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A comprehensive benchmark dataset for SSVEP-based hybrid BCI

A comprehensive benchmark dataset for SSVEP-based hybrid BCI

Abstract: The use of hybrid braincomputer interface (HBCI) systems have received much attention in recent years.
However, there are few published datasets for HBCI systems. This paper provides a benchmark dataset for these
systems. The provided dataset consist of data corresponding to three speller systems, including SSVEP-based BCI
system and HBCI systems based on SSVEP-EMG and SSVEP-EOG. These spellers have been implemented using
electroencephalogram (EEG) and other biosignals, including electromyogram (EMG) and electrooculogram
(EOG). The virtual keyboard of spellers was composed of 9 visual flickers with stimulation frequencies ranged
from 5.58 Hz to 11.11 Hz. The stimuli presentation was controlled by the psychophysics toolbox of Matlab. We
conducted three experiments, using three speller systems under the similar situation. Ten subjects participated in
a copy-spelling task experiment. For each system, the data included ten sessions corresponding to 10 times,
which the subject spells the determined phrase. These HBCI systems were validated using NASA-TLX workload
index and performance evaluation parameters, including the accuracy and the information transfer rate. Results
showed that combining two control signals only when signals are derived from one organ of the body, might
increase the workload. These datasets can be used as a comprehensive dataset to evaluate the performance of
different HBCI systems. They are freely available from
Http://maleki.semnan.ac.ir/.

Cite: Sadeghi S, Maleki A. A Comprehensive Benchmark Dataset for SSVEP-based Hybrid BCI. Expert Systems with Applications. 2022 Apr 4:117180.

DOI: https://doi.org/10.1016/j.eswa.2022.117180

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