Analysis of the application of brain-computer interfaces of a selected paradigm in everyday life

B. Michalik, Analiza porównawcza wydajności klasyfikatorów pod nadzorem w realizacji interfejsu mózg-komputer opartego o paradygmat P300, Praca inżynierska, Politechnika Lubelska, Lublin, 2018.
  Google Scholar

D. Aminaka, S. Makino, T. M. Rutkowski, Chromatic SSVEP BCI paradigm targeting the higher frequency EEG responses, Asia-Pacific Signal and Information Processing Association, Annual Summit and Conference (APSIPA) (2014) 1-7.
DOI: https://doi.org/10.1109/APSIPA.2014.7041761   Google Scholar

F. Aloise, P. Aricò, F. Schettini, S. Salinari, D. Mattia, F. Cincotti, Asynchronous gaze-independent event-related potential-based brain–computer interface, Artificial Intelligence in Medicine 59(2) (2013) 61–69.
DOI: https://doi.org/10.1016/j.artmed.2013.07.006   Google Scholar

R. A. Robbins, Z. Simmons, B. A. Bremer, S. M. Walsh, S. Fischer, Quality of life in ALS is maintained as physical function declines, Neurology 56(4) (2001) 442–444.
DOI: https://doi.org/10.1212/WNL.56.4.442   Google Scholar

T. Cedro, A. Grzanka, CeDeROM Brain Computer Interface, Information Technologies in Biomedicine (2012) 219-231.
DOI: https://doi.org/10.1007/978-3-642-31196-3_22   Google Scholar

M. M. Jackson, R. Mappus, Applications for Brain-Computer Interfaces, Brain-Computer Interfaces (2010) 89-103.
DOI: https://doi.org/10.1007/978-1-84996-272-8_6   Google Scholar

H. Suryotrisongko, F. Samopa, Evaluating OpenBCI Spiderclaw V1 Headwear’s Electrodes Placements for Brain-Computer Interface (BCI) Motor Imagery Application, Procedia Computer Science 72 (2015) 398–405.
DOI: https://doi.org/10.1016/j.procs.2015.12.155   Google Scholar

K. Cieślak-Blinowska, R. Kuś, J. Ginter, Propagation of EEG activity during finger movement and its imagination, Acta Neurobiologiae experimentalis 66 (2006) 195-206.
  Google Scholar

C. W. Anderson, E.A. Stolz, S. Shamsunder, Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks, IEEE Transactions on Biomedical Engineering 45(3) (1998) 277-286.
DOI: https://doi.org/10.1109/10.661153   Google Scholar

M. Kołodziej, A. Majkowski, R. J. Rak, Linear discriminant analysis as a feature reduction technique of EEG signal for brain-computer interfaces, Przegląd Elektrotechniczny 88 (2012) 28-30.
  Google Scholar

S. Aggarwal, N. Chugh, Review of Machine Learning Techniques for EEG Based Brain Computer Interface, Archives of Computational Methods in Engineering (2022) 1-20.
DOI: https://doi.org/10.1007/s11831-021-09684-6   Google Scholar

A. Hulewicz, M. Jukiewicz, Analiza sygnałów EEG na potrzeby interfejsu mózg-komputer, Acta Bio-Optica et Informatica Medica. Inżyneria Biomedyczna 3 (2014) 137-143.
  Google Scholar

A. Cegielska, M. Olszewski, Nieinwazyjny interfejs mózg-komputer do zastosowań technicznych, Pomiary Automatyka Robotyka 3 (2015) 5-14
DOI: https://doi.org/10.14313/PAR_217/5   Google Scholar