RT-Blink: A Method Toward Real-Time Blink Detection From Single Frontal EEG Signal

Abstract

Eye blinks take an important role for electroencephalography (EEG) signals that on one hand, they can severely impact the EEG signals, and on the other hand, they may provide useful information for brain–computer interface (BCI) and scientific applications. In particular, it is challenging to detect blinks in real time from a single channel of EEG signals. In this work, we propose a short windowed and random forest based method toward the Real-Time Blink detection (RT-Blink), which balances the processing granularity and computation complexity. RT-Blink uses a potential blink (PB) boundary detecting algorithm and a pretrained random forest (RF) model with a set of features, including sample entropy (SampEn), standard deviation (SD), range of amplitude (RA), and rate of grade (RG), which enables fully automated identification of the duration of blinks from a single-channel EEG signal. The window size of RT-Blink can be adjusted according to the processing speed of the underlying hardware and the requirement of real time, which can be down to one-point EEG data. RT-Blink provides a scalable framework, which can be realized in software, hardware accelerator, or their mixture. Using EEG data contaminated by blinks, we show that RT-Blink achieves 96.54% and 91.25% for average sensitivity and precision, respectively. The time window is 60 ms based on our computer, with minimized overlapping for blink boundary detection. The average processing time is 5.07 ms for each time window, with average 1.65 ms of SD for all the EEG test cases. The results suggest that RT-Blink has promising potential toward real-time EEG applications.