A bidirectional interaction-based hybrid network architecture for EEG cognitive recognition

BACKGROUND AND OBJECTIVE

Extracting cognitive representation and computational representation information simultaneously from electroencephalography (EEG) data and constructing corresponding information interaction models can effectively improve the recognition capability of brain cognitive status. However, due to the huge gap in the interaction between the two types of information, existing studies have yet to consider the advantages of the interaction of both.

METHODS

This paper introduces a novel architecture named the bidirectional interaction-based hybrid network (BIHN) for EEG cognitive recognition. BIHN consists of two networks: a cognitive-based network named CogN (e.g., graph convolution network, GCN; capsule network, CapsNet) and a computing-based network named ComN (e.g., EEGNet). CogN is responsible for extracting cognitive representation features from EEG data, while ComN is responsible for extracting computational representation features. Additionally, a bidirectional distillation-based coadaptation (BDC) algorithm is proposed to facilitate information interaction between CogN and ComN to realize the coadaptation of the two networks through bidirectional closed-loop feedback.

RESULTS

Cross-subject cognitive recognition experiments were performed on the Fatigue-Awake EEG dataset (FAAD, 2-class classification) and SEED dataset (3-class classification), and hybrid network pairs of GCN + EEGNet and CapsNet + EEGNet were verified. The proposed method achieved average accuracies of 78.76% (GCN + EEGNet) and 77.58% (CapsNet + EEGNet) on FAAD and 55.38% (GCN + EEGNet) and 55.10% (CapsNet + EEGNet) on SEED, outperforming the hybrid networks without the bidirectional interaction strategy.

CONCLUSIONS

Experimental results show that BIHN can achieve superior performance on two EEG datasets and enhance the ability of both CogN and ComN in EEG processing as well as cognitive recognition. We also validated its effectiveness with different hybrid network pairs. The proposed method could greatly promote the development of brain-computer collaborative intelligence.

Microbiota-gut-brain axis and nutritional strategy under heat stress

Heat stress is a very universal stress event in recent years. Various lines of evidence in the past literatures indicate that gut microbiota composition is susceptible to variable temperature. A varied microbiota is necessary for optimal regulation of host signaling pathways and disrupting microbiota-host homeostasis that induces disease pathology. The microbiota–gut–brain axis involves an interactive mode of communication between the microbes colonizing the gut and brain function. This review summarizes the effects of heat stress on intestinal function and microbiota–gut–brain axis. Heat stress negatively affects intestinal immunity and barrier functions. Microbiota-gut-brain axis is involved in the homeostasis of the gut microbiota, at the same time, heat stress affects the metabolites of microbiota which could alter the function of microbiota–gut–brain axis. We aim to bridge the evidence that the microbiota is adapted to survive and thrive in an extreme environment. Additionally, nutritional strategies for alleviating intestinal heat stress are introduced.