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- W1976845187 abstract "Multivariate pattern classification methods are increasingly applied to neuroimaging data in the context of both fundamental research and in brain-computer interfacing approaches. Such methods provide a framework for interpreting measurements made at the single-trial level with respect to a set of two or more distinct mental states. Here, we define an approach in which the output of a binary classifier trained on data from an auditory mismatch paradigm can be used for online tracking of perception and as a neurofeedback signal. The auditory mismatch paradigm is known to induce distinct perceptual states related to the presentation of high- and low-probability stimuli, which are reflected in event-related potential (ERP) components such as the mismatch negativity (MMN). In the first part of the paper, we illustrate how pattern classification methods can be applied to data collected in an MMN paradigm, including discussion of the optimization of preprocessing steps, the interpretation of features and how the performance of these methods generalizes across individual participants and measurement sessions. We then go on to show that the output of these decoding methods can be used in online settings as a continuous index of single-trial brain activation underlying perceptual discrimination. We conclude by discussing several potential domains of application, including neurofeedback, cognitive monitoring and passive brain-computer interfaces." @default.
- W1976845187 created "2016-06-24" @default.
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- W1976845187 date "2013-01-01" @default.
- W1976845187 modified "2023-10-14" @default.
- W1976845187 title "Decoding of single-trial auditory mismatch responses for online perceptual monitoring and neurofeedback" @default.
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- W1976845187 doi "https://doi.org/10.3389/fnins.2013.00265" @default.
- W1976845187 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3874475" @default.
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