7,122 research outputs found
Act quickly, decide later: long latency visual processing underlies perceptual decisions but not reflexive behavior
Jolij J, Scholte H, Van Gaal S, Hodgson TL, Lamme VAF (2011) Act quickly, decide later: Long latency visual processing underlies perceptual decisions but not reflexive behavior. Journal of Cognitive Neuroscience 23(12), p 3734-3745
Single-trial analysis of EEG during rapid visual discrimination: enabling cortically-coupled computer vision
We describe our work using linear discrimination of multi-channel electroencephalography
for single-trial detection of neural signatures of visual recognition events. We demonstrate
the approach as a methodology for relating neural variability to response variability, describing
studies for response accuracy and response latency during visual target detection.
We then show how the approach can be utilized to construct a novel type of brain-computer
interface, which we term cortically-coupled computer vision. In this application, a large
database of images is triaged using the detected neural signatures. We show how ‘corticaltriaging’
improves image search over a strictly behavioral response
Induced gamma-band activity is related to the time point of object identification
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Neural correlates of subjective timing precision and confidence
Humans perceptual judgments are imprecise, as repeated exposures to the same physical stimulation (e.g. audio-visual inputs separated by a constant temporal offset) can result in different decisions. Moreover, there can be marked individual differences – precise judges will repeatedly make the same decision about a given input, whereas imprecise judges will make different decisions. The causes are unclear. We examined this using audio-visual (AV) timing and confidence judgments, in conjunction with electroencephalography (EEG) and multivariate pattern classification analyses. One plausible cause of differences in timing precision is that it scales with variance in the dynamics of evoked brain activity. Another possibility is that equally reliable patterns of brain activity are evoked, but there are systematic differences that scale with precision. Trial-by-trial decoding of input timings from brain activity suggested precision differences may not result from variable dynamics. Instead, precision was associated with evoked responses that were exaggerated (more different from baseline) ~300 ms after initial physical stimulations. We suggest excitatory and inhibitory interactions within a winner-take-all neural code for AV timing might exaggerate responses, such that evoked response magnitudes post-stimulation scale with encoding success
Data-driven analysis of simultaneous EEG/fMRI using an ICA approach
Due to its millisecond-scale temporal resolution, EEG allows to assess neural correlates with precisely defined temporal relationship relative to a given event. This knowledge is generally lacking in data from functional magnetic resonance imaging (fMRI) which has a temporal resolution on the scale of seconds so that possibilities to combine the two modalities are sought. Previous applications combining event-related potentials (ERPs) with simultaneous fMRI BOLD generally aimed at measuring known ERP components in single trials and correlate the resulting time series with the fMRI BOLD signal. While it is a valuable first step, this procedure cannot guarantee that variability of the chosen ERP component is specific for the targeted neurophysiological process on the group and single subject level. Here we introduce a newly developed data-driven analysis procedure that automatically selects task-specific electrophysiological independent components (ICs). We used single-trial simultaneous EEG/fMRI analysis of a visual Go/Nogo task to assess inhibition-related EEG components, their trial-to-trial amplitude variability, and the relationship between this variability and the fMRI. Single-trial EEG/fMRI analysis within a subgroup of 22 participants revealed positive correlations of fMRI BOLD signal with EEG-derived regressors in fronto-striatal regions which were more pronounced in an early compared to a late phase of task execution. In sum, selecting Nogo-related ICs in an automated, single subject procedure reveals fMRI-BOLD responses correlated to different phases of task execution. Furthermore, to illustrate utility and generalizability of the method beyond detecting the presence or absence of reliable inhibitory components in the EEG, we show that the IC selection can be extended to other events in the same dataset, e.g., the visual responses
Coding of visual object features and feature conjunctions in the human brain
Peer reviewedPublisher PD
Detecting single-trial EEG evoked potential using a wavelet domain linear mixed model: application to error potentials classification
Objective. The main goal of this work is to develop a model for multi-sensor
signals such as MEG or EEG signals, that accounts for the inter-trial
variability, suitable for corresponding binary classification problems. An
important constraint is that the model be simple enough to handle small size
and unbalanced datasets, as often encountered in BCI type experiments.
Approach. The method involves linear mixed effects statistical model, wavelet
transform and spatial filtering, and aims at the characterization of localized
discriminant features in multi-sensor signals. After discrete wavelet transform
and spatial filtering, a projection onto the relevant wavelet and spatial
channels subspaces is used for dimension reduction. The projected signals are
then decomposed as the sum of a signal of interest (i.e. discriminant) and
background noise, using a very simple Gaussian linear mixed model. Main
results. Thanks to the simplicity of the model, the corresponding parameter
estimation problem is simplified. Robust estimates of class-covariance matrices
are obtained from small sample sizes and an effective Bayes plug-in classifier
is derived. The approach is applied to the detection of error potentials in
multichannel EEG data, in a very unbalanced situation (detection of rare
events). Classification results prove the relevance of the proposed approach in
such a context. Significance. The combination of linear mixed model, wavelet
transform and spatial filtering for EEG classification is, to the best of our
knowledge, an original approach, which is proven to be effective. This paper
improves on earlier results on similar problems, and the three main ingredients
all play an important role
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