SSVEP-EEG Denoising via Image Filtering Methods

Yan, Wenqiang; Du, Chenlin; Wu, Yongcheng; Zheng, Xiaowei; Xu, Guanghua

doi:10.1109/tnsre.2021.3104825

Cited by 13 publications

(9 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Algo-H, in addition to the above methods, we used image filtering denoising (IFD) proposed by Yan et al [19] to denoise the EEG data. In IFD, the authors found that image filtering of SSVEP could not effectively remove the noise; thus, they proposed a reverse solution in which the SSVEP noise signal was obtained by image filtering, and the noise was subtracted from the original signal.…”

Section: Preprocessing Methodsmentioning

confidence: 99%

Overview of the winning approaches in BCI Controlled Robot Contest in World Robot Contest 2021: Calibration-free SSVEP

Bian

2022

Brain Science Advances

View full text Add to dashboard Cite

Recently, steady-state visual evoked potential (SSVEP) has become one of the most popular electroencephalography paradigms due to its high information transfer rate. Several approaches have been proposed to improve the performance of SSVEP. The calibration-free scenario is significant in SSVEP-based brain–computer interface systems, where the subject is the first time to use the system. The participating teams proposed several effective calibration-free algorithm frameworks in the SSVEP competition (calibration-free) of the BCI Controlled Robot Contest in World Robot Contest 2021. This paper introduces the approaches used in the algorithms of the top five teams in the final. The results of the five subjects in the final proved the effectiveness of the approaches. This paper discusses the effectiveness of each approach in improving the system performance in the calibration-free scenario and gives suggestions on how to use these approaches in a real-world system.

show abstract

Section: Preprocessing Methodsmentioning

confidence: 99%

Overview of the winning approaches in BCI Controlled Robot Contest in World Robot Contest 2021: Calibration-free SSVEP

Bian

2022

Brain Science Advances

View full text Add to dashboard Cite

show abstract

“…One of the paramount applications of SSVEP is within the realm of BCI, where the precise identification of a user's intent stands as a pivotal research direction [5–8]. In SSVEP‐based BCIs, an array of flashing modules, each oscillating at distinct frequencies, is used as stimuli, where each stimulus corresponds to a specific operational command [9]. When a user fixates on a particular stimulus, cortical neural activities get modulated, resulting in the generation of periodic rhythms that resonate at the same frequency as the stimulus.…”

Section: Introductionmentioning

confidence: 99%

“…To enhance the extraction efficiency of SSVEP signals, it becomes imperative to augment their characteristics. Yan et al [9] introduced an image filtering denoising (IFD) method for SSVEP, initially filtering multichannel EEG signals to retrieve noise, which is then subtracted from the original signal to procure the denoised version. This approach essentially delineates blurred details from an image and then excludes them to obtain finer details.…”

Section: Introductionmentioning

confidence: 99%

Research on SSVEP‐EEG feature enhancement algorithm based on fractional differentiation

Li,

Wang,

Yuan

et al. 2024

The Journal of Engineering

View full text Add to dashboard Cite

Steady‐state visual evoked potentials (SSVEP), significant in brain‐computer interfaces (BCI) and medical diagnostics, benefit from enhanced signal processing for improved analysis and interpretation. This study introduces a novel enhancement algorithm for SSVEP electroencephalogram (EEG) signals, employing fractional‐order differentiation operators combined with image processing techniques. Utilizing fractional‐order differentiation within a Laplace pyramid framework, the algorithm achieves hierarchical signal enhancement, facilitating detailed feature extraction and emphasizing SSVEP signal characteristics. This innovative approach merges the precision of fractional calculus with the structural benefits of the Laplace pyramid, leading to enhanced signal clarity and feature discrimination. The efficacy of this method was validated using canonical correlation analysis (CCA), filter bank CCA (FBCCA), and task‐related component analysis (TRCA) on a public dataset. Compared to conventional methods, our algorithm not only mitigates trend components in SSVEP signals but also significantly boosts the recognition accuracy of CCA, FBCCA, and TRCA algorithms. Experimental results indicate a marked improvement in recognition precision, underscoring the algorithm's potential to advance SSVEP‐based BCI research.

show abstract

“…In standard SSVEP-BCI systems, the user can view multiple concurrent stimuli located at various positions in the visual field (e.g., multiple light flicker patterns on the screen). Each stimulus is presented at a fixed frequency and represents a specific BCI output (e.g., outputting a specific letter or moving a wheelchair in a specific direction), and the user outputs a control command by directing their gaze at the stimulus representing the desired BCI output ( Yan et al, 2021a , b , 2022 ).…”

Section: Introductionmentioning

confidence: 99%

SSVEP unsupervised adaptive feature recognition method based on self-similarity of same-frequency signals

Yan,

He,

Zhao

2023

Front. Neurosci.

Self Cite

View full text Add to dashboard Cite

IntroductionAs an important human-computer interaction technology, steady-state visual evoked potential (SSVEP) plays a key role in the application of brain computer interface (BCI) systems by accurately decoding SSVEP signals. Currently, the majority SSVEP feature recognition methods use a static classifier. However, electroencephalogram (EEG) signals are non-stationary and time-varying. Hence, an adaptive classification method would be an alternative option to a static classifier for tracking the changes in EEG feature distribution, as its parameters can be re-estimated and updated with the input of new EEG data.MethodsIn this study, an unsupervised adaptive classification algorithm is designed based on the self-similarity of same-frequency signals. The proposed classification algorithm saves the EEG data that has undergone feature recognition as a template signal in accordance with its estimated label, and the new testing signal is superimposed with the template signals at each stimulus frequency as the new test signals to be analyzed. With the continuous input of EEG data, the template signals are continuously updated.ResultsBy comparing the classification accuracy of the original testing signal and the testing signal superimposed with the template signals, this study demonstrates the effectiveness of using the self-similarity of same-frequency signals in the adaptive classification algorithm. The experimental results also show that the longer the SSVEP-BCI system is used, the better the responses of users on SSVEP are, and the more significantly the adaptive classification algorithm performs in terms of feature recognition. The testing results of two public datasets show that the adaptive classification algorithm outperforms the static classification method in terms of feature recognition.DiscussionThe proposed adaptive classification algorithm can update the parameters with the input of new EEG data, which is of favorable impact for the accurate analysis of EEG data with time-varying characteristics.

show abstract

SSVEP-EEG Denoising via Image Filtering Methods

Cited by 13 publications

References 26 publications

Overview of the winning approaches in BCI Controlled Robot Contest in World Robot Contest 2021: Calibration-free SSVEP

Overview of the winning approaches in BCI Controlled Robot Contest in World Robot Contest 2021: Calibration-free SSVEP

Research on SSVEP‐EEG feature enhancement algorithm based on fractional differentiation

SSVEP unsupervised adaptive feature recognition method based on self-similarity of same-frequency signals

Contact Info

Product

Resources

About