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
Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition
TL;DR: It is shown that direct multichannel processing via MEMD allows for enhanced localization of the frequency information in EEG, and, in particular, its noise-assisted mode of operation (NA-MEMD) provides a highly localized time-frequency representation.
Journal ArticleDOI
A review on directional information in neural signals for brain-machine interfaces.
Stephan Waldert,Tobias Pistohl,Christoph Braun,Christoph Braun,Tonio Ball,Ad Aertsen,Carsten Mehring +6 more
TL;DR: Recent findings showing that analog neuronal population signals, ranging from intracortical local field potentials over epicortical ECoG to non-invasive EEG and MEG, can also be used to decode movement direction and continuous movement trajectories are reviewed.
Journal ArticleDOI
Neural interfaces for the brain and spinal cord--restoring motor function.
TL;DR: It is proposed that several known plasticity mechanisms, operating in a complementary manner, might underlie the therapeutic effects that are achieved by closing the loop between electronic devices and the nervous system.
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Transfer Learning for Brain–Computer Interfaces: A Euclidean Space Data Alignment Approach
He He,Dongrui Wu +1 more
TL;DR: Zhang et al. as discussed by the authors proposed an approach to align EEG data from different subjects in the Euclidean space to make them more similar, and hence improve the learning performance for a new subject.
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Machine-learning-based coadaptive calibration for brain-computer interfaces
TL;DR: Adaptive machine learning methods to eliminate offline calibration are investigated, the performance of 11 volunteers in a BCI based on the modulation of sensorimotor rhythms is analyzed, and an adaptation scheme that individually guides the user is presented.
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.