A feature enhanced EEG compression model using asymmetric encoding-decoding network.

Objective.Recently, the demand for wearable devices using electroencephalography (EEG) has increased rapidly in many fields. Due to its volume and computation constraints, wearable devices usually compress and transmit EEG to external devices for analysis. However, current EEG compression algorithms are not tailor-made for wearable devices with limited computing and storage. Firstly, the huge amount of parameters makes it difficult to apply in wearable devices; secondly, it is tricky to learn EEG signals' distribution law due to the low signal-to-noise ratio, which leads to excessive reconstruction error and suboptimal compression performance.Approach.Here, a feature enhanced asymmetric encoding-decoding network is proposed. EEG is encoded with a lightweight model, and subsequently decoded with a multi-level feature fusion network by extracting the encoded features deeply and reconstructing the signal through a two-branch structure.Main results.On public EEG datasets, motor imagery and event-related potentials, experimental results show that the proposed method has achieved the state of the art compression performance. In addition, the neural representation analysis and the classification performance of the reconstructed EEG signals also show that our method tends to retain more task-related information as the compression ratio increases and retains reliable discriminative information after EEG compression.Significance.This paper tailors an asymmetric EEG compression method for wearable devices that achieves state-of-the-art compression performance in a lightweight manner, paving the way for the application of EEG-based wearable devices.

Decoding Human Interaction Type from Inter-brain Synchronization by Using EEG Brain Network.

Cooperation and competition are two common forms of interpersonal interactions and exploring inter-brain synchronization in these two forms can help to further deliberate the underlying neural mechanisms of interpersonal interactions. Recently, studies revealed that electrode-paired inter-brain synchronization plays an important role in human interactions. This study investigated the neural correlates of interpersonal synchronization at the brain network scale and interaction type. Firstly, the network-wise inter-brain synchronization (NIBS) index reflecting cross-brain network synchronization from the global brain perspective was advanced. Secondly, statistical analysis demonstrated that there are differences in NIBS activities between cooperative and competitive interactions. And a row-filtered depthwise separable convolution network was proposed to classify the NIBS features. Results of EEG hyper-scanning data showed significant differences in NIBS between cooperative and competitive tasks, and a comparative study manifested that the cross-brain synchronization in cooperative tasks is more consistent than that of competitive tasks. The neural decoder using a modified convolution network achieved a peak accuracy of 96.05% under the binary classification(cooperation vs competition).

Influence of Different pH Values on Gels Produced from Tea Polyphenols and Low Acyl Gellan Gum.

To explore the influence of pH values on the properties of a compound system containing tea polyphenols (TPs) and low acyl gellan gum (LGG), the color, texture characteristics, rheological properties, water holding capacity (WHC), and microstructure of the compound system were measured. The results showed that the pH value noticeably affects the color and WHC of compound gels. Gels from pH 3 to 5 were yellow, gels from pH 6 to 7 were light brown, and gels from pH 8 to 9 were dark brown. The hardness decreased and the springiness increased with an increase in pH. The steady shear results showed that the viscosity of the compound gel solutions with different pH values decreased with increasing shear rates, indicating that all of the compound gel solutions were pseudoplastic fluids. The dynamic frequency results showed that the G' and G″ of the compound gel solutions gradually decreased with increasing pH and that G' was higher than G″. No phase transition occurred in the gel state under heating or cooling conditions at pH 3, indicating that the pH 3 compound gel solution was elastic. The WHC of the pH 3 compound gel was only 79.97% but the WHC of compound gels pH 6 and pH 7 was almost 100%. The network structure of the gels was dense and stable under acidic conditions. The electrostatic repulsion between the carboxyl groups was shielded by H+ with increasing acidity. The three-dimensional network structure was easily formed by an increase in the interactions of the hydrogen bonds.