Paper Info
Title
A Dynamic Window Recognition Algorithm For Ssvep-Based Brain-Computer Interfaces Using A Spatio-Temporal Equalizer
Abstract
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.
Year
DOI
Venue
2018
10.1142/S0129065718500284
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
Keywords
Field
DocType
Brain-computer interface, steady-state visual evoked potentials, spatio-temporal equalization, dynamic window
Equalizer,Bottleneck,Pattern recognition,Computer science,Brain–computer interface,Speech recognition,Artificial intelligence,Recognition algorithm
Journal
Volume
Issue
ISSN
28
10
0129-0657
Citations 
PageRank 
References 
1
0.35
20
Authors
6
Name
Order
Citations
PageRank
Chen Yang174.97
Xu Han2239.85
Yijun Wang330846.68
Rami Saab410.35
Gao Shangkai530232.86
Xiaorong Gao659881.99