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In this paper, we propose a highly accurate and fast spelling system that employs multi-modal electroencephalography-electrooculography (EEG-EOG) signals and visual feedback technology. Over the last 20 years, various types of speller systems have been developed in brain-computer interface and EOG/eye-tracking research; however, these conventional systems have a tradeoff between the spelling accuracy (or decoding) and typing speed. Healthy users and physically challenged participants, in particular, may become exhausted quickly; thus, there is a need for a speller system with fast typing speed while retaining a high level of spelling accuracy. In this paper, we propose the first hybrid speller system that combines EEG and EOG signals with visual feedback technology so that the user and the speller system can act cooperatively for optimal decision-making. The proposed spelling system consists of a classic row-column event-related potential (ERP) speller, an EOG command detector, and visual feedback modules. First, the online ERP speller calculates classification probabilities for all candidate characters from the EEG epochs. Second, characters are sorted by their probability, and the characters with the highest probabilities are highlighted as visual feedback within the row-column spelling layout. Finally, the user can actively select the character as the target by generating an EOG command. The proposed system shows 97.6% spelling accuracy and an information transfer rate of 39.6 (±13.2) [bits/min] across 20 participants. In our extended experiment, we redesigned the visual feedback and minimized the number of channels (four channels) in order to enhance the speller performance and increase usability. Most importantly, a new weighted strategy resulted in 100% accuracy and a 57.8 (±23.6) [bits/min] information transfer rate across six participants. This paper demonstrates that the proposed system can provide a reliable communication channel for practical speller applications and may be used to supplement existing systems. [ABSTRACT FROM AUTHOR]

Most event-related potential (ERP)-based brain–computer interface (BCI) spellers primarily use matrix layouts and generally require moderate eye movement for successful operation. The fundamental objective of this paper is to enhance the perceptibility of target characters by introducing motion stimuli to classical rapid serial visual presentation (RSVP) spellers that do not require any eye movement, thereby applying them to paralyzed patients with oculomotor dysfunctions. To test the feasibility of the proposed motion-based RSVP paradigm, we implemented three RSVP spellers: 1) fixed-direction motion (FM-RSVP); 2) random-direction motion (RM-RSVP); and 3) (the conventional) non-motion stimulation (NM-RSVP), and evaluated the effect of the three different stimulation methods on spelling performance. The two motion-based stimulation methods, FM- and RM-RSVP, showed shorter P300 latency and higher P300 amplitudes (i.e., 360.4–379.6 ms; 5.5867– 5.7662~\mu V ) than the NM-RSVP (i.e., 480.4 ms; 4.7426~\mu V ). This led to higher and more stable performances for FM- and RM-RSVP spellers than NM-RSVP speller (i.e., 79.06±6.45% for NM-RSVP, 90.60±2.98% for RM-RSVP, and 92.74±2.55% for FM-RSVP). In particular, the proposed motion-based RSVP paradigm was significantly beneficial for about half of the subjects who might not accurately perceive rapidly presented static stimuli. These results indicate that the use of proposed motion-based RSVP paradigm is more beneficial for target recognition when developing BCI applications for severely paralyzed patients with complex ocular dysfunctions. [ABSTRACT FROM PUBLISHER]