Introduction to machine learning for brain imaging

By Steven Lemm, Benjamin Blankertz, Thorsten Dickhaus, K. Müller. 2011

659 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Molecular Biology

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Citations in the corpus

Methods for interpreting and understanding deep neural networks (2017, background): “ of these individual EEG brain maps are given in Figure 10(b). For classical linear explanation of neural activation patterns in cognitive brain science experiments or Brain Computer Interfacing, see [13, 30, 12, 22]. Deep neural networks have also been proposed to make sense of the human genome. Al”
Understanding Machine-learned Density Functionals (2014, background): “Then and only then, the generalization ability of the model can be assessed with the test data ([31, 36], see also [55]).”
Towards Explainable Artificial Intelligence (2019, background): “In the case of scientific data, they can be unknown associations between genes and diseases [51], chemical compounds and material properties [68] or brain activations and cognitive states [49].”
Brain Computer Interfaces, a Review (2012, background): “Bias and variance of the estimated error of the algorithms, and their overfitting are the main source of difficulties [224].”
eDoctor: machine learning and the future of medicine (2018, background): “…common concepts and pitfalls that the reader should familiarize themselves with before embarking on any data analysis and Deo’s article in Circulation further expands on this and the importance of intelligent features or variable selection and the various pitfalls of improper data input [85, 86].”
Reliable estimation of prediction errors for QSAR models under model uncertainty using double cross-validation (2014, positive): “This in turn renders possible to estimate the prediction error unbiasedly [11].”
Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data (2016, methodology): “Although introductions to BCI exist (Blankertz, Lemm, Treder, Haufe, & Müller, 2011; Lemm, Blankertz, Dickhaus, & Müller, 2011), the aims of time series decoding for cognitive neuroscience are distinct from those that drive the application of these methods in BCI, thus requiring a targeted…”
BCILAB: a platform for brain–computer interface development (2013, methodology): “Note: the trials available for testing in A.2 were interleaved with the training data, likely producing somewhat optimistic results (see also [25]).”
Machine Learning in Major Depression: from Classification to Treatment Outcome Prediction. (2018, methodology): “30 The new data have to be preprocessed in the same way as the training data, and the same feature reduction method is applied with optimized parameters obtained from the training phase.”