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
Low Quality Samples Detection in Motor Imagery EEG Data by Combining Independent Component Analysis and Confident Learning
TL;DR: In this article , the feasibility and performance of confident learning (CL) for detecting low-quality samples in motor imagery EEG (MI-EEG) data was studied, and the authors found that the CL method, while very effective in image data cleaning, is not suitable for EEG processing due to the impact of artifacts.
Proceedings ArticleDOI
System Identification of the EEG Transformation Due to TMS Pulses: A Novel Method for a Synchronous Brain Computer Interface.
Gregory W. Price,Roberto Togneri +1 more
TL;DR: This study is the first known to use system identification, and in particular the system identification of the brain's response to a TMS pulse as an index of intention, and provides proof of concept as well as an initial implementation and evaluation of this form of BCI.
Journal ArticleDOI
An optimized artificial intelligence based technique for identifying motor imagery from EEGs for advanced brain computer interface technology
Journal ArticleDOI
Improving Performance of Motor Imagery-Based Brain–Computer Interface in Poorly Performing Subjects Using a Hybrid-Imagery Method Utilizing Combined Motor and Somatosensory Activity
TL;DR: In this paper , the authors used a hybrid-imagery approach that combined motor and somatosensory activity to enhance the classification performance of motor imagery-based brain-computer interface (three-class: left hand, right hand, and right foot) of poor performers.
Posted Content
Hierarchical feature fusion framework for frequency recognition in SSVEP-based BCIs
TL;DR: In this article, a hierarchical feature fusion framework was proposed to design high-performance frequency recognition methods in steady-state visual evoked potential (SSVEP) based brain-computer interfaces.
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.