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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Proceedings ArticleDOI
BCI control system for humanoid robot based on motor imaginary
TL;DR: The paper successfully completes the control of NAO robot through the EEG signals of motion imagination, and compares the result with that of manual control, and proves the effectiveness of the method of the system.
Journal ArticleDOI
Explorative data analysis for changes in neural activity.
TL;DR: A novel algorithm is proposed for disentangling such different causes of non-stationarity and in this manner enable better neurophysiological interpretation for a wider set of experimental paradigms.
Journal ArticleDOI
Single trial variability in brain–computer interfaces based on motor imagery: Learning in the presence of labeling noise
TL;DR: The two‐step procedure presented in this article is shown to significantly outperform a comparative naive approach and allows the identification and quantification of labeling noise in brain–computer interfaces (BCIs) based on motor imagery.
Proceedings ArticleDOI
Tensor Discriminant Analysis for MI-EEG Signal Classification Using Convolutional Neural Network
TL;DR: This paper proposes a new framework by introducing a tensor-based feature representation of the data and also utilizing a convolutional neural network architecture for performing classification of MI-EEG signal and demonstrates that the framework can further improve classification performance and has great potential for the practical application of BCI.
Journal ArticleDOI
Design and development of BCI for online acquisition, monitoring and digital processing of EEG waveforms
Shweta Singh,Dipali Bansal +1 more
TL;DR: Details of a simple and robust mechanism for online acquisition and real time processing of EEG signals in MATLAB without any loss of information, using custom developed API are given.
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.