A High-Rate BCI Speller Based on Eye-Closed EEG Signal

This paper aims to develop a Brain-computer interface (BCI) speller utilizing eyes-closed and double-blinking electroencephalogram (EEG) signals and based on asynchronous mechanism. The proposed system comprises a signal processing module and a graphical user interface (virtual keyboard-VK) with 26 English characters plus a special symbol. A detected “eyes-closed”(EC) event induces the “select”command, whereas a “double-blinking”(DB) event functions as the “undo”command. A three-class support vector machine classifier involving EEG signal analysis of three groups of events (“eyes-open” - idle state, EC, and DB) is proposed. The results show that the proposed BCI can achieve an overall accuracy of 93.8% for multi-class classification. The proposed speller is then employed in the online experiment of spelling the word “bcispeller”using a 1 s time window. Consequently, it achieves an average accuracy of 92.3% and an average spelling rate of 5 letters/min. Overall, this paper shows improvement with high accuracy and spelling rate demonstrating the feasibility and reliability of implementing a real-world BCI speller.