From Neurons to Networks: The Impact of Neuromorphic Computing on BCI Development

This review paper explores various brain-computerinterface (BCI)methodologies and examines their integration withneuromorphic computing to enhance functionality, efficiency, andadaptability. BCIs serve as a critical bridge between the brain andexternal devices, enabling applications in neuroprosthetics,assistive communication, and cognitive augmentation.Meanwhile, neuromorphic computing, which mimics biologicalneural processes, offers promising solutionsfor improving real-time signal processing, energy efficiency, andadaptive learning within BCI systems.This study provides a comparative analysis of different BCIapproaches—including invasive, partially invasive, and noninvasive methods—assessing their architectures, signalacquisition techniques, and processing capabilities. Specialattention is given to cutting- edge BCI technologies such asSynchron’s Stentrode, Blackrock NeuroPort, along with a casestudy on the ROLLS neuromorphic processor for bidirectionalBCI applications. The findings indicate that integratingneuromorphic computing with BCIsenhances neural signal processing, reduces power consumption,and enables real-time adaptation, making it a promising avenue fornext-generation neuroprosthetic systems.As a review paper, this work consolidates existing research andhighlights key challenges, such as hardware constraints, signalfidelity, and ethical considerations. The study underscores theneed for further empirical validation and large-scale clinical trialsto realize the full potential of neuromorphic-enhanced BCIs. Bysummarizing the state of the field and discussing future directions,this paper contributes to the ongoing development of moreadvanced and scalable brain-machine interface technologies