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

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 equip...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 23; H. 14; S. 6310
Hauptverfasser: Liang, Liyan, Zhang, Qian, Zhou, Jie, Li, Wenyu, Gao, Xiaorong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 11.07.2023
MDPI
Schlagworte:
algorithm
Algorithms
Brain research
Brain-Computer Interfaces
brain–computer interface (BCI)
Codes
Correlation analysis
dataset
Datasets
Electrodes
Electroencephalography - methods
Evoked Potentials, Visual
Humans
Methods
performance testing
Photic Stimulation
Signal to noise ratio
steady-state visual evoked potential (SSVEP)
brain–computer interface (BCI)
performance testing
dataset
algorithm
steady-state visual evoked potential (SSVEP)
ISSN:1424-8220, 1424-8220
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1424-8220
1424-8220
DOI:10.3390/s23146310