Time sparsification of EEG signals in motor-imagery based brain computer interfaces

2012 ◽  
Author(s):  
H. Higashi ◽  
T. Tanaka
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interfaces ◽  
Eeg Signals ◽  
Computer Interfaces

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.

scholarly journals Motor Imagery EEG Signals Decoding by Multivariate Empirical Wavelet Transform-Based Framework for Robust Brain–Computer Interfaces

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 171431-171451 ◽  
Author(s):  
Muhammad Tariq Sadiq ◽  
Xiaojun Yu ◽  
Zhaohui Yuan ◽  
Fan Zeming ◽  
Ateeq Ur Rehman ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Motor Imagery ◽  
Brain Computer Interfaces ◽  
Eeg Signals ◽  
Computer Interfaces ◽  
Empirical Wavelet Transform

Classification of EEG Signals Based on Filter Bank and Sparse Representation in Motor Imagery Brain-Computer Interfaces

2019 ◽  
Vol 29 (03) ◽  
pp. 2050034 ◽  
Author(s):  
Jin Wang ◽  
Qingguo Wei
Keyword(s):  
Sparse Representation ◽  
Motor Imagery ◽  
Filter Bank ◽  
Classification Performance ◽  
Brain Computer Interfaces ◽  
Eeg Signals ◽  
Fisher Score ◽  
Common Spatial Pattern ◽  
Signal Processing Algorithm ◽  
Computer Interfaces

To improve the classification performance of motor imagery (MI) based brain-computer interfaces (BCIs), a new signal processing algorithm for classifying electroencephalogram (EEG) signals by combining filter bank and sparse representation is proposed. The broadband EEG signals of 8–30[Formula: see text]Hz are segmented into 10 sub-band signals using a filter bank. EEG signals in each sub-band are spatially filtered by common spatial pattern (CSP). Fisher score combined with grid search is used for selecting the optimal sub-band, the band power of which is employed for designing a dictionary matrix. A testing signal can be sparsely represented as a linear combination of some columns of the dictionary. The sparse coefficients are estimated by [Formula: see text] norm optimization, and the residuals of sparse coefficients are exploited for classification. The proposed classification algorithm was applied to two BCI datasets and compared with two traditional broadband CSP-based algorithms. The results showed that the proposed algorithm provided superior classification accuracies, which were better than those yielded by traditional algorithms, verifying the efficacy of the present algorithm.

scholarly journals User Experience May be Producing Greater Heart Rate Variability than Motor Imagery Related Control Tasks during the User-System Adaptation in Brain-Computer Interfaces

2016 ◽  
Vol 7 ◽  
Author(s):  
Luz M. Alonso-Valerdi ◽  
David A. Gutiérrez-Begovich ◽  
Janet Argüello-García ◽  
Francisco Sepulveda ◽  
Ricardo A. Ramírez-Mendoza
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Motor Imagery ◽  
User Experience ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
System Adaptation

Ethical implications of brain-computer interfaces with emotion, motor imagery, and subvocal speech classification

2021 ◽  
Author(s):  
Joseph O'Neill ◽  
Jenario Johnson ◽  
Rutledge Detyens ◽  
Roberto W. Batista ◽  
Sorinel Oprisan ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interfaces ◽  
Ethical Implications ◽  
Computer Interfaces ◽  
Speech Classification
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