Proceedings ArticleDOI
Frequency detection for SSVEP-based BCI using deep canonical correlation analysis
Hanh Vu,Bonkon Koo,Seungjin Choi +2 more
- pp 001983-001987
Reads0
Chats0
TLDR
To the best knowledge, this is the first time to apply deep CCA (DCCA) to the task of frequency component extraction in SSVEP and it demonstrates that DCCA extracts more robust feature, which has significantly higher signal to noise ratio (SNR) compared to those of CCA.Abstract:
Canonical correlation analysis (CCA) has been successfully used for extracting frequency components of steady-state visual evoked potential (SSVEP) in electroencephalography (EEG). Recently, a few efforts on CCA-based SSVEP methods have been made to demonstrate the benefits for brain computer interface (BCI). Most of these methods are limited to linear CCA. In this paper consider a deep extension of CCA where input data are processed through multiple layers before their correlations are computed. To our best knowledge, it is the first time to apply deep CCA (DCCA) to the task of frequency component extraction in SSVEP. Our empirical study demonstrates that DCCA extracts more robust feature, which has significantly higher signal to noise ratio (SNR) compared to those of CCA, and it results in better performance in classification with the averaged accuracy of 92%.read more
Citations
More filters
Journal ArticleDOI
A Dynamic Window Recognition Algorithm for SSVEP-Based Brain–Computer Interfaces Using a Spatio-Temporal Equalizer
TL;DR: It is suggested 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.
Journal ArticleDOI
Efficient representations of EEG signals for SSVEP frequency recognition based on deep multiset CCA
Qianqian Liu,Yong Jiao,Yangyang Miao,Cili Zuo,Xingyu Wang,Andrzej Cichocki,Andrzej Cichocki,Jing Jin +7 more
TL;DR: A nonlinear feature extraction method based on deep multiset CCA (DMCCA) is proposed for SSVEP recognition to fully utilize the real EEG and constructed sine–cosine signals.
Journal ArticleDOI
Data-Driven Fault Diagnosis Using Deep Canonical Variate Analysis and Fisher Discriminant Analysis
TL;DR: In this article, a novel data-driven fault diagnosis method by combining deep canonical variate analysis and Fisher discriminant analysis (DCVA-FDA) is proposed for complex industrial processes.
Journal ArticleDOI
Spatiotemporal Beamforming: A Transparent and Unified Decoding Approach to Synchronous Visual Brain-Computer Interfacing.
TL;DR: It is argued that spatiotemporal beamforming can serve several synchronous visual BCI paradigms even without attempting to optimizing their electrode sets and investigated whether interactions between beamformer outputs could be employed to increase accuracy.
Proceedings ArticleDOI
A time domain classification of steady-state visual evoked potentials using deep recurrent-convolutional neural networks
TL;DR: This paper proposes a deep learning model that uses a hybrid architecture based on Convolutional and Recurrent Neural Networks to classify SSVEP signals in the time domain directly and achieves accuracy higher than the state-of-the-art method: canonical correlation analysis in the frequency domain.
References
More filters
Journal ArticleDOI
Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs
Journal ArticleDOI
Frequency Recognition in SSVEP-based BCI using Multiset Canonical Correlation Analysis
TL;DR: Experimental study with EEG data from 10 healthy subjects demonstrates that the proposed MsetCCA method improves the recognition accuracy of SSVEP frequency in comparison with the CCA method and other two competing methods (multiway CCA (MwayCCA), especially for a small number of channels and a short time window length.
Journal ArticleDOI
Frequency recognition in SSVEP-based BCI using multiset canonical correlation analysis.
TL;DR: In this paper, a multiset canonical correlation analysis (MsetCCA) method was proposed to optimize the reference signals used in the CCA method for SSVEP frequency recognition.
Journal ArticleDOI
A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials.
TL;DR: The results suggest that individual calibration data can significantly improve the detection performance of SSVEP detection methods and show that the combination method based on the standard CCA and the individual template based CCA (IT-CCA) achieved the highest performance.
Proceedings ArticleDOI
Unsupervised learning of acoustic features via deep canonical correlation analysis
TL;DR: This work uses the recently proposed deep CCA, where the functional form of the feature mapping is a deep neural network, and applies the approach on a speaker-independent phonetic recognition task using data from the University of Wisconsin X-ray Microbeam Database.