| dc.description.abstract | Decoding and predicting words from brain
wave patterns is an area of growing interest, with
numerous methodologies and machine learning
techniques being developed to achieve this. Although
various tools and approaches such as convolutional
neural networks (CNNs), support vector machines
(SