Effective Connectivity-Based Multi-View Feature Learning Method for Dementia Diagnosis with FNIRS Signal

Brain computer interface with time-series physiological signal analysis (e.g., EEG and fNIRS) is commonly-used technology for the auxiliary diagnosis of dementia. However, due to the non-stationary, non-linear and low signal-to-noise ratio of time-series signal, as well as the lack of relevant dementia diagnosis datasets, the discriminative feature learning and model construction with time-series signal face great challenges. Therefore, we proposed an Effective Connectivity-based Multi-view Feature learning (ECMFeat) method to realize the auxiliary diagnosis of dementia with fNIRS signal. ECMFeat uses Dynamic Bayesian Inference and EEGNet to learn the effective connectivity and temporal-spatial features of fNIRS signal, respectively. By the weighted fusion of the features from different views, we construct the final auxiliary diagnosis model. Experiments are conducted on the real clinical environment–the First People’s Hospital of Foshan, with the participation of 25 subjects covering three different cognition groups. Experimental results verify the inter-group differences in effective connectivity and the effectiveness of ECMFeat in diagnosing dementia.