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
Towards optimum linear transformation under zero-mean Gaussian mixtures for detection of motor imagery EEG
TL;DR: This paper formulate optimum transformation under mixture of zero-mean Gaussian conditions as a Bhattacharyya error bound minimization problem, and derive a numerical solution to estimate the bound from training samples, and develops an algorithm for selecting optimum linear transformation.
DissertationDOI
Brain-machine interface using electrocorticography in humans
TL;DR: This paper is intended to serve as a “roadmap” for future efforts to improve upon the current state of knowledge and understanding of earthquake-triggered landsliding and liquefaction.
Journal ArticleDOI
Sequential Probability Ratio Testing with Power Projective Base Method Improves Decision-Making for BCI.
TL;DR: The proposed SPRT method provides an explicit relationship between stopping time, thresholds, and error, which is important for balancing the time-accuracy trade-off, and suggest SPRT would be useful in speeding up decision-making while trading off errors in BCI.
Proceedings ArticleDOI
Multiclass Classification of Brain-Computer Interface Motor Imagery System: A Systematic Literature Review
TL;DR: In this article, the authors conducted a systematic literature review for 30 articles that have gone through the selection process and found that the most used dataset in Multiclass BCI MI-EEG System is BCI Competition IV dataset 2a.
Proceedings ArticleDOI
A Subject-Independent Brain-Computer Interface Framework Based on Supervised Autoencoder
TL;DR: In this article , a subject-independent MI-BCI based on a supervised autoencoder (SAE) was proposed to circumvent the calibration phase, which is validated on dataset 2a from BCI competition IV.
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.