MC-FAW: A Multi-Scale Convolutional Feature Adaptive Weighting Fusion Network for Detecting Disorders of Consciousness

The clinical assessment of patients with disorders of consciousness (DoC) still faces challenges due to the lack of objective and accurate methods. To address this issue, this paper proposes a multi-scale convolutional feature adaptive weighting (MC-FAW) fusion network, which aims to integrate multi-scale electroencephalogram (EEG) functional connectivity features to improve the classification performance of consciousness states. Based on resting-state EEG data from 15 healthy controls (HC), 15 patients in a minimally conscious state (MCS), and 17 patients in a vegetative state (VS), the MC-FAW model effectively captures multi-scale features of brain neural activity and dynamically weights these features to achieve a more comprehensive and accurate assessment of consciousness. The classification results demonstrate that this method achieves classification accuracies of 96.54% for distinguishing HC from MCS, 98.47% for HC from VS, and 85.25% for MCS from VS. These results indicate that our method has the potential to improve the objectivity and accuracy of clinical diagnosis, thereby providing important support for clinical decision-making.