Classifying the Perceptual Interpretations of a Bistable Image Using EEG and Artificial Neural Networks

In order to classify different human brain states related to visual perception of ambiguous images, we use an artificial neural network (ANN) to analyze multichannel EEG. The classifier built on the basis of a multilayer perceptron achieves up to 95% accuracy in classifying EEG patterns corresponding to two different interpretations of the Necker cube. The important feature of our classifier is that trained on one subject it can be used for the classification of EEG traces of other subjects. This result suggests the existence of common features in the EEG structure associated with distinct interpretations of bistable objects. We firmly believe that the significance of our results is not limited to visual perception of the Necker cube images; the proposed experimental approach and developed computational technique based on ANN can also be applied to study and classify different brain states using neurophysiological data recordings. This may give new directions for future research in the field of cognitive and pathological brain activity, and for the development of brain-computer interfaces

Experimental study of oscillatory patterns in the human EEG during the perception of bistable images

We propose a new approach for the quantitative characterization of cognitive human brain activity during visual perception. According to the theoretical background we analyze human electro-encephalograms (EEG) obtained while the subjects observe ambiguous images. We found that the decision-making process is characterized by specific oscillatory patterns in the multi-channel EEG data

Human personality reflects spatio-temporal and time-frequency EEG structure.

The reliable and objective assessment of intelligence and personality has been a topic of increasing interest of contemporary neuroscience and psychology. It is known that intelligence can be measured by estimating the mental speed or velocity of information processing. This is usually measured as a reaction time during elementary cognitive task processing, while personality is often assessed by means of questionnaires. On the other hand, human personality affects the way a subject accomplishes elementary cognitive tasks and, therefore, some personality features can define intelligence. It is expected that these features, as well as mental abilities in performing cognitive tasks are associated with the brain's electrical neural activity. Although several studies reported correlation between event-related potentials, mental ability and intelligence, there is a lack of information about time-frequency and spatio-temporal structures of neural activity which characterize this relation. In the present work, we analyzed human electroencephalograms (EEG) recorded during the performance of elementary cognitive tasks using the Schulte test, which is a paper-pencil based instrument for assessing elementary cognitive ability or mental speed. According to particular features found of the EEG structure, we divided the subjects into three groups. For subjects in each group, we applied the Sixteen Personality Factor Questionnaire (16PF) to assess the their personality traits. We demonstrated that each group exhibited a different score on the personality scale, such as warmth, reasoning, emotional stability and dominance. Summing up, we found a link between EEG features, mental abilities and personality traits. The obtained results can be of great interest for testing human personality to create automatized intelligent programs which combine simple tests and EEG measurements for real estimation of human personality traits and mental abilities

Visual perception affected by motivation and alertness controlled by a noninvasive brain-computer interface.

The influence of motivation and alertness on brain activity associated with visual perception was studied experimentally using the Necker cube, which ambiguity was controlled by the contrast of its ribs. The wavelet analysis of recorded multichannel electroencephalograms (EEG) allowed us to distinguish two different scenarios while the brain processed the ambiguous stimulus. The first scenario is characterized by a particular destruction of alpha rhythm (8-12 Hz) with a simultaneous increase in beta-wave activity (20-30 Hz), whereas in the second scenario, the beta rhythm is not well pronounced while the alpha-wave energy remains unchanged. The experiments were carried out with a group of financially motivated subjects and another group of unpaid volunteers. It was found that the first scenario occurred mainly in the motivated group. This can be explained by the increased alertness of the motivated subjects. The prevalence of the first scenario was also observed in a group of subjects to whom images with higher ambiguity were presented. We believe that the revealed scenarios can occur not only during the perception of bistable images, but also in other perceptual tasks requiring decision making. The obtained results may have important applications for monitoring and controlling human alertness in situations which need substantial attention. On the base of the obtained results we built a brain-computer interface to estimate and control the degree of alertness in real time

Brain-computer interface for estimation and control of alertness.

