Introduction to machine learning for brain imaging
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 …
become a working horse in brain imaging and the computational neurosciences, as they are …
Optimizing spatial filters for robust EEG single-trial analysis
B Blankertz, R Tomioka, S Lemm… - IEEE Signal …, 2007 - ieeexplore.ieee.org
Due to the volume conduction multichannel electroencephalogram (EEG) recordings give a
rather blurred image of brain activity. Therefore spatial filters are extremely useful in single …
rather blurred image of brain activity. Therefore spatial filters are extremely useful in single …
Correlated components of ongoing EEG point to emotionally laden attention–a possible marker of engagement?
JP Dmochowski, P Sajda, J Dias… - Frontiers in human …, 2012 - frontiersin.org
Recent evidence from functional magnetic resonance imaging suggests that cortical
hemodynamic responses coincide in different subjects experiencing a common naturalistic …
hemodynamic responses coincide in different subjects experiencing a common naturalistic …
Recipes for the linear analysis of EEG
In this paper, we describe a simple set of “recipes” for the analysis of high spatial density
EEG. We focus on a linear integration of multiple channels for extracting individual …
EEG. We focus on a linear integration of multiple channels for extracting individual …
Machine learning for detection and diagnosis of disease
P Sajda - Annu. Rev. Biomed. Eng., 2006 - annualreviews.org
Machine learning offers a principled approach for developing sophisticated, automatic, and
objective algorithms for analysis of high-dimensional and multimodal biomedical data. This …
objective algorithms for analysis of high-dimensional and multimodal biomedical data. This …
EEG in the classroom: Synchronised neural recordings during video presentation
We performed simultaneous recordings of electroencephalography (EEG) from multiple
students in a classroom, and measured the inter-subject correlation (ISC) of activity evoked …
students in a classroom, and measured the inter-subject correlation (ISC) of activity evoked …
Multiclass common spatial patterns and information theoretic feature extraction
M Grosse-Wentrup, M Buss - IEEE transactions on Biomedical …, 2008 - ieeexplore.ieee.org
We address two shortcomings of the common spatial patterns (CSP) algorithm for spatial
filtering in the context of brain-computer interfaces (BCIs) based on electroencephalography …
filtering in the context of brain-computer interfaces (BCIs) based on electroencephalography …
[HTML][HTML] Attention strongly modulates reliability of neural responses to naturalistic narrative stimuli
Attentional engagement is a major determinant of how effectively we gather information
through our senses. Alongside the sheer growth in the amount and variety of information …
through our senses. Alongside the sheer growth in the amount and variety of information …
No relationship between frontal alpha asymmetry and depressive disorders in a multiverse analysis of five studies
For decades, the frontal alpha asymmetry (FAA)–a disproportion in EEG alpha oscillations
power between right and left frontal channels–has been one of the most popular measures …
power between right and left frontal channels–has been one of the most popular measures …
[HTML][HTML] A tutorial on generalized eigendecomposition for denoising, contrast enhancement, and dimension reduction in multichannel electrophysiology
MX Cohen - Neuroimage, 2022 - Elsevier
The goal of this paper is to present a theoretical and practical introduction to generalized
eigendecomposition (GED), which is a robust and flexible framework used for dimension …
eigendecomposition (GED), which is a robust and flexible framework used for dimension …