Dataset Information

Measuring Integration Processes in Visual Symmetry with Frequency-Tagged EEG.

ABSTRACT: Symmetry is a highly salient feature of the natural world which requires integration of visual features over space. The aim of the current work is to isolate dynamic neural correlates of symmetry-specific integration processes. We measured steady-state visual evoked potentials (SSVEP) as participants viewed symmetric patterns comprised of distinct spatial regions presented at two different frequencies (f1 and f2). We measured intermodulation components, shown to reflect non-linear processing at the neural level, indicating integration of spatially separated parts of the pattern. We generated a wallpaper pattern containing two reflection symmetry axes by tiling the plane with a two-fold reflection symmetric unit-pattern and split each unit-pattern diagonally into separate parts which could be presented at different frequencies. We compared SSVEPs measured for wallpapers and control patterns for which both images were equal in terms of translation and rotation symmetry but reflection symmetry could only emerge for the wallpaper pattern through integration of the image-pairs. We found that low-frequency intermodulation components differed between the wallpaper and control stimuli, indicating the presence of integration mechanisms specific to reflection symmetry. These results showed that spatial integration specific to symmetry perception can be isolated through a combination of stimulus design and the frequency tagging approach.

SUBMITTER: Alp N

PROVIDER: S-EPMC5934372 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Measuring Integration Processes in Visual Symmetry with Frequency-Tagged EEG.

Alp Nihan N Kohler Peter Jes PJ Kogo Naoki N Wagemans Johan J Norcia Anthony Matthew AM

Scientific reports 20180503 1

Symmetry is a highly salient feature of the natural world which requires integration of visual features over space. The aim of the current work is to isolate dynamic neural correlates of symmetry-specific integration processes. We measured steady-state visual evoked potentials (SSVEP) as participants viewed symmetric patterns comprised of distinct spatial regions presented at two different frequencies (f<sub>1</sub> and f<sub>2</sub>). We measured intermodulation components, shown to reflect non ...[more]

PMID: 29725022

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Project description:We studied the capability of a Hybrid functional neuroimaging technique to quantify human mental workload (MWL). We have used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) as imaging modalities with 17 healthy subjects performing the letter n-back task, a standard experimental paradigm related to working memory (WM). The level of MWL was parametrically changed by variation of n from 0 to 3. Nineteen EEG channels were covering the whole-head and 19 fNIRS channels were located on the forehead to cover the most dominant brain region involved in WM. Grand block averaging of recorded signals revealed specific behaviors of oxygenated-hemoglobin level during changes in the level of MWL. A machine learning approach has been utilized for detection of the level of MWL. We extracted different features from EEG, fNIRS, and EEG+fNIRS signals as the biomarkers of MWL and fed them to a linear support vector machine (SVM) as train and test sets. These features were selected based on their sensitivity to the changes in the level of MWL according to the literature. We introduced a new category of features within fNIRS and EEG+fNIRS systems. In addition, the performance level of each feature category was systematically assessed. We also assessed the effect of number of features and window size in classification performance. SVM classifier used in order to discriminate between different combinations of cognitive states from binary- and multi-class states. In addition to the cross-validated performance level of the classifier other metrics such as sensitivity, specificity, and predictive values were calculated for a comprehensive assessment of the classification system. The Hybrid (EEG+fNIRS) system had an accuracy that was significantly higher than that of either EEG or fNIRS. Our results suggest that EEG+fNIRS features combined with a classifier are capable of robustly discriminating among various levels of MWL. Results suggest that EEG+fNIRS should be preferred to only EEG or fNIRS, in developing passive BCIs and other applications which need to monitor users' MWL.

Dataset Information

Measuring Integration Processes in Visual Symmetry with Frequency-Tagged EEG.

Publications

Measuring Integration Processes in Visual Symmetry with Frequency-Tagged EEG.

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