Media 1: See-through integral imaging display using a resolution and fill factor-enhanced lens-array holographic optical element

Originally published in Optics Express on 17 November 2014 (oe-22-23-27958

Comparison of initial feature masks for decoding.

<p>(a) The initial feature masks from statistical parametric maps generated from all trials (total 80 trials, colored in red and yellow) and all trials except for a test trial (total 79 trials, colored in green and yellow) of a participant were compared. The initial feature masks show 95% overlaps (in yellow color). (b) The initial feature masks derived from the statistical parametric maps of union and difference between voluntary experience and passive experience (VE & PE and VE − PE) in an individual participant are displayed. Red-yellow color indicates increased activation in memory retrieval of a voluntary experience than in a passive experience, while reverse for blue colors.</p

Classification accuracy of the sparse MVB compared to other classification methods.

<p>(a) In the MVB model-based classification for single trial, the two-models approach showed significantly higher accuracy than the single-model approach (p < 0.001). (b) Comparison results of MVB model-based classification performance compared to GLM and SVM for single, two and three trials are displayed. The classification accuracy of all the methods are statistically higher than the chance level of 0.5 after one sample t-tests (p < 0.05). The proposed MVB (VE & PE) method with a feature mask containing both voluntary experience (VE) and passive experience (PE) showed greater classification accuracy for single and multiple trials than the classification method based on GLM (T-weighted), SVM (the parameter C = 1 over the feature mask VE & PE), and MVB (VE—PE, over the feature mask in the contrast between voluntary experience versus passive experience).</p

Detecting brain states using multivariate Bayesian inversion scheme.

<p>(a) A general overview for decoding. (b) An example of application in a rapid event related design to models with different design matrices X_A (assigning 1 for the regressor A and 0 for the regressor B at the target stimulus) and X_B (assigning 0 for the regressor A and 1 for the regressor B at the target stimulus) assuming the unknown target class as class A and class B, respectively. The class for the unknown stimulus was chosen by selecting the model with higher free-energy F among models with different design matrices X_A and X_B.</p

Summary of individual features and accuracies of MVB optimization according to the number of trials.

<p>Summary of individual features and accuracies of MVB optimization according to the number of trials.</p

Effects of interstimulus interval on the free energy approximation.

<p>(a) The Free energy approximation had a significant positive correlation with interstimulus intervals between onset times of present and next stimulus (POST-ISI) (r = 0.2277, p = 0.0423). (b) There was a tendency toward positive correlation between the Free energy approximation and the total interstimulus interval (TOTAL-ISI) (r = 0.2169, p = 0.0532).</p

An illustration for overlapping effects of hemodynamic responses on the spatial patterns.

<p>(a) The illustration is based on the rapid event related design with three classes of events, with short intervals between events. (b) Each stimulus event elicits a class-specific hemodynamic response. (c) Due to a long hemodynamic response for a neural event, overlapped hemodynamic responses of preceding events are generally observed at each time point in the rapid event-related design. (d, e) The intrinsic neural responses can construct class-specific spatial patterns for each event, (f) whereas the overlapped responses contaminated event-specific spatial patterns.</p

Media 1: Real-time capturing and 3D visualization method based on integral imaging

Originally published in Optics Express on 12 August 2013 (oe-21-16-18742

Exemplary display of distributed sparse feature maps used to decode voluntary and passive visual stimuli in two participants.

<p>The feature maps in (a) and (b) were generated based on the voxel weights from two MVB models. In these examples, model parameters (voxel weights) estimated by assuming the unknown target stimuli as either voluntary experience class (VE→VE) or passive experience class (VE→PE) are displayed, with red colors for positive weights and blue colors for negative weights. The size of spheres indicates the strength of weights. The histograms of the voxel weights (features) show small non-zero values showing sparsity.</p

Media 3: Real-time capturing and 3D visualization method based on integral imaging

Originally published in Optics Express on 12 August 2013 (oe-21-16-18742