accurate electrode positioning in Parkinson’s deep brain stimulation based on clinical low-resolution image data and electrophysiology. PLoS ONE, 19(3 March), 1–23.

Schneider, J., Bakštein, E., Kolenič, M., Vostatek, P., Correll, C. U., Novák, D., & Španiel, F. (2022). Motor activity patterns can distinguish between interepisode bipolar disorder patients and healthy controls. CNS Spectrums, 27(1), 82–92.

Anýž, J., Bakštein, E., Dally, A., Kolenič, M., Hlinka, J., Hartmannová, T., Urbanová, K., Correll, C. U., Novák, D., & Španiel, F. (2021). Validity of the Aktibipo Self-rating Questionnaire for the Digital Self-assessment of Mood and Relapse Detection in Patients With Bipolar Disorder: Instrument Validation Study. JMIR Mental Health, 8(8), e26348.

Schneider, J., Bakštein, E., Kolenič, M., Vostatek, P., Correll,C. U., Novák, D. and Španiel, F. (2020). Motor Activity Patterns Can Distinguish Between Inter-Episode Bipolar Disorder Patients and Healthy Controls. CNS Spectrums, September 2020

Bakstein, E., Mladá, K., Fárková, E., Kolenič, M., Španiel, F., Manková, D., … Hajek, T. (2020). Cross‐sectional and within‐subject seasonality and regularity of hospitalizations: A population study in mood disorders and schizophrenia. Bipolar Disorders, (902), bdi.12884.

Serranová, T., Sieger, T., Růžička, F., Bakštein, E., Dušek, P., Vostatek, P., … Jech, R. (2019). Topography of emotional valence and arousal within the motor part of the subthalamic nucleus in Parkinson’s disease. Scientific Reports, 9(1), 19924.

Klempíř, O., Krupička, R., Bakštein, E., & Jech, R. (2019). Identification of Microrecording Artifacts with Wavelet Analysis and Convolutional Neural Network: An Image Recognition Approach. Measurement Science Review, 19(5), 222–231. https://doi.org/10.2478/msr-2019-0029

Anýž, J., Bakštein, E., Dudysová, D., Veldová, K., Kliková, M., Fárková, E., … Španiel, F. (2019). No wink of sleep: Population sleep characteristics in response to the brexit poll and the 2016 U.S. presidential election. Social Science and Medicine, 222, 112–121. [PDF author ver.]

Bakštein E., Sieger T., Růžička F., Novák D., Jech R. (2018) Fusion of Microelectrode Neuronal Recordings and MRI Landmarks for Automatic Atlas Fitting in Deep Brain Stimulation Surgery. In: Stoyanov D. et al. (eds) OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis. CARE 2018, CLIP 2018, OR 2.0 2018, ISIC 2018. Lecture Notes in Computer Science, vol 11041. Springer, Cham, [PDF preprint]

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: PDF, matlab 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]

Awards

IFBME Young Ivestigator Competition (3rd place), at the World Congress on Medical Physics and Biomedical Engineering, Prague, Czech Republic, June 3-8. 2018

Other

Utility model: A device for scanning dermatoglyphic palm patterns (2018, link)