Artificial neural networks (ANNs) are known to be a powerful tool for data analysis. They are used in social science, robotics, and neurophysiology for solving tasks of classification, forecasting, pattern recognition, etc. In neuroscience, ANNs allow the recognition of specific forms of brain activity from multichannel EEG or MEG data. This makes the ANN an efficient computational core for brain-machine systems. However, despite significant achievements of artificial intelligence in recognition and classification of well-reproducible patterns of neural activity, the use of ANNs for recognition and classification of patterns in neural networks still requires additional attention, especially in ambiguous situations. According to this, in this research, we demonstrate the efficiency of application of the ANN for classification of human MEG trials corresponding to the perception of bistable visual stimuli with different degrees of ambiguity. We show that along with classification of brain states associated with multistable image interpretations, in the case of significant ambiguity, the ANN can detect an uncertain state when the observer doubts about the image interpretation. With the obtained results, we describe the possible application of ANNs for detection of bistable brain activity associated with difficulties in the decision-making process.
References
1.
B.
Müller
, J.
Reinhardt
, and M. T.
Strickland
, Neural Networks: An Introduction
(Springer Science & Business Media
, 2012
).2.
J.
Schmidhuber
, “Deep learning in neural networks: An overview
,” Neural Networks
61
, 85
–117
(2015
).3.
Y.
LeCun
, Y.
Bengio
, and G.
Hinton
, “Deep learning
,” Nature
521
, 436
–444
(2015
).4.
M.
Lukoševičius
and H.
Jaeger
, “Reservoir computing approaches to recurrent neural network training
,” Comput. Sci. Rev.
3
, 127
–149
(2009
).5.
M.
Lee
, S.
Bressler
, and R.
Kozma
, “Advances in cognitive engineering using neural networks
,” Neural Networks
92
, 1
–2
(2017
).6.
L.
Guo
, D.
Rivero
, and A.
Pazos
, “Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks
,” J. Neurosci. Methods
193
, 156
–163
(2010
).7.
S. C.
Jun
and B. A.
Pearlmutter
, “Fast robust subject-independent magnetoencephalographic source localization using an artificial neural network
,” Hum. Brain Mapping
24
, 21
–34
(2005
).8.
M.
Misaki
and S.
Miyauchi
, “Application of artificial neural network to FMRI regression analysis
,” Neuroimage
29
, 396
–408
(2006
).9.
T.
Emoto
, U. R.
Abeyratne
, Y.
Chen
, I.
Kawata
, M.
Akutagawa
, and Y.
Kinouchi
, “Artificial neural networks for breathing and snoring episode detection in sleep sounds
,” Physiol. Meas.
33
, 1675
(2012
).10.
L.
Quitadamo
, F.
Cavrini
, L.
Sbernini
, F.
Riillo
, L.
Bianchi
, S.
Seri
, and G.
Saggio
, “Support vector machines to detect physiological patterns for EEG and EMG-based human–computer interaction: A review
,” J. Neural Eng.
14
, 011001
(2017
).11.
A. E.
Hramov
, S. V.
Pchelintseva
, A. E.
Runnova
, V. Y.
Musatov
, V. V.
Grubov
, M. O.
Zhuravlev
, V. A.
Maksimenko
, A. A.
Koronovskii
, and A. N.
Pisarchik
, “Classifying the perceptual interpretations of a bistable image using EEG and artificial neural networks
,” Front. Neurosci.
11
, 674
(2017
).12.
E. S.
Nurse
, P. J.
Karoly
, D. B.
Grayden
, and D. R.
Freestone
, “A generalizable brain-computer interface (BCI) using machine learning for feature discovery
,” PloS one
10
, e0131328
(2015
).13.
G.
Ghazaei
, A.
Alameer
, P.
Degenaar
, G.
Morgan
, and K.
Nazarpour
, “Deep learning-based artificial vision for grasp classification in myoelectric hands
,” J. Neural Eng.
14
, 036025
(2017
).14.
Y.
Ma
, X.
Ding
, Q.
She
, Z.
Luo
, T.
Potter
, and Y.
Zhang
, “Classification of motor imagery EEG signals with support vector machines and particle swarm optimization
,” Comput. Math. Methods Med.
2016
, 4941235
.15.
M. H.
Alomari
, A.
Samaha
, and K.
AlKamha
, “Automated classification of L/R hand movement EEG signals using advanced feature extraction and machine learning
,” International Journal of Advanced Computer Science and Applications
4
(6) (2013
).16.
W.
Webber
, R. P.
Lesser
, R. T.
Richardson
, and K.
Wilson
, “An approach to seizure detection using an artificial neural network (ANN)
,” Electroencephalogr. Clin. Neurophysiol.
98
, 250
–272
(1996
).17.
P.
Fergus
, D.
Hignett
, A.
Hussain
, D.
Al-Jumeily
, and K.
Abdel-Aziz
, “Automatic epileptic seizure detection using scalp EEG and advanced artificial intelligence techniques
,” BioMed Res. Int.
2015
, 986736
.18.
C.
Galletly
, C.
Clark
, and A.
McFarlane
, “Artificial neural networks and psychiatric disorders
,” Brit. J. Psychiatry
169
, 793
–794
(1996
).19.
E.
Grossi
, F.
Veggo
, A.
Narzisi
, A.
Compare
, and F.
Muratori
, “Pregnancy risk factors in autism: A pilot study with artificial neural networks
,” Pediatr. Res.
79
, 339
–347
(2015
).20.
Z.
Zhang
, F.
Vanderhaegen
, and P.
Millot
, “Prediction of human behaviour using artificial neural networks
,” Lecture Notes Comput. Sci.
3930
, 770
(2006
).21.
L.
Necker
, “Observations on some remarkable phenomena seen in Switzerland and on an optical phenomenon which occurs on viewing of a crystal or geometrical solid
,” Philos. Mag.
111
, 329
–337
(1832
).22.
B.
Mathes
, D.
Strüber
, M. A.
Stadler
, and C.
Basar-Eroglu
, “Voluntary control of Necker cube reversals modulates the EEG delta-and gamma-band response
,” Neurosci. Lett.
402
, 145
–149
(2006
).23.
A. N.
Pisarchik
, R.
Jaimes-Reategui
, C. D. A.
Magallón-Garcia
, and C. O.
Castillo-Morales
, “Critical slowing down and noise-induced intermittency in bistable perception: Bifurcation analysis
,” Biol. Cybern.
108
, 397
–404
(2014
).24.
A. E.
Runnova
, A. E.
Hramov
, V.
Grubov
, A. A.
Koronovsky
, M. K.
Kurovskaya
, and A. N.
Pisarchik
, “Theoretical background and experimental measurements of human brain noise intensity in perception of ambiguous images
,” Chaos, Solitons Fractals
93
, 201
–206
(2016
).25.
A. E.
Hramov
, M. K.
Kurovskaya
, A. E.
Runnova
, M. O.
Zhuravlev
, V. V.
Grubov
, A. A.
Koronovskii
, A. N.
Pavlov
, and A. N.
Pisarchik
, “Intermittent behavior in the brain neuronal network in the perception of ambiguous images
,” in Dynamics and Fluctuations in Biomedical Photonics XIV
(International Society for Optics and Photonics
, 2017
), p. 1006314
.26.
A. N.
Pisarchik
, I. A.
Bashkirtseva
, and L.
Ryashko
, “Controlling bistability in a stochastic perception model
,” Eur. Phys. J. Spec. Top.
224
, 1477
–1484
(2015
).27.
I. A.
Bashkirtseva
and L.
Ryashko
, “Stochastic sensitivity of a bistable energy model for visual perception
,” Indian J. Phys.
91
, 57
–62
(2017
).28.
A.
Pastukhov
, P. E.
Garcia-Rodriguez
, J.
Haenicke
, A.
Guillamon
, G.
Deco
, and J.
Braun
, “Multi-stable perception balances stability and sensitivity
,” Front. Comput. Neurosci.
7
, 17
(2013
).29.
R. H. S.
Carpenter
, “Analysing the detail of saccadic reaction time distributions
,” Biocybern. Biomed. Eng.
32
, 49
–63
(2012
).30.
I.
Merk
and J.
Schnakenberg
, “A stochastic model of multistable visual perception
,” Biol. Cybern.
86
, 111
–116
(2002
).31.
D. A.
Leopold
, M.
Wilke
, A.
Maier
, and N. K.
Logothetis
, “Stable perception of visually ambiguous patterns
,” Nature Neurosci.
5
, 605
–609
(2002
).32.
S.
Taulu
and R.
Hari
, “Removal of magnetoencephalographic artifacts with temporal signal-space separation: Demonstration with single-trial auditory-evoked responses
,” Hum. Brain Mapping
30
, 1524
–1534
(2009
).33.
F.
Amato
, A.
López
, E. M.
Peña-Méndez
, P.
Vaňhara
, A.
Hampl
, and J.
Havel
, “Artificial neural networks in medical diagnosis
,” J. Appl. Biomed.
11
, 47
–58
(2013
).34.
T.
Strutz
, Data Fitting and Uncertainty (a Practical Introduction to Weighted Least Squares and Beyond)
, 2nd ed. (Springer
, 2016
).35.
T.
Inui
, S.
Tanaka
, T.
Okada
, S.
Nishizawa
, M.
Katayama
, and J.
Konishi
, “Neural substrates for depth perception of the Necker cube: A functional magnetic resonance imaging study in human subjects
,” Neurosci. Lett.
282
, 145
–148
(2000
).36.
T. J.
Müller
, A.
Federspiel
, H.
Horn
, K.
Lovblad
, C.
Lehmann
, T.
Dierks
, and W.
Strick
, “The neurophysiological time pattern of illusionary visual perceptual transitions: A simultaneous EEG and fMRI study
,” Int. J. Psychophysiol.
55
, 299
–312
(2005
).37.
J.
Kornmeier
and M.
Bach
, “Bistable perception along the processing chain from ambiguous visual input to a stable percept
,” Int. J. Psychophysiol.
62
, 345
–349
(2006
).38.
A. P.
Mapp
, H.
Ono
, and R.
Barbeito
, “What does the dominant eye dominate? a brief and somewhat contentious review
,” Atten. Percept. Psychophys.
65
, 310
–317
(2003
).39.
S.
Chokron
and M.
De Agostini
, “Reading habits and line bisection: A developmental approach
,” Cognitive Brain Res.
3
, 51
–58
(1995
).40.
M. E.
Nicholls
and G. R.
Roberts
, “Can free-viewing perceptual asymmetries be explained by scanning, pre-motor or attentional biases?
,” Cortex
38
, 113
–136
(2002
).41.
H. R.
Heekeren
, S.
Marrett
, P. A.
Bandettini
, and L. G.
Ungerleider
, “A general mechanism for perceptual decision-making in the human brain
,” Nature
431
, 859
–862
(2004
).42.
K.
Starcke
and M.
Brand
, “Decision making under stress: A selective review
,” Neurosci. Biobehav. Rev.
36
, 1228
–1248
(2012
).43.
S. E.
Wemm
and E.
Wulfert
, “Effects of acute stress on decision making
,” Appl. Psychophysiol. Biofeedback
42
, 1
–12
(2017
).44.
S.
Amitay
, J.
Guiraud
, E.
Sohoglu
, O.
Zobay
, B. A.
Edmonds
, Y.-X.
Zhang
, and D. R.
Moore
, “Human decision making based on variations in internal noise: An EEG study
,” PloS one
8
, e68928
(2013
).45.
R.
Poli
, D.
Valeriani
, and C.
Cinel
, “Collaborative brain-computer interface for aiding decision-making
,” PloS one
9
, e102693
(2014
).© 2018 Author(s).
2018
Author(s)
You do not currently have access to this content.
