A hybrid EEG-fNIRS brain-computer interface achieved an average classification accuracy of 76.5% for four fNIRS-based commands and 86% for four EEG-based commands, enabling quadcopter control.
A hybrid EEG-fNIRS interface can successfully decode eight active brain commands to control a quadcopter in real-time with high accuracy.
In this paper, a hybrid electroencephalography-functional near infrared spectroscopy (EEG-fNIRS) scheme to decode eight active brain commands from the frontal brain region for brain-computer interface is presented. A total of eight commands are decoded by fNIRS, as positioned on the prefrontal cortex, and by EEG, around the frontal, parietal, and visual cortices. Mental arithmetic, mental counting, mental rotation, and word formation tasks are decoded with fNIRS, in which the selected features for classification and command generation are the peak, minimum, and mean ∆HbO values within a 2-second moving window. In the case of EEG, two eye-blinks, three eye-blinks, and eye movement in the up/down and left/right directions are used for four-command generation. The features in this case are the number of peaks and the mean of the EEG signal during one second window. We tested the generated commands on a quadcopter in an open space. An average accuracy of 75.6% was achieved with fNIRS for four-command decoding and 86% with EEG for another four-command decoding. The testing results show the possibility of controlling a quadcopter online and in real-time using eight commands from the prefrontal and frontal cortices via the proposed hybrid EEG-fNIRS interface.
Khan et al. (Fri,) conducted a other in Healthy adults (n=10). Hybrid EEG-fNIRS BCI was evaluated on Classification accuracy of 8 active brain commands. A hybrid EEG-fNIRS brain-computer interface achieved an average classification accuracy of 76.5% for four fNIRS-based commands and 86% for four EEG-based commands, enabling quadcopter control.