An Adaptive Task-Related Component Analysis Method for SSVEP Recognition

Oikonomou, Vangelis P.

doi:10.3390/s22207715

Cited by 9 publications

(2 citation statements)

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“…SSVEP-BCI is an important human-computer interaction technique, and the accurate decoding of SSVEP signals is the key to ensuring the widespread application of SSVEP-BCI systems. Common SSVEP classification techniques include the matrix classifier (spatial filter) ( Wong et al, 2020 ; Oikonomou, 2022 ), Riemannian geometry classifier ( Kalunga et al, 2016 ), tensor classifier, transfer learning, and deep learning, all of which are static classifiers, i.e., the classifier parameters are fixed. However, EEG signals are non-stationary and time-varying.…”

Section: Discussionmentioning

confidence: 99%

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

Yan,

He,

Zhao

2023

Front. Neurosci.

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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.

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Section: Discussionmentioning

confidence: 99%

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

Yan,

He,

Zhao

2023

Front. Neurosci.

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“…However, the majority of current algorithmic research utilizes one or two datasets to verify their performance [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ], which did not make full use of public data resources, and the results were limited by the distribution of data samples in individual datasets, so it was not conducive to judge the application effect of the algorithm in the actual scene through the result. This issue has two underlying causes.…”

Section: Introductionmentioning

confidence: 99%

Dataset Evaluation Method and Application for Performance Testing of SSVEP-BCI Decoding Algorithm

Liang

Zhang

Zhou

et al. 2023

Sensors

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Steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) systems have been extensively researched over the past two decades, and multiple sets of standard datasets have been published and widely used. However, there are differences in sample distribution and collection equipment across different datasets, and there is a lack of a unified evaluation method. Most new SSVEP decoding algorithms are tested based on self-collected data or offline performance verification using one or two previous datasets, which can lead to performance differences when used in actual application scenarios. To address these issues, this paper proposed a SSVEP dataset evaluation method and analyzed six datasets with frequency and phase modulation paradigms to form an SSVEP algorithm evaluation dataset system. Finally, based on the above datasets, performance tests were carried out on the four existing SSVEP decoding algorithms. The findings reveal that the performance of the same algorithm varies significantly when tested on diverse datasets. Substantial performance variations were observed among subjects, ranging from the best-performing to the worst-performing. The above results demonstrate that the SSVEP dataset evaluation method can integrate six datasets to form a SSVEP algorithm performance testing dataset system. This system can test and verify the SSVEP decoding algorithm from different perspectives such as different subjects, different environments, and different equipment, which is helpful for the research of new SSVEP decoding algorithms and has significant reference value for other BCI application fields.

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