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

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 14; p. 6310
• Main Authors: Liang, Liyan, Zhang, Qian, Zhou, Jie, Li, Wenyu, Gao, Xiaorong
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 11.07.2023; MDPI
• Subjects: 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)

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...