Towards Feasible Deep Learning Approach Using EEG for Neurodegenerative Diseases - Park, Dogeun; Kim, Ho-Jung; Ju, Young-Gi; Kim, Keun-Tae; Wont, Dong-Ok (The ... International Winter Conference on Brain-Computer Interface, 2025)
In this study, we propose the phase space reconstruction (PSR)-based deep learning framework for enhanced detection of frontotemporal dementia (FTD) utilizing electroen-cephalography (EEG). Our experimental results demonstrate significant improvements over conventional screening methods, particularly the mini-mental state examination (MMSE), which exhibits a considerable false-negative rate of 39.1 % in FTD screening. The proposed framework achieved a remarkable 88.89 % accuracy in identifying previously misclassified FTD cases. The evaluation was conducted using a public dataset comprising 23 FTD patients and 29 healthy controls, wherein our framework consistently outperformed both traditional MMSE and the EEGNet-based approach. Through the integration of PSR techniques with advanced deep learning architectures, our findings suggest that EEG-based computational approaches could serve as robust complementary diagnostic tools in clinical FTD assessment protocols. These results underscore the potential of machine learning applications in enhancing the accuracy and reliability of neurological disorder diagnosis.