Journal ArticleDOI
The non-invasive Berlin Brain-Computer Interface: fast acquisition of effective performance in untrained subjects.
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TLDR
It is proposed that the key to quick efficiency in the BBCI system is its flexibility due to complex but physiologically meaningful features and its adaptivity which respects the enormous inter-subject variability.About:
This article is published in NeuroImage.The article was published on 2007-08-15. It has received 865 citations till now. The article focuses on the topics: Brain–computer interface.read more
Citations
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Journal ArticleDOI
Learning from feedback training data at a self-paced brain–computer interface
TL;DR: This paper proposes a new supervised method that learns from a feedback session to construct a more appropriate feature space, on the basis of the maximum mutual information principle between feedback signal, target signal and EEG, and forms the learning objective as maximizing a kernel-based mutual information estimate with respect to the spatial-spectral filtering parameters.
Book ChapterDOI
Brain Computer Interface Enhancement by Independent Component Analysis
TL;DR: It is shown that independent components corresponding to the \(\mu \)-rhythm allow for higher classification accuracy comparing to raw EEG recordings usage.
Dissertation
Multi-objective particle swarm optimisation : methods and applications
TL;DR: The presented research brings together recent advances in the eld of multi-objective optimisation and particle swarm optimisation raising several challenges including the proposal of new archiving techniques to developing new methods and quality measures.
Breaking down the barriers to operator workload estimation: Advancing algorithmic handling of temporal non stationarity and cross participant differences for EEG analysis using deep learning
TL;DR: This research focuses on two barriers: Temporal non-stationarity in feature-to-target mappings when using EEG data, and the myriad of individual differences which lead to cross-participant applicability challenges.
Proceedings ArticleDOI
Common Tensor Discriminant Analysis for human brainwave recognition accelerated by massive parallelism
TL;DR: A massively parallel implementation of Common Tensor Discriminant Analysis is presented with applications to human brainwave pattern recognition, and is shown to be 11.49x faster than the original MATLAB version.
References
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Book
Adaptive Filter Theory
TL;DR: In this paper, the authors propose a recursive least square adaptive filter (RLF) based on the Kalman filter, which is used as the unifying base for RLS Filters.
Book
Introduction to Statistical Pattern Recognition
TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.
Journal ArticleDOI
Brain-computer interfaces for communication and control.
Jonathan R. Wolpaw,Jonathan R. Wolpaw,Niels Birbaumer,Niels Birbaumer,Dennis J. McFarland,Gert Pfurtscheller,Theresa M. Vaughan +6 more
TL;DR: With adequate recognition and effective engagement of all issues, BCI systems could eventually provide an important new communication and control option for those with motor disabilities and might also give those without disabilities a supplementary control channel or a control channel useful in special circumstances.
Journal ArticleDOI
Event-related EEG/MEG synchronization and desynchronization: basic principles.
TL;DR: Quantification of ERD/ERS in time and space is demonstrated on data from a number of movement experiments, whereby either the same or different locations on the scalp can display ERD and ERS simultaneously.