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.
References :
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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.