Wavelet-Based Cross-Frequency and Cross-Region Interaction Convolutional Neural Network for Working Memory Load Level Detection

The detection of Working Memory Load WML plays a crucial role in neu-rofeedback processes and the treatment of disorders such as ADHD. How-ever, the performance of existing detection methods remains unsatisfactory. Neuropsychology research indicates that high-level cognitive processes are driven by both inter-regional collaborations across different brain functional areas and cross-frequency couplings. To comprehensively capture brain ac-tivities spanning both frequency domains and intra inter-regional interac-tions, we propose a novel cognitively-inspired neural network – the Wavelet-based Cross-Frequency and Cross-Region Interaction Convolutional Neural Network CFCRNet – for WML decoding. Specifically, CFCRNet first em-ploys predefined wavelet kernels to perform 1D convolution for time-frequency feature extraction, followed by multi-branch learning to model cross-frequency feature coupling with varying scales, and finally integrates intra- and inter-regional information interactions through spatial attention mechanisms. This architecture systematically fuses neurophysiologically meaningful cross-frequency coupling mechanisms with functional integra-tion principles across brain regions, constructing a network model capable of simultaneously resolving dynamic characteristics of neural signals across dif-ferent frequency bands and complex interactive relationships within between functional areas. Experimental validation on our collected working memory dataset and public benchmarks demonstrates that incorporating neuroscien-tific priors into neural network design enhances classification performance. Collectively, our findings establish an advanced framework for accurate WML detection that can be extended to explore detection tasks associated with other cognitive behaviors and neurological disorders.