<p>(a) Schematical illustration of the experimental setup. (b) Control characteristics <i>G</i>_1,2,3(<i>t</i>) describing the degree of alertness during the processing of visual stimuli, obtained from three subjects of the group. The vertical dashed lines indicate the moments of time when the external disturbance (<i>t</i>_<i>EP</i>) was applied and the feedback message about the attention decrease (<i>t</i>_<i>FB</i>) was sent. The horizontal dash-dotted lines indicate the values of , , calculated by averaging <i>G</i>_1,2,3(<i>t</i>) over time intervals <i>t</i> < <i>t</i>_<i>EP</i>, <i>t</i>_<i>EP</i> > <i>t</i> > <i>t</i>_<i>FB</i> and <i>t</i> > <i>t</i>_<i>FB</i>. (c) Values of , and averaged over the group of eight subjects. The error bars indicate the standard deviation of these values among all participants.</p

Experimental observation.

<p>(a) The scheme of the electrode position the typical set of registered EEG traces. Different segments of the EEG recording are named I, II, III, which correspond, respectively, to the 1-sec time interval preceding the cube presentation (<i>before perception</i>), ∼ 1-sec interval of the cube observation (<i>perception</i>), and 1-sec interval after the cube observation (<i>after perception</i>) and (b) The values of (triangles) and (circles) illustrating the relation between the power of alpha and beta waves in intervals I and II obtained by the statistical analysis of the 40-min experimental session of each of the 10 subjects. The horizontal dashed lines indicate threshold values defining a > 40% decrease of alpha-activity (line 1) and a > 20% increase of beta-activity (line 2) used to identify different perception scenarios. The solid red boxes highlight the subjects (2,3,9) following the first scenario. Other subjects are associated with the second scenario. (c,d) 3-D histograms illustrating the distribution of the statistical measure <i>L</i>(<i>f</i>, <i>t</i>) calculated by <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188700#pone.0188700.e006" target="_blank">Eq (3)</a> which indicates the location of the maximal spectral component during the 40-min session for two subjects demonstrating the (c) first (subject #9) and (d) second (subject #7) perception scenarios.</p

Effect of motivation.

<p>(a) Colored areas containing dependencies of the percentage of type-1 events on the number of the cube presentations for participants belonging to GROUP I (motivated subjects) and GROUP II (unpaid volunteers). (b) Percentage of type-1 events averaged over participants belonging to GROUP I (left circle) and GROUP II (right circle). The error bars show the standard deviation for each group.</p

Examples of Necker cube images.

<p>The ambiguity of the Necker cube is controlled by contrast parameter <i>I</i>. The left-hand image with <i>I</i> = 0 corresponds to the fully left-oriented cube, whereas the right-hand image with <i>I</i> = 1 to the fully right-oriented cube. The middle image with <i>I</i> = 0.5 has the highest ambiguity.</p

Spectral properties of two different perception scenarios.

<p>Upper row: Typical EEG trials associated with perception of ambiguous images illustrating (a) first and (b) second perception scenarios. The colored solid lines show the temporal dependences of the dominant frequencies of the first and second maximal spectral components during the perception. The line color indicates the frequency band within which these spectral components occur at the current moment of time: green refers to delta band (1-4 Hz), red to alpha band (8-12 Hz), and purple to beta band (20-30 Hz). The double vertical lines limit the time interval of the button pressing. Lower row: Coefficients 〈<i>F</i>_{<i>α</i>,<i>β</i>,<i>δ</i>}〉 characterizing the location of the maximal spectral components averaged over all channels and time intervals Δ<i>τ</i>_I,II,III corresponding to different segments during perception for subjects belonging to (c) group 1 and (d) group 2. The error bars indicate standard deviations for each group. The horizontal bars with stars show significant differences in contributions of the alpha and beta components according to the statistical analysis using paired t-test.</p