Decoding Brain-Controlled Intention for UAVs and IVs Based on Lightweight Network

Brain–computer-interface (BCI) plays an important role in the Internet of Things (IoT). With the development of electroencephalogram (EEG) signal processing and deep learning, researchers are beginning to decode intention for controlling smart movable devices by EEG signals, such as unmanned aerial vehicles (UAVs) and intelligent vehicles (IVs). However, the current related studies less consider the generic decoding of the two, and the performance of the adopted models needs to be improved in terms of both lightness and accuracy. In this article, we are dedicated to the study of a generic brain-controlled intention decoding task serving UAVs and IVs. We adopt three different data sets, encompassing different BCI paradigms. A lightweight self-attention enhancement model is proposed, which incorporates self-attention into the model for feature enhancement. Experimental results show that our method outperforms the baselines on brain-controlled intention decoding for all three data sets. This research is useful for the work in the field of brain-controlled intention decoding, and also provides new ideas for lightweight network-based control of UAVs and IVs.