Multi-modal bioelectrical signal fusion analysis based on different acquisition devices and scene settings: Overview, challenges, and novel orientation

Multi-modal fusion combines multiple modal information to overcome the limitation of incomplete information expressed by a single modality, so as to realize the complementarity of modal information and enhance feature representation. Multi-modal medical signal fusion algorithm and extraction equipment play an important role in improving the recognition accuracy of brain diseases. This paper compared the existing data fusion methods and explored the fusion research of multi-modal bioelectrical signals, including: (1) the challenges and shortcomings in the signal acquisition phase are explored from the biological signal acquisition equipment and scene settings; (2) five multi-modal fusion forms are analyzed; (3) the fusion methods and evaluation indexes are briefly reviewed; (4) the research status and challenges of multi-modal fusion in the field of spatial cognitive impairment and biometrics are explored; (5) the advantages and challenges of multi-modal fusion are described. The conclusion of this review is that the research of multimodal medical signal fusion is in the initial stage, and some studies have proved that multi-modal fusion is meaningful for medical research. However, the fusion algorithm and fusion strategy need to be improved. While learning the relatively perfect image fusion algorithm, we need to develop the fusion algorithm and fusion strategy that is suitable for medical signal and strengthen its feasibility in clinical application.

Automated summary

Multi-modal fusion aims to overcome the limitation of incomplete information expressed by a single modality, enhancing feature representation and accuracy in signal fusion. Medical signal fusion algorithms play a crucial role in improving brain disease recognition, but there is room for improvement in algorithm and strategy development. Research in the field of multi-modal fusion is still in its early stages, highlighting the need to strengthen feasibility through improved fusion algorithms and strategies.