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Claudia Sannelli

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3 papers

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YNIMG Journal 2015 Journal Article

Extracting latent brain states — Towards true labels in cognitive neuroscience experiments

Anne K. Porbadnigk
Nico Görnitz
Claudia Sannelli
Alexander Binder
Mikio Braun
Marius Kloft
Klaus-Robert Müller

Neuroscientific data is typically analyzed based on the behavioral response of the participant. However, the errors made may or may not be in line with the neural processing. In particular in experiments with time pressure or studies where the threshold of perception is measured, the error distribution deviates from uniformity due to the structure in the underlying experimental set-up. When we base our analysis on the behavioral labels as usually done, then we ignore this problem of systematic and structured (non-uniform) label noise and are likely to arrive at wrong conclusions in our data analysis. This paper contributes a remedy to this important scenario: we present a novel approach for a) measuring label noise and b) removing structured label noise. We demonstrate its usefulness for EEG data analysis using a standard d2 test for visual attention (N=20 participants).

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YNIMG Journal 2011 Journal Article

Large-scale EEG/MEG source localization with spatial flexibility

Stefan Haufe
Ryota Tomioka
Thorsten Dickhaus
Claudia Sannelli
Benjamin Blankertz
Guido Nolte
Klaus-Robert Müller

We propose a novel approach to solving the electro-/magnetoencephalographic (EEG/MEG) inverse problem which is based upon a decomposition of the current density into a small number of spatial basis fields. It is designed to recover multiple sources of possibly different extent and depth, while being invariant with respect to phase angles and rotations of the coordinate system. We demonstrate the method's ability to reconstruct simulated sources of random shape and show that the accuracy of the recovered sources can be increased, when interrelated field patterns are co-localized. Technically, this leads to large-scale mathematical problems, which are solved using recent advances in convex optimization. We apply our method for localizing brain areas involved in different types of motor imagery using real data from Brain–Computer Interface (BCI) sessions. Our approach based on single-trial localization of complex Fourier coefficients yields class-specific focal sources in the sensorimotor cortices.

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YNIMG Journal 2010 Journal Article

Neurophysiological predictor of SMR-based BCI performance

Benjamin Blankertz
Claudia Sannelli
Sebastian Halder
Eva M. Hammer
Andrea Kübler
Klaus-Robert Müller
Gabriel Curio
Thorsten Dickhaus

Brain–computer interfaces (BCIs) allow a user to control a computer application by brain activity as measured, e. g. , by electroencephalography (EEG). After about 30years of BCI research, the success of control that is achieved by means of a BCI system still greatly varies between subjects. For about 20% of potential users the obtained accuracy does not reach the level criterion, meaning that BCI control is not accurate enough to control an application. The determination of factors that may serve to predict BCI performance, and the development of methods to quantify a predictor value from psychological and/or physiological data serve two purposes: a better understanding of the ‘BCI-illiteracy phenomenon’, and avoidance of a costly and eventually frustrating training procedure for participants who might not obtain BCI control. Furthermore, such predictors may lead to approaches to antagonize BCI illiteracy. Here, we propose a neurophysiological predictor of BCI performance which can be determined from a two minute recording of a ‘relax with eyes open’ condition using two Laplacian EEG channels. A correlation of r =0. 53 between the proposed predictor and BCI feedback performance was obtained on a large data base with N =80 BCI-naive participants in their first session with the Berlin brain–computer interface (BBCI) system which operates on modulations of sensory motor rhythms (SMRs).

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