Prediction of Event Related Potential Speller Performance Using Resting-State EEG
This work addresses performance instability and ERP-illiteracy in brain-computer interfaces for severely disabled patients, but it is incremental as it builds on existing methods for prediction.
The study tackled the problem of predicting event-related potential (ERP) speller performance for locked-in patients by analyzing resting-state EEG features, finding significant correlations with delta power and functional connectivity in specific brain regions and frequency bands.
Event-related potential (ERP) speller can be utilized in device control and communication for locked-in or severely injured patients. However, problems such as inter-subject performance instability and ERP-illiteracy are still unresolved. Therefore, it is necessary to predict classification performance before performing an ERP speller in order to use it efficiently. In this study, we investigated the correlations with ERP speller performance using a resting-state before an ERP speller. In specific, we used spectral power and functional connectivity according to four brain regions and five frequency bands. As a result, the delta power in the frontal region and functional connectivity in the delta, alpha, gamma bands are significantly correlated with the ERP speller performance. Also, we predicted the ERP speller performance using EEG features in the resting-state. These findings may contribute to investigating the ERP-illiteracy and considering the appropriate alternatives for each user.