Hybrid EEG–fNIRS-Based Eight-Command Decoding for BCI: Application to Quadcopter Control

TLDR

The study introduces a hybrid EEG–fNIRS brain‑computer interface that decodes eight distinct brain commands from frontal cortical activity. Eight commands are extracted by fNIRS from the prefrontal cortex using peak, minimum, and mean ΔHbO features over a 2‑second window for mental arithmetic, counting, rotation, and word formation, while EEG from frontal, parietal, and visual areas captures eye blinks and movements using peak counts and mean amplitude over a 1‑second window, and the resulting commands are applied to control a quadcopter in open space. The hybrid system achieved 75.6 % accuracy for four fNIRS‑based commands and 86 % for four EEG‑based commands, demonstrating real‑time quadcopter control with eight brain‑derived commands.

Abstract

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 ΔHbO values within a 2-s moving window. In the case of EEG, two eyeblinks, three eyeblinks, 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 1 s 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.