Incremental SSVEP analysis for BCI implementation

This work presents an incremental analysis of EEG records containing Steady-State Visual Evoked Potential (SSVEP). This analysis consists of two steps: feature extraction, performed using a statistic test, and classification, performed by a decision tree. The result is a system with high classification rate (a test with six volunteers resulted in an average classification rate of 91.2%), high Information Transfer Rate (ITR) (a test with the same six volunteers resulted in an average value of 100.2 bits/min) and processing time, for each incremental analysis, of approximately 120 ms. These are very good features for an efficient Brain-Computer Interface (BCI) implementation.