A Novel Criterion of Wavelet Packet Best Basis Selection for Signal Classification With Application to Brain–Computer Interfaces

2009 ◽  
Vol 56 (11) ◽  
pp. 2734-2738 ◽  
Author(s):  
D. Vautrin ◽  
X. Artusi ◽  
M.-F. Lucas ◽  
D. Farina
Keyword(s):  
Wavelet Packet ◽  
Signal Classification ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Selection For ◽  
Best Basis Selection

Channel Selection for Optimal EEG Measurement in Motor Imagery-Based Brain-Computer Interfaces

2020 ◽  
pp. 2150003
Author(s):  
Pasquale Arpaia ◽  
Francesco Donnarumma ◽  
Antonio Esposito ◽  
Marco Parvis
Keyword(s):  
Motor Imagery ◽  
Classification Accuracy ◽  
State Of The Art ◽  
Binary Classification ◽  
Brain Computer Interfaces ◽  
Eeg Signals ◽  
Non Invasive ◽  
Computer Interfaces ◽  
Selection For ◽  
A Performance

A method for selecting electroencephalographic (EEG) signals in motor imagery-based brain-computer interfaces (MI-BCI) is proposed for enhancing the online interoperability and portability of BCI systems, as well as user comfort. The attempt is also to reduce variability and noise of MI-BCI, which could be affected by a large number of EEG channels. The relation between selected channels and MI-BCI performance is therefore analyzed. The proposed method is able to select acquisition channels common to all subjects, while achieving a performance compatible with the use of all the channels. Results are reported with reference to a standard benchmark dataset, the BCI competition IV dataset 2a. They prove that a performance compatible with the best state-of-the-art approaches can be achieved, while adopting a significantly smaller number of channels, both in two and in four tasks classification. In particular, classification accuracy is about 77–83% in binary classification with down to 6 EEG channels, and above 60% for the four-classes case when 10 channels are employed. This gives a contribution in optimizing the EEG measurement while developing non-invasive and wearable MI-based brain-computer interfaces.

Variable Down-Selection for Brain-Computer Interfaces

2010 ◽  
pp. 158-172 ◽  
Author(s):  
Nuno S. Dias ◽  
Mst Kamrunnahar ◽  
Paulo M. Mendes ◽  
Steven J. Schiff ◽  
Jose H. Correia
Keyword(s):  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Selection For
Sign in / Sign up

Export Citation Format

Share Document