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The goal of this study was an administration of the navigation task in a three-dimensional virtual environment to localizethe electroencephalogram (EEG) features responsible for egocentric and allocentric reference frame processing inahorizontalandalsoinaverticalplane.We recordedtheEEGsignalofatraversethroughavirtual tunneltosearchforthebest signal features that discriminate between specific strategies in particular plane. We identified intrahemisphericcoherencesinoccipital–parietalandtemporal–parietalareasasthemostdiscriminativefeatures.Theyhave10%lowererrorrate compared to single electrode features adopted in previous studies. The behavioral analysis revealed that 11% ofparticipants switched from egocentric to allocentric strategy in a vertical plane, while 24% of participants consistentlyadopted egocentric strategy in both planes.Keywords: Reference frames, Spatial navigation, Electroencephalography, Virtual environmentIntroductionThe human capacity to represent space and to orient in an en-vironment is manifested in the ability to create mental images andto talk about the environment from different perspectives. Thisability is related to a navigation in various frames of reference. Inthe area of spatial cognition, the reference frame is considered as anorthogonal system with its origin (deixis center) in the retina, head,body or other points, objects, or array in space (Colby G Behrmann, 2000; McCloskey, 2001). In a series of psycho-logicalexperiments,researchersconcludedthatreferenceframesaresimultaneously activated (Carlson-Radvansky & Irwin, 1993). Theunanswered question is whether the simultaneous activation isexpressed as a different activity in a single cortical area or whetherdifferent reference frames activate several brain centers.The basic classification of reference frames in the area of neu-roscience involves spatial coordinate systems based on egocentricand allocentric orientation (Howard & Templeton, 1966). In theegocentric frame of reference, the position of objects is encodedwith a reference to the body of the observer or to the relevant bodyparts. Egocentric representations of objects may be used for goal-directedmovements, suchasreachingatargetoravoidingadanger-ousstimulus(Galatietal.,2000).Spatialpositionscanalsobecodedin object-centered coordinates that are independent from theobserver’s current position. This allocentric frame of reference isconstituted of object-to-object relations. Knowing the allocentricposition in the space, and having egocentric coordinates to otherobjects, navigators can form a map that allows object-to-objectrelations to be represented allocentrically (Klatzky, 1998).Neurophysiological research in humans confirmed differencesbetween the utilization of egocentric and allocentric frames ofreference. Committeri et al. (2004) attribute egocentric codingmainly to the dorsal stream (BA 7) and connected frontal areas,whereas allocentric coding is mediated by both dorsal and ventralregions.Finketal.(2003)attributeallocentricprocessingtotherightposterior parietal (BA 7) and ventral premotor cortex (BA 6).Wilson et al. (2005) reported greater activity in the left parietalcortex compared to the counterpart in the right hemisphere (hemi-spheric asymmetry) for the allocentric frame of reference.There are also theories that attribute parallel processing of theegocentric and allocentric information to the parietal lobe (BA 7) andthe hippocampal formation (BA 27, 2 8), respectively. Informationfrom the parietal lobe is transferred to the hippocampus for a long-term storage in allocentric coordinates (Kesner, 2000;SaveP Feigenbaum & Morris, 2004). Other studies have associated thehippocampus with allocentric strategies and the striatum with egocen-tric strategies (Maguire et al., 1998; Hartley et al., 2003; Iaria et al.,2003). The most recent study (Galati et al., 2010) provides a summaryof functional magnetic resonance imaging (fMRI) experiments withthe static stimuli. According to this study, the posterior parietal andassociated frontal regions are involved in processing the egocentricreference frame. The allocentric n avigation is attributed to the specificparietal subregions and also to the hippocampal and retrosplenialregion.On the other hand, the way of stimuli presentation in thesestudies should be considered as insufficient (Gramann et al., 2006).Theexperimentsmentionedabovearebasedonastaticpresentationof stimuli (there are various forms of the line bisection paradigm)

In our contribution, we investigate differences in EEG signal between mentally gifted and average gifted adolescents during the mental rotation task. We focus mainly on the activation of individual lobes and hemispheres. EEG signal preprocessing was done and various feature selection and classification methods were compared. We visualize the power of frequency spectra of alpha, beta and gamma to show the active areas of gifted and average gifted adolescents and we compare our results with findings from previous studies.

SCOPUS: ed.j, info:eu-repo/semantics/published

In this paper we propose a novel method for the EEG signal processing based on the classification of independent components of the signal features. ICA algorithm has been successfully applied to the area of EEG artefact detection, however this algorithm can be applied to identify independent components of signal features. We decomposed the EEG signal to the descriptive features, calculated independent components for specific features, linked them to the appropriate electrodes and classified these feature components by several algorithms. This method was applied to the data from a psychological experiment focused on the adoption of a specific frame of reference within the spatial navigation. The results were compared with the widely adopted method of signal feature classification. The feature components method revealed the brain structures involved in the spatial navigation similar to the results of recent EEG and fMRI studies.

The paper proposes a method for the classification of EEG signal based on machine learning methods. We analyzed the data from an EEG experiment consisting of affective picture stimuli presentation, and tested automatic recognition of the individual emotional states from the EEG signal using Bayes classifier. The mean accuracy was about 75 percent, but we were not able to select universal features for classification of all subjects, because of inter-individual differences in the signal. We also identified correlation between the classification error and the extroversion-introversion personality trait measured by EPQ-R test. Introverts have lower excitation threshold so we are able to detect the differences in their EEG activity with better accuracy. Furthermore, the use of Kohonen's self-organizing map for visualization is suggested and demonstrated on one subject.

In the paper we describe current results of our ongoing research, concerning the navigation in the virtual tunnel task and its EEG correlates. We searched for the features in the EEG signal to discriminate the employment of the allocentric and the egocentric reference frames. These two reference frames differ in the center of deixis (the origin of the coordinating system). Our sample comprised groups that tend to solve the task by adopting one of the mentioned reference frames. We decomposed the EEG signal to the basic features and used this data as the input for the neural networks. The classification task is to select the best features to discriminate between these reference frames. The result was congruent with the similar study (Gramann et al., 2006) in the Brodmann area 7 differences, but we also detected other brain areas involved in this task.

The mental rotation task (Shepard and Metzler, 1971) is a standard cognitive task and a huge amount of research has been done comparing reaction times and error rates between different groups of people. O’Boyle et al. (2005) in their neuroimaging study demonstrated that mathematically gifted male adolescents engage different brain structures than those average gifted when performing 3D mental rotation. The purpose of this study was to observe differences between gifted and average adolescents during both 2D and 3D mental rotation task. We recorded and analyzed 16 adolescents (10 boys, 6 girls) with IQ > 130 and 10 adolescents with average IQ (〈90, 110〉), all 15–18 years old. From the subsequent EEG signal analysis we were able to identify brain structures that discriminates between mathematically gifted and average ability. Together with the EEG signal we measured reaction times and error rates and evaluated them. The significant difference between experimental groups was detected in both reaction times and error rates. After finishing the experiment subjects performed Raven intelligence test and filled out psychological questionnaires (autistic, systematizing and empathizing quotient) and questionnaire with factual, self-evaluation questions and questions about used strategy. Finally, the values from questionnaires were correlated with the EEG signal. We acknowledge the support of SGS grant “New approaches to collection, evaluation and utilization of biological data II”, No. SGS13/203/OHK3/3T/13.

Recent methods of emotional states detection are mostly based on low-density EEG recording and analysis of the scalp activity. This disadvantage should result in the low classification accuracy of emotions. We extend the research in this area to high-density EEG recordings (118 electrodes) and source localization methods. Our experiment consisted of 100 affective pictures (mostly IAPS dataset) standing for three different emotional categories (positive, neutral and negative). They were presented to 30 participants and the EEG signal was recorded. The spectral bands were calculated for scalp projection and further ICA decomposition and dipole fitting was applied for source localization. We analyzed classification errors for specific emotional categories. The goal of our study was to identify differences between low-density and high-density density EEG recordings. We were interested in the difference between scalp maps and the localized sources of the EEG signal. These differences should clarify possible sources of classification errors. We also analyzed whether differences in the spectral activity of specific emotions on the scalp correlates with the source signals. The advantages and disadvantages of specific methods are discussed.