A Dynamic Window Recognition Algorithm for SSVEP-Based Brain–Computer Interfaces Using a Spatio-Temporal Equalizer

2018 ◽  
Vol 28 (10) ◽  
pp. 1850028 ◽  
Author(s):  
Chen Yang ◽  
Xu Han ◽  
Yijun Wang ◽  
Rami Saab ◽  
Shangkai Gao ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Recognition Accuracy ◽  
Recognition Algorithm ◽  
Benchmark Dataset ◽  
Ease Of Use ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Spatio Temporal

The past decade has witnessed rapid development in the field of brain–computer interfaces (BCIs). While the performance is no longer the biggest bottleneck in the BCI application, the tedious training process and the poor ease-of-use have become the most significant challenges. In this study, a spatio-temporal equalization dynamic window (STE-DW) recognition algorithm is proposed for steady-state visual evoked potential (SSVEP)-based BCIs. The algorithm can adaptively control the stimulus time while maintaining the recognition accuracy, which significantly improves the information transfer rate (ITR) and enhances the adaptability of the system to different subjects. Specifically, a spatio-temporal equalization algorithm is used to reduce the adverse effects of spatial and temporal correlation of background noise. Based on the theory of multiple hypotheses testing, a stimulus termination criterion is used to adaptively control the dynamic window. The offline analysis which used a benchmark dataset and an offline dataset collected from 16 subjects demonstrated that the STE-DW algorithm is superior to the filter bank canonical correlation analysis (FBCCA), canonical variates with autoregressive spectral analysis (CVARS), canonical correlation analysis (CCA) and CCA reducing variation (CCA-RV) algorithms in terms of accuracy and ITR. The results show that in the benchmark dataset, the STE-DW algorithm achieved an average ITR of 134 bits/min, which exceeds the FBCCA, CVARS, CCA and CCA-RV. In off-line experiments, the STE-DW algorithm also achieved an average ITR of 116 bits/min. In addition, the online experiment also showed that the STE-DW algorithm can effectively expand the number of applicable users of the SSVEP-based BCI system. We suggest that the STE-DW algorithm can be used as a reliable identification algorithm for training-free SSVEP-based BCIs, because of the good balance between ease of use, recognition accuracy, ITR and user applicability.

Underwater Sediments Echoes Recognition Based on KECCA + PLS

2013 ◽  
Vol 310 ◽  
pp. 629-633
Author(s):  
Bo Wen Luo ◽  
Bu Yan Wan ◽  
Wei Bin Qin ◽  
Ji Yu Xu
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Feature Fusion ◽  
Recognition Accuracy ◽  
Partial Least Square ◽  
Least Square ◽  
Classification Model ◽  
Nuclear Space ◽  
Nonlinear Feature

In order to solve the nonlinear feature fusion of underwater sediments echoes, the shortage of Enhanced Canonical Correlation Analysis (ECCA) was analyzed and made ECCA extend to Kernel ECCA (KECCA) in the nuclear space, a multi-feature nonlinear fusion classification model with KECCA combining with Partial Least-Square (PLS ) was put forward。In the process of identifying four types of underwater sediment such as Basalt, Volcanic breccia, Cobalt crusts and Mudstone, the results showed that the recognition accuracy could be further improved for the KECCA + PLS model.

scholarly journals FREQUENCY RECOGNITION IN SSVEP-BASED BCI USING MULTISET CANONICAL CORRELATION ANALYSIS

2014 ◽  
Vol 24 (04) ◽  
pp. 1450013 ◽  
Author(s):  
YU ZHANG ◽  
GUOXU ZHOU ◽  
JING JIN ◽  
XINGYU WANG ◽  
ANDRZEJ CICHOCKI
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Time Window ◽  
Recognition Accuracy ◽  
Stimulus Frequency ◽  
Training Data ◽  
Common Features ◽  
Eeg Data ◽  
Reference Signals

Canonical correlation analysis (CCA) has been one of the most popular methods for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs). Despite its efficiency, a potential problem is that using pre-constructed sine-cosine waves as the required reference signals in the CCA method often does not result in the optimal recognition accuracy due to their lack of features from the real electro-encephalo-gram (EEG) data. To address this problem, this study proposes a novel method based on multiset canonical correlation analysis (MsetCCA) to optimize the reference signals used in the CCA method for SSVEP frequency recognition. The MsetCCA method learns multiple linear transforms that implement joint spatial filtering to maximize the overall correlation among canonical variates, and hence extracts SSVEP common features from multiple sets of EEG data recorded at the same stimulus frequency. The optimized reference signals are formed by combination of the common features and completely based on training data. Experimental study with EEG data from 10 healthy subjects demonstrates that the MsetCCA method improves the recognition accuracy of SSVEP frequency in comparison with the CCA method and other two competing methods (multiway CCA (MwayCCA) and phase constrained CCA (PCCA)), especially for a small number of channels and a short time window length. The superiority indicates that the proposed MsetCCA method is a new promising candidate for frequency recognition in SSVEP-based BCIs.

Analysis of Maximum Expiratory Flow Volume Curves Using Canonical Correlation Analysis

1985 ◽  
Vol 24 (02) ◽  
pp. 91-100 ◽  
Author(s):  
W. van Pelt ◽  
Ph. H. Quanjer ◽  
M. E. Wise ◽  
E. van der Burg ◽  
R. van der Lende
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Flow Volume ◽  
Non Linear ◽  
Maximum Expiratory Flow ◽  
Expiratory Flow ◽  
Relationship Of ◽  
The Relationship ◽  
Age And Sex

SummaryAs part of a population study on chronic lung disease in the Netherlands, an investigation is made of the relationship of both age and sex with indices describing the maximum expiratory flow-volume (MEFV) curve. To determine the relationship, non-linear canonical correlation was used as realized in the computer program CANALS, a combination of ordinary canonical correlation analysis (CCA) and non-linear transformations of the variables. This method enhances the generality of the relationship to be found and has the advantage of showing the relative importance of categories or ranges within a variable with respect to that relationship. The above is exemplified by describing the relationship of age and sex with variables concerning respiratory symptoms and smoking habits. The analysis of age and sex with MEFV curve indices shows that non-linear canonical correlation analysis is an efficient tool in analysing size and shape of the MEFV curve and can be used to derive parameters concerning the whole curve.

Sign in / Sign up

Export Citation Format

Share Document