JMIR Publications

ABSTRACT

Background:

Background:

Brain-Computer Interface (BCI) closed-loop systems have emerged as a promising tool in healthcare and wellness monitoring, particularly in neurorehabilitation and cognitive assessment. With the increasing burden of neurological disorders, including Alzheimer’s Disease and Related Dementias (AD/ADRD), there is a critical need for real-time, non-invasive monitoring technologies. BCIs enable direct communication between the brain and external devices, leveraging artificial intelligence (AI) and machine learning (ML) to interpret neural signals. However, challenges such as signal noise, data processing limitations, and privacy concerns hinder widespread implementation. This review explores the integration of ML and AI in BCI closed-loop systems, evaluating their effectiveness in improving neurological assessments and interventions.

Objective:

Objective:

The primary objective of this study is to investigate the role of ML and AI in enhancing BCI closed-loop systems for healthcare applications. Specifically, we aim to analyze the methods and parameters used in these systems, assess the effectiveness of different AI and ML techniques, identify key challenges in their development and implementation, and propose a framework for utilizing BCIs in the longitudinal monitoring of AD/ADRD patients. By addressing these aspects, this study seeks to provide a comprehensive overview of the potential and limitations of AI-driven BCIs in neurological healthcare.

Methods:

Methods:

A systematic literature review was conducted following PRISMA guidelines, focusing on studies published between 2019 and 2024. Research articles were sourced from PubMed, IEEE, ACM, and Scopus using predefined keywords related to BCIs, AI, and AD/ADRD. A total of 220 papers were initially identified, with 18 meeting the final inclusion criteria. Data extraction followed a structured matrix approach, categorizing studies based on methods, ML algorithms, limitations, and proposed solutions. A comparative analysis was performed to synthesize key findings and trends in AI-enhanced BCI systems for neurorehabilitation and cognitive monitoring.

Results:

Results:

The review identified several ML techniques, including Transfer Learning, Support Vector Machines, and Convolutional Neural Networks, that enhance BCI closed-loop performance. These methods improve signal classification, feature extraction, and real-time adaptability, enabling accurate monitoring of cognitive states. However, challenges such as long calibration sessions, computational costs, data security risks, and variability in neural signals were also highlighted. To address these issues, emerging solutions such as improved sensor technology, efficient calibration protocols, and advanced AI-driven decoding models are being explored. Additionally, BCIs show potential for real-time alert systems that support caregivers in managing AD/ADRD patients.

Conclusions:

Conclusions:

BCI closed-loop systems, when integrated with AI and ML, offer significant advancements in neurological healthcare, particularly in AD/ADRD monitoring and neurorehabilitation. Despite their potential, challenges related to data accuracy, security, and scalability must be addressed for widespread clinical adoption. Future research should focus on refining AI models, improving real-time data processing, and enhancing user accessibility. With continued advancements, AI-powered BCIs can revolutionize personalized healthcare by providing continuous, adaptive monitoring and intervention for patients with neurological disorders.