A novel lightweight dynamic focusing convolutional neural network LAND-FCNN for EEG emotion recognition

Abstract

The inefficiency of model inference and the limited sample data are significant issues in electroencephalogram (EEG) emotion recognition models based on neural networks. Therefore, we propose a novel lightweight adaptive dynamic focusing convolutional neural network (LAND-FCNN). We use Partial Convolution (PConv) and Batch interactive attention in constructing the Backbone network. PConv reduces the computational cost by selectively extracting features from a subset of input channels, utilizing feature graph redundancy. Batch interactive attention establishes connections between various types of samples to address sample scarcity without relying on data augmentation. Then Backbone serves as the encoder for the Glance and Focus Dynamic Inference Networks (GFnet). The patch proposal network in GFnet adaptively captures task-relevant areas of each sample, allowing encoders to process only those areas. This increases inference speed and ensures reliable results with minimal computational effort. Extensive validation of diverse datasets with varying distributions confirms the efficacy and precision of LAND-FCNN.