Brain-Computer Interface Data from Code-Modulated Visual Evoked Potentials

Experimental data from a brain-computer interface study involving eight subjects. It was used to analyze a novel four-group code-modulated visual evoked potential paradigm with 64 stimulus targets, achieving an average classification accuracy of 88.36%.

Use Cases

• Analyze auto- and cross-correlation characteristics of c-VEP signals from the four target groups.
• Calculate signal-to-noise ratio and correlation coefficients to evaluate the proposed grouping modulation paradigm.
• Train classifiers for single-trial attended target recognition using data from one stimulus cycle.
• Compute information transfer rates based on classification accuracy results across eight subjects.

Strengths

• Data is derived from a controlled BCI experiment with eight recruited subjects.
• The study achieved a high average classification accuracy of 88.36%.
• The novel paradigm tested involves 64 distinct stimulus targets.

Limitations

• The sample size is limited to data from only eight subjects.
• Specific data structure details like column names, row counts, and file formats are unknown.

Provenance

Source

Dryad

Collection Method

Data collected from a controlled BCI experiment where subjects interacted with a four-group c-VEP system.

Freshness

2020-06-24