scholarly journals In-Ear Electrode EEG for Practical SSVEP BCI

Technologies ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 63
Author(s):  
Surej Mouli ◽  
Ramaswamy Palaniappan ◽  
Emmanuel Molefi ◽  
Ian McLoughlin
Keyword(s):  
Transfer Rate ◽  
Information Transfer ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Stimulus Frequency ◽  
Alternative Methods ◽  
Occipital Region ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Information Transfer Rate

Steady State Visual Evoked Potential (SSVEP) methods for brain–computer interfaces (BCI) are popular due to higher information transfer rate and easier setup with minimal training, compared to alternative methods. With precisely generated visual stimulus frequency, it is possible to translate brain signals into external actions or signals. Traditionally, SSVEP data is collected from the occipital region using electrodes with or without gel, normally mounted on a head cap. In this experimental study, we develop an in-ear electrode to collect SSVEP data for four different flicker frequencies and compare against occipital scalp electrode data. Data from five participants demonstrates the feasibility of in-ear electrode based SSVEP, significantly enhancing the practicability of wearable BCI applications.

SSVEP Recognition by Using Higher Harmonics Based on Music

2016 ◽  
Vol 16 (4) ◽  
Author(s):  
Kun Chen ◽  
Fei Xu ◽  
Quan Liu ◽  
Haojie Liu ◽  
Yang Zhang ◽  
...  
Keyword(s):  
Transfer Rate ◽  
Information Transfer ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Signal To Noise Ratio ◽  
Signal Classification ◽  
Recognition Time ◽  
Multiple Signal Classification ◽  
Computer Interfaces ◽  
Information Transfer Rate

Among different brain–computer interfaces (BCIs), the steady-state visual evoked potential (SSVEP)-based BCI has been widely used because of its higher signal to noise ratio (SNR) and greater information transfer rate (ITR). In this paper, a method based on multiple signal classification (MUSIC) was proposed for multidimensional SSVEP signal processing. Both fundamental and second harmonics of SSVEPs were employed for the final target recognition. The experimental results proved it has the advantage of reducing recognition time. Also, the relation between the duty-cycle of the stimulus signals and the amplitude of the second harmonics of SSVEPs was discussed via experiments. In order to verify the feasibility of proposed methods, a two-layer spelling system was designed. Different subjects including those who have never used BCIs before used the system fluently in an unshielded environment.

Multi-Channel SSVEP Pattern Recognition Based on MUSIC

2014 ◽  
Vol 539 ◽  
pp. 84-88 ◽  
Author(s):  
Kun Chen ◽  
Quan Liu ◽  
Qing Song Ai
Keyword(s):  
Information Transfer ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Recognition Accuracy ◽  
Signal To Noise Ratio ◽  
Frequency Resolution ◽  
Disabled People ◽  
Multiple Signal Classification ◽  
Computer Interfaces ◽  
Information Transfer Rate

Brain computer interfaces (BCIs) have become a research hotspot in recent years because of great potentials to help disabled people communicate with the outside world. Among different paradigms, steady state visual evoked potential (SSVEP)-based BCIs are commonly implemented in real applications, because they provide higher signal to noise ratio (SNR) and greater information transfer rate (ITR) than other BCI techniques. Various algorithms have been employed for SSVEP signal processing, like fast Fourier transform (FFT), wavelet analysis and canonical correlation analysis (CCA). In this paper, a new method based on multiple signal classification (MUSIC) was proposed for SSVEP feature extraction. The experimental results proved that it could provide higher frequency resolution and the recognition accuracy was excellent via adjusting some parameters.

scholarly journals An Efficient Frequency Recognition Method Based on Likelihood Ratio Test for SSVEP-Based BCI

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Yangsong Zhang ◽  
Li Dong ◽  
Rui Zhang ◽  
Dezhong Yao ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Transfer Rate ◽  
Information Transfer ◽  
Time Window ◽  
Recognition Accuracy ◽  
Stimulus Frequency ◽  
Ratio Test ◽  
Recognition Method ◽  
Information Transfer Rate

An efficient frequency recognition method is very important for SSVEP-based BCI systems to improve the information transfer rate (ITR). To address this aspect, for the first time, likelihood ratio test (LRT) was utilized to propose a novel multichannel frequency recognition method for SSVEP data. The essence of this new method is to calculate the association between multichannel EEG signals and the reference signals which were constructed according to the stimulus frequency with LRT. For the simulation and real SSVEP data, the proposed method yielded higher recognition accuracy with shorter time window length and was more robust against noise in comparison with the popular canonical correlation analysis- (CCA-) based method and the least absolute shrinkage and selection operator- (LASSO-) based method. The recognition accuracy and information transfer rate (ITR) obtained by the proposed method was higher than those of the CCA-based method and LASSO-based method. The superior results indicate that the LRT method is a promising candidate for reliable frequency recognition in future SSVEP-BCI.

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