Machine learning for real-time single-trial EEG-analysis: from brain-computer interfacing to mental state monitoring.

By K. Müller, Michael W. Tangermann, Guido Dornhege, Matthias Krauledat, Gabriel Curio, Benjamin Blankertz. Journal of Neuroscience Methods, 2008

506 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/114b386477a9303cc61d871a7c1c34a5cd09d148

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In Cognitive Neuroscience

Citations in the corpus

Targeting EEG/LFP Synchrony with Neural Nets (2017, background): “There is signiﬁcant current research on methods for Electroencephalography (EEG) and Local Field Potential (LFP) data in a variety of applications, such as Brain-Machine Interfaces (BCIs) [20], seizure detection [23, 25], and fundamental research in ﬁelds such as psychiatry [10].”
Introduction to machine learning for brain imaging (2011, background): “…et al., 2004; Blankertz et al., 2006a, 2008b; Müller et al., 2008; Tomioka et al., 2007; Tomioka and Müller, 2010), (c) spatio-temporal ﬁ ltering algorithms (e.g. Fukunaga, 1972; Koles, 1991; Ramoser et al., 2000; Lemm et al., 2005; Blankertzetal.,2008b;Dornhegeetal.,2006,2007;TomiokaandMüller,…”
The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology (2010, methodology): “For a more detailed physiological analysis please refer to the original paper (Müller et al., 2008).”
Brain-Computer Interfaces for Communication and Control (2011, unknown): “31 The Mu rhythm systems made use of machine-learning paradigms that minimized training, with users of both mu-based systems reportedly averaging between 2.3–7 characters/ minute 22 and 2.85–3.38 characters/ minute.”
EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy (2019, unknown): “3A and 3B).”
Single-trial analysis and classification of ERP components - A tutorial (2011, unknown): “This branch of research is strongly influenced by the development of an effective communication interface connecting the human brain and a computer ([19, 33, 35, 76, 5, 52, 16, 77, 34]), which finally also attracted the machine learning community to the field [6, 69, 27, 47, 11, 50, 7, 8, 74, 49, 67”
Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data (2016, methodology): “Although decoding methods for time series neuroimaging data such as MEG/EEG have been extensively applied in brain– computer interfaces (BCI; Müller et al., 2008; Curran & Stokes, 2003; Wolpaw, Birbaumer, McFarland, Pfurtscheller, & Vaughan, 2002; Kübler, Kotchoubey, Kaiser, Wolpaw, & Birbaumer, 200”
Towards a Cure for BCI Illiteracy (2009, methodology): “Machine Learning based BCIs use EEG features of larger complexity that can be fitted better to the individual characteristics of brain patterns of each user (see Blankertz et al. 2007, 2008b; Dornhege et al. 2004, 2007; Müller et al. 2003, 2008).”
Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces (2011, methodology): “Most of the effective solutions use machine learning models [5], [6], [7], [8].”