BRIEDGE: EEG-Adaptive Edge AI for Multi-Brain to Multi-Robot Interaction
Jinhui Ouyang Mingzhu Wu Xinglin Li Hanhui Deng Di Wu
Recent advances in EEG-based BCI technologies have revealed the potential of
brain-to-robot collaboration through the integration of sensing, computing,
communication, and control. In this paper, we present BRIEDGE as an end-to-end
system for multi-brain to multi-robot interaction through an EEG-adaptive
neural network and an encoding-decoding communication framework, as illustrated
in Fig.1. As depicted, the edge mobile server or edge portable server will
collect EEG data from the users and utilize the EEG-adaptive neural network to
identify the users' intentions. The encoding-decoding communication framework
then encodes the EEG-based semantic information and decodes it into commands in
the process of data transmission. To better extract the joint features of
heterogeneous EEG data as well as enhance classification accuracy, BRIEDGE
introduces an informer-based ProbSparse self-attention mechanism. Meanwhile,
parallel and secure transmissions for multi-user multi-task scenarios under
physical channels are addressed by dynamic autoencoder and autodecoder
communications. From mobile computing and edge AI perspectives, model
compression schemes composed of pruning, weight sharing, and quantization are
also used to deploy lightweight EEG-adaptive models running on both transmitter
and receiver sides. Based on the effectiveness of these components, a code map
representing various commands enables multiple users to control multiple
intelligent agents concurrently. Our experiments in comparison with
state-of-the-art works show that BRIEDGE achieves the best classification
accuracy of heterogeneous EEG data, and more stable performance under noisy
environments.