ANALIZA WPŁYWU DOBORU ODPROWADZEŃ REFERENCYJNYCH ZAPISU EEG NA UZYSKANE WIDMO

Adeli H., Ghosh-Dastidar S., Dadmehr N.: A wavelet chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy. IEEE Trans. Biomedical Eng. 54(2)/2007, 205–211.
  Google Scholar

Automated Neural Network Detection of EEG Spikes. IEEE Engineering in Medicine and Biology[J], (3/4)/1995, 0739–5175.
  Google Scholar

Atcherson S. R., Gould H. J., Pousson M.A., Prout T. M.: Variability of electrode positions using electrode caps. Brain Topogr. 20/2007, 105–111.
  Google Scholar

Cunnington R., Iansek R., Bradshaw J. L., Phillips, J. G.: Movement-related potentials associated with movement preparation and motor imagery. Experimental brain research 111(3)/1996, 429–436.
  Google Scholar

Croft R., Barry R.: EOG correction: a new perspective. Electroencephalogr. Clin. Neurophysiol. 107/1998, 387–394.
  Google Scholar

De Weerd A. W., Despland P.A., Plouin P.: Neonatal EEG. The international federation of clinical neurophysiology. Electroencephalogr Clin Neurophysiol Suppl. 52/1999, 149–157.
  Google Scholar

Farwell L. A., Donchin E.: Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and clinical Neurophysiology 70(6)/1988, 510–523.
  Google Scholar

Joyce C., Gorodnitsky I., Kutas M.: Automatic removal of eye movement and blink artifacts from eeg data using blind component separation. Psychophysiology 41/2004, 313–325.
  Google Scholar

Kulaichev A. P.: Optimal choice of a reference electrode for EEG recording. Moscow University Biological Sciences Bulletin 71(3)/2016, 145–150.
  Google Scholar

Mak, J. N., Wolpaw, J. R.: Clinical applications of brain-computer interfaces: current state and future prospects. Biomedical Engineering, IEEE Reviews 2/2009, 187–199.
  Google Scholar

Miller K., Halpern C.: Stereotactic Bony Trajectory Preservation for Responsive Neurostimulator Lead Placement Following Depth EEG Recording. Cureus 8(3)/2016.
  Google Scholar

Ng S. C., Raveendran, P.: Comparison of different Montages on to EEG classification. In 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006, Springer Berlin Heidelberg, 365–368.
  Google Scholar

Patel R., Gireesan K., Sengottuvel S., Janawadkar M. P., Radhakrishnan T. S.: Common Methodology for Cardiac and Ocular Artifact Suppression from EEG Recordings by Combining Ensemble Empirical Mode Decomposition with Regression Approach. Journal of Medical and Biological Engineering 2017, 1–8.
  Google Scholar

Pfurtscheller G., Neuper C.: Motor imagery and direct brain-computer communication. Proceedings of the IEEE 89(7)/2001, 1123–1134.
  Google Scholar

Stasinski R.: Radix-K FFT's using K-point convolutions. IEEE Transactions Signal Processing 42/1994, 743–750.
  Google Scholar

Tanner A. E., Särkelä M. O., Virtanen J., Viertiö-Oja H. E., Sharpe M. D., Norton L., Young G. B.: Application of subhairline EEG montage in intensive care unit: comparison with full montage. Journal of Clinical Neurophysiology 31(3)/2014, 181–186.
  Google Scholar

Tokariev A., Sampsa V., Palva J.: Analysis of infant cortical synchrony is constrained by the number of recording electrodes and the recording montage. Clinical Neurophysiology 127(1)/2016, 310–323.
  Google Scholar

Trambaiolli L. R., Lorena A. C., Fraga F. J., Kanda P. A. M. K., Nitrini R., Anghinah R.: Does EEG montage influence Alzheimer's disease electroclinic diagnosis? International Journal of Alzheimer’s Disease, 2011.
  Google Scholar