PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2019 | Vol. 15, no. 3 | 1--8
Tytuł artykułu

Azure Machine Learning tools efficiency in the electroencephalographic signal P300 standard and target responses classification

Wybrane pełne teksty z tego czasopisma
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The Event-Related Potentials were investigated on a group of 70 participants using the dense array electroencephalographic amplifier with photogrammetry geodesic station. The source localisation was computed for each participant. The activity of brodmann areas (BAs) involved in the brain cortical activity of each participant was measured. Then the mean electric charge flowing through particular areas was calculated. The five different machine learning tools (logistic regression, boosted decision tree, Bayes point machine, classic neural network and averaged perceptron classifier) from the Azure ecosystem were trained, and their accuracy was tested in the task of distinguishing standard and target responses in the experiment. The efficiency of each tool was compared, and it was found out that the best tool was logistic regression and the boosted decision tree in our task. Such an approach can be useful in eliminating somatosensory responses in experimental psychology or even in establishing new communication protocols with mildly mentally disabled subjects.
Wydawca

Rocznik
Strony
1--8
Opis fizyczny
Bibliogr. 36 poz., rys., tab.
Twórcy
  • Department of Neuroinformatics, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, ul. Akademicka 9/509, 20-033 Lublin, Poland, gmwojcik@live.umcs.edu.pl
  • Department of Neuroinformatics, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, ul. Akademicka 9/509, 20-033 Lublin, Poland
  • Department of Neuroinformatics, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, ul. Akademicka 9/509, 20-033 Lublin, Poland
  • Department of Neuroinformatics, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, ul. Akademicka 9/509, 20-033 Lublin, Poland
  • Neurophysiological Independent Unit of the Department of Psychiatry, Medical University of Lublin, ul. Gluska 2, 20-439 Lublin, Poland
Bibliografia
  • [1] Pascual-Marqui RD, Michel CM, Lehmann D. Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. Int J Psychophysiol 1994;18:49-65.
  • [2] Pascual-Marqui RD, Lehmann D, Koenig T, Kochi K, Merlo MC, Hell D, et al. Low resolution brain electromagnetic tomography (LORETA) functional imaging in acute, neuroleptic-naive, first-episode, productive schizophrenia. Psychiat Res Neuroim 1999;90:169-79.
  • [3] Pascual-Marqui RM, et al. Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find Exp Clin Pharmacol 2002;24:5-12.
  • [4] Kamarajan C, Porjesz B. Advances in electrophysiological research. Alcohol Res 2015;37:53.
  • [5] Tohka J, Ruotsalainen U. Imaging brain change across different time scales. Front Neuroinform 2012;6:29.
  • [6] Goldenholz DM, Ahlfors SP, Hämäläinen MS, Sharon D, Ishitobi M, Vaina LM, et al. Mapping the signal-to-noise-ratios of cortical sources in magnetoencephalography and electroencephalography. Hum Brain Mapp 2009;30:1077-86.
  • [7] Nidal K, Malik AS. EEG/ERP analysis: methods and applications. Boca Raton, FL: CRC Press, 2014.
  • [8] Campanella S. Why it is time to develop the use of cognitive Event-Related Potentials in the treatment of psychiatric diseases. Neuropsych Dis Treat 2013;9:1835.
  • [9] Wojcik GM, Masiak J, Kawiak A, Schneider P, Kwasniewicz L, Polak N, Gajos-Balinska A. New protocol for quantitative analysis of brain cortex electroencephalographic activity in patients with psychiatric disorders. Front Neuroinform 2018;12:27.
  • [10] Viaene AN, Petrof I, Sherman SM. Synaptic properties of thalamic input to layers 2/3 and 4 of primary somatosensory and auditory cortices. J Neurophysiol 2010;105:279-92.
  • [11] Elliott R. Executive functions and their disorders: imaging in clinical neuroscience. Brit Med Bull 2003;65:49-59.
  • [12] Monsell S. Task switching. Trends Cogn Sci 2001;7:134-40.
  • [13] Chan RC, Shum D, Toulopoulou T, Chen EY. Assessment of executive functions: review of instruments and identification of critical issues. Arch Clin Neuropsych 2008;23:201-16.
  • [14] Wikenheiser AM, Schoenbaum G. Over the river, through the woods: cognitive maps in the hippocampus and orbitofrontal cortex. Nat Rev Neurosci 2016;17:513.
  • [15] Fettes P, Schulze L, Downar J. Cortico-striatal-thalamic loop circuits of the orbitofrontal cortex: promising therapeutic targets in psychiatric illness. Front Syst Neurosci 2017;11:25.
  • [16] Wilson RC, Takahashi YK, Schoenbaum G, Niv Y. Orbitofrontal cortex as a cognitive map of task space. Neuron 2014;81:267-79.
  • [17] Sadacca BF, Wikenheiser AM, Schoenbaum G. Toward a theoretical role for tonic norepinephrine in the orbitofrontal cortex in facilitating flexible learning. Neuroscience 2017;345:124-9.
  • [18] Wierzgała P, Zapala D, Wójcik GM, Masiak J. Most popular signal processing methods in motor-imagery BCI: a review and meta-analysis. Front Neuroinform 2018;12:78.
  • [19] Mikołajewska E, Mikołajewski D. Ethical considerations in the use of brain-computer interfaces. Cent Eur J Med 2013;8:720-4.
  • [20] Szaleniec J, Wiatr M, Szaleniec M, SkłAdzień J, Tomik J, Oleś K, Tadeusiewicz R. Artificial neural network modelling of the results of tympanoplasty in chronic suppurative otitis media patients. Comput Biol Med 2013;43:16-22.
  • [21] Ogiela L, Tadeusiewicz R, Ogiela MR. Cognitive techniques in medical information systems. Comput Biol Med 2008;38:501-7.
  • [22] Koczkodaj WW, Szybowski J. Pairwise comparisons simplified. Appl Math Comput 2015;253:387-94.
  • [23] Kakiashvili T, Koczkodaj WW, Woodbury-Smith M. Improving the medical scale predictability by the pairwise comparisons method: evidence from a clinical data study. Comput Meth Prog Bio 2012;105:210-6.
  • [24] Ważny M, Wojcik GM. Shifting spatial attention - numerical model of posner experiment. Neurocomputing 2014;135:139-44.
  • [25] Wojcik GM. Electrical parameters influence on the dynamics of the Hodgkin-Huxley liquid state machine. Neurocomputing 2012;79:68-74.
  • [26] Wojcik GM, Ważny M. Bray-Curtis metrics as measure of liquid state machine separation ability in function of connections density:[procs 51c (2015) 2948-2951]. Procedia Comput Sci 2015;51:2978.
  • [27] Kufel D, Wojcik GM. Analytical modelling of temperature effects on an AMPA-type synapse. J Comput Neurosci 2018;44:379-91.
  • [28] Wojcik GM, Kaminski WA. Liquid state machine and its separation ability as function of electrical parameters of cell. Neurocomputing 2007;70:2593-7.
  • [29] Wojcik GM, Kaminski WA, Matejanka P. Self-organised criticality in a model of the rat somatosensory cortex. In International conference on parallel computing technologies. Cham: Springer, 2007: 468-76.
  • [30] Wojcik GM, Kaminski WA. Self-organised criticality as a function of connections’ number in the model of the rat somatosensory cortex. In: International conference on computational science. Cham: Springer, 2008:620-9.
  • [31] Wojcik GM, Garcia-Lazaro JA. Analysis of the neural hypercolumn in parallel pcsim simulations. Procedia Comput Sci 2010;1:845-54.
  • [32] Kotyra S, Wojcik GM. The station for neurofeedback phenomenon research. In: Polish conference on biocybernetics and biomedical engineering. Cham: Springer, 2017:32-43.
  • [33] Kotyra S, Wojcik GM. Steady state visually evoked potentials and their analysis with graphical and acoustic transformation. In: Polish conference on biocybernetics and biomedical engineering. Cham: Springer, 2017:22-31.
  • [34] Ozga WK, Zapała D, Wierzgała P, Augustynowicz P, Porzak R, Wójcik GM. Acoustic neurofeedback increases beta ERD during mental rotation task. Appl Psychophysiol Biofeedback 2019;44:103-15.
  • [35] John ER, Prichep LS, Fridman J, Easton P. Neurometrics: computer-assisted differential diagnosis of brain dysfunctions. Science 1988;239:162-9.
  • [36] Sand T, Bjørk MH, Vaaler AE. Is eeg a useful test in adult psychiatry? Tidsskri Nor Laegeforen 2013;133:1200-4.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-2f7e8517-eecc-41f1-bff8-a1bb56d32258
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.