SSVEP-assisted RSVP brain-computer interface paradigm for multi-target classification

Li-Wei Ko*, D. Sandeep Vara Sankar, Yufei Huang, Yun-Chen Lu, Siddharth Shaw, Tzyy-Ping Jung

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Brain-computer Interface (BCI) is actively involved in optimizing the communication medium between the human brain and external devices.Objective. Rapid serial visual presentation (RSVP) is a robust and highly efficient BCI technique in recognizing target objects but suffers from limited target selections. Hybrid BCI systems that combine steady-state visual evoked potential (SSVEP) and RSVP can mitigate this limitation and allow users to operate on multiple targets. Approach. This study proposes a novel hybrid SSVEP-RSVP BCI to improve the performance of classifying the target/non-target objects in a multi-target scenario. In this paradigm, SSVEP stimulation helps in identifying the user's focus location and RSVP stimuli that elicit event-related potentials differentiate target and non-target objects. Main results. The proposed model achieved an offline accuracy of 81.59% by using 12 electroencephalography (EEG) channels and an online (real-time) accuracy of 78.10% when only four EEG channels are considered. Further, the biomarkers of physiological states are analyzed to assess the cognitive states (mental fatigue and user attention) of the participants based on resting theta and alpha band powers. The results indicate an inverse relationship between the BCI performance and the resting EEG power, validating that the subjects' performance is affected by physiological states for long-term use of the BCI. Significance. Our findings demonstrate that the combination of SSVEP and RSVP stimuli improves the BCI performance and further enhances the possibility of performing multiple user command tasks, which are inevitable in real-world applications. Additionally, the cognitive state biomarkers discussed imply the need for an efficient and attractive experimental paradigm that reduces the physiological state disparities and provide enhanced BCI performance.

Original languageEnglish
Article number016021
Pages (from-to)1-12
Number of pages12
JournalJournal of Neural Engineering
Issue number1
StatePublished - Feb 2021


  • electroencephalography
  • brain-computer interface
  • visual evoked potential
  • event-related potential
  • physiological biomarkers
  • multiple targets

Cite this