Научная статья на тему 'Detection of the EEG-Correlates of subjective significance of visual stimuli'

Detection of the EEG-Correlates of subjective significance of visual stimuli Текст научной статьи по специальности «Медицинские технологии»

CC BY
91
17
i Надоели баннеры? Вы всегда можете отключить рекламу.
i Надоели баннеры? Вы всегда можете отключить рекламу.
iНе можете найти то, что вам нужно? Попробуйте сервис подбора литературы.
i Надоели баннеры? Вы всегда можете отключить рекламу.

Текст научной работы на тему «Detection of the EEG-Correlates of subjective significance of visual stimuli»

Volga Neuroscience School 2016 Astroglial control of rhythm genesis in the brain

Detection of the EEG-Correlates of Subjective Significance of Visual Stimuli

I.P. Ganin1,2 *, E.A. Kosichenko1 and A.Ya. Kaplan1'2,3

1 Lomonosov Moscow State University, Moscow, Russian Federation, [email protected]

2 Lobachevsky State University of Nizhni Novgorod, Nizhni Novgorod, Russian Federation

3 Pirogov National Russian Medical University, Moscow, Russian Federation * Presenting e-mail: [email protected]

Aims of the study

Brain-computer interface (BCI) is a highly developing area of investigation in neurophysiology. BCI provides the users brain with new output pathways, enabling an individual to send messages or control devices without using muscles, directly using their electrical activity (EEG). One of the most popular BCIs, the P300 BCI, was proposed as a variation of the classical visual oddball paradigm, a discrimination task well known for eliciting the P300 wave of the event-related potentials (ERP). In this BCI a command is detected by the maximal P300 amplitude in response to flashes of the attended symbol compared to low P300 amplitude in response to all other unattended symbols. However, the attention response to external stimulus can be obtained not only when the user has to respond to the instructed symbol flash, but even when the user does not have specific instruction and the presented stimuli vary in their significance for the user on the basis of his personal experience or emotional state. We hypothesize that using P300 BCI algorhytms and appropriate experimental design a system for EEG- and ERP-based detection of not overt, but covert, intentions or psycho-emotional states of subject can be built. This can be applied in medicine, marketing research, entertainment etc. For primary evaluation of the classification possibility of ERPs in response to stimuli with covert meaning the primary goal was to study ERPs in response to stimuli with overt emotional meaning. In this study we used images of human faces as such emotional stimuli. Thus the aim of this work was to compare the ERPs in response to presenting emotional stimuli against neutral stimuli under conditions when emotional stimuli are attended or unattended.

Methods

Fourteen healthy volunteers (age 19-20 years, five males) participated in the study. Each participant signed informed consent. The participants sat in an armchair and viewed a 24-inch LCD monitor at approximately 80 cm distance from their eyes. The stimuli were presented in the center of the screen and had an angle size of 7.4 x 5.5°. All the stimuli were emotional or neutral human faces photos from the Park Aging Mind Lab database (UT Dallas). The stimuli presentation was organized in 'stimuli sequences' which means presentation of six different photos consequentially in random order. Each stimulus was presented for 200 ms with 400 ms interval. One run consisted of ten stimuli sequences without pauses between them that corresponded presenting totally 60 stimuli (ten times for each photo). The experiment consisted of three blocks; each of them included ten runs. The participants' task in the first block ('Emotional Unattended') was just to look at the center of the screen, where the photos were presented. There was one emotional photo and five neutral photos in each run. The task in the second and the third blocks was to remember the target photo at the beginning of the run and then silently count the number of times this photo was presented among other photos. But in the one block ('Emotional Attended') the target was always the emotional face among neutral ones, and in the other block ('Neutral Attended') the targets and non-targets were both neutral faces. The order of the last two blocks was randomly alternated in all experiments. EEG was recorded from 24 electrodes with reference on mastoids. We analyzed the ERP waveforms and amplitudes for target and non-target stimuli. We also calculated offline classification accuracy for target stimuli in each block for each participant in P300 BCI-similar fashion. In the first block we analyzed group of emotional photos (as targets) against neutral faces (as non-targets), as there was not any discrimination task.

Results

We compared amplitudes of several peaks for the ERP difference (target minus non-target) in three blocks. The amplitudes of N170 and P200 components were higher for the two blocks with emotional faces compared to neutral faces. The amplitudes of N170, P200, P300 and N400 components were the highest in the 'Emotional Attended' condition. For offline classification we used Fisher's linear discriminant analysis which chose one target from six stimuli in each run (thus the random classification level was 1/6 = 16.7%). The maximal mean accuracy for 'Emotional Attended' and 'Neutral Attended' conditions were 97.1% and 89.3%, respectively. Although in the 'Emotional Unattended' condition participants did not have any instructions except looking at the stimuli, the classification accuracy for emotional faces was 36.4%, which exceeds the random level more than twice.

OM&P

OM&P

Volga Neuroscience School 2016 Astroglial control of rhythm genesis in the brain

Conclusions

In our work we made an attempt to find a specific stimuli setup where one stimulus can elicit a prominent ERP response that can be distinguished from ERPs to other stimuli. An emotional face among neutral faces was used as such stimulus under conditions where subjects' task was just to look at all presented stimuli without any other instructions. We found out that such unusual stimulus can not only enhance several ERP components, but also be classified as target stimulus among non-targets as if it was a typical P300 BCI task where the user has to pay attention to the desired stimulus. A particular advantage of using emotional faces as stimuli can be developed from comparing the two conditions where participants had to pay attention to one target stimulus and ignore all the other stimuli: the amplitude of all analyzed ERP components and classification accuracies were higher for 'Emotional Attended' vs 'Neutral Attended' conditions. Our results provide evidence that using stimuli with emotional faces can increase the efficiency of P300 BCI operating and, with adopting presentation paradigm and classification techniques, can be helpful for creating systems for EEG-based emotion detection.

Acknowledgements

This study was partially supported by funding from the Skolkovo Foundation (project 1110034) and from the Russian Science Foundation (15-19-20053).

Multistable Dynamics in the Motif of Inhibitory Coupled Rulkov Neurons

T. A. Levanova1 *, A.O. Kazakov1,2, G. V. Osipov1

1 Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia;

2 National Research University Higher School of Economics, Nizhny Novgorod, Russia. * Presenting e-mail: [email protected]

We study the model of small ensemble of inhibitory-coupled neuron-like elements based on phenomenological Rulkov model of neuron cell [1-4]. We study analytically and numerically possible transitional and stable regimes of neuron-like activity and bifurcation transitions between them. We focus on phenomena of regular and chaotic sequential switching activity. We describe the influence of couplings topology on the dynamics of the ensemble.

Acknowledgements

This study was supported by the Russian Science Foundation (grant 14 12 00811) and the Russian Foundation for Fundamental Research (grant 16-32-00835)

References

1. N.F. Rulkov, Phys. Rev. E, 2002, 65, 041922.

2. A.L. Shilnikov, N.F. Rulkov, 2003, Int. J. Bif. Chaos, 13(11), 3325.

3. A.L. Shilnikov, N.F. Rulkov, Phys. Lett. A, 2004, 328, 177.

4. N.F. Rulkov, I. Timofeev, M. Bazhenov, J. Comput. Neurosci., 2004, 17, 203.

i Надоели баннеры? Вы всегда можете отключить рекламу.