Depression recognition from EEG based on nonlinear analysis and adaptive feature fusion

Machine learning techniques have recently shown significant promise in electroencephalograph (EEG)-based depression recognition. However, existing methods often rely on simple feature concatenation to fuse information from multiple perspectives, failing to adequately exploit the complementarity of heterogeneous features. To this end, we propose a novel affective computing framework for identifying depression that integrates nonlinear analysis with adaptive feature coupling. The framework first uses different entropy measures to effectively characterize the intricate and chaotic dynamics present in EEG signals. Then, a new fusion strategy based on weighted average is proposed to adaptively aggregate complementary information among multi-view features. More importantly, this strategy can mitigate the influence of redundant features. Experimental results on publicly available datasets show that our methodology can dramatically improve the accuracy of depression recognition. Meanwhile, visualization analysis reveals that compared with healthy controls, patients with depression exhibit lower EEG entropy values, reflecting reduced complexity in their brain activity. Due to the good performance of the framework, this study provides important insights into the usefulness of nonlinear analysis and adaptive feature fusion in EEG decoding tasks.