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Thorsten Dickhaus

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3 papers

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YNIMG Journal 2011 Journal Article

Introduction to machine learning for brain imaging

Steven Lemm
Benjamin Blankertz
Thorsten Dickhaus
Klaus-Robert Müller

Machine learning and pattern recognition algorithms have in the past years developed to become a working horse in brain imaging and the computational neurosciences, as they are instrumental for mining vast amounts of neural data of ever increasing measurement precision and detecting minuscule signals from an overwhelming noise floor. They provide the means to decode and characterize task relevant brain states and to distinguish them from non-informative brain signals. While undoubtedly this machinery has helped to gain novel biological insights, it also holds the danger of potential unintentional abuse. Ideally machine learning techniques should be usable for any non-expert, however, unfortunately they are typically not. Overfitting and other pitfalls may occur and lead to spurious and nonsensical interpretation. The goal of this review is therefore to provide an accessible and clear introduction to the strengths and also the inherent dangers of machine learning usage in the neurosciences.

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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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