A Detailed Overview of Brain-Computer and Brain-Machine Interfaces


Authors : Uday S. Yeshi; Atharva A. Khode; Shashvat Sangle; Surabhi Vishwasrao; Gautami Salve

Volume/Issue : Volume 9 - 2024, Issue 12 - December

Google Scholar : https://tinyurl.com/yh4dux22

Scribd : https://tinyurl.com/3jhthbsn

DOI : https://doi.org/10.5281/zenodo.14598593

Abstract : Brain-Computer Interfaces (BCIs) and Brain- Machine Interfaces (BMIs) represent trans-formative technologies capable of enabling communication and control for individuals with severe disabilities. These systems employ a series of intricate processes, including signal acquisition, feature extraction, feature translation, and device output, to translate neural activity into actionable commands. While BCIs predominantly focus on noninvasive applications, BMIs often involve invasive methods, with preclinical studies on animal models advancing the un- derstanding of neural decoding. Despite their promise, several technical challenges remain, including signal reliability, adaptive user interfaces, feedback mechanisms, and economic scalability. Addressing these gaps through interdisciplinary research is critical to unlocking the full potential of BCIs and BMIs for real-world applications. This paper reviews current methodologies, highlights technical limitations, and proposes future directions to enhance the reliability, usability, and accessibility of these groundbreaking technologies.

Keywords : Brain-Computer Interfaces (BCIs), Brain- Machine Interfaces (BMIs), Neural Decoding, Signal Acquisition, Feature Extraction, Device Output, Invasive Technologies, Non- Invasive Technologies, Technical Challenges, Feedback Mechanisms, Economic Feasibility, Interdisciplinary Research, Real-World Applications.

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Brain-Computer Interfaces (BCIs) and Brain- Machine Interfaces (BMIs) represent trans-formative technologies capable of enabling communication and control for individuals with severe disabilities. These systems employ a series of intricate processes, including signal acquisition, feature extraction, feature translation, and device output, to translate neural activity into actionable commands. While BCIs predominantly focus on noninvasive applications, BMIs often involve invasive methods, with preclinical studies on animal models advancing the un- derstanding of neural decoding. Despite their promise, several technical challenges remain, including signal reliability, adaptive user interfaces, feedback mechanisms, and economic scalability. Addressing these gaps through interdisciplinary research is critical to unlocking the full potential of BCIs and BMIs for real-world applications. This paper reviews current methodologies, highlights technical limitations, and proposes future directions to enhance the reliability, usability, and accessibility of these groundbreaking technologies.

Keywords : Brain-Computer Interfaces (BCIs), Brain- Machine Interfaces (BMIs), Neural Decoding, Signal Acquisition, Feature Extraction, Device Output, Invasive Technologies, Non- Invasive Technologies, Technical Challenges, Feedback Mechanisms, Economic Feasibility, Interdisciplinary Research, Real-World Applications.

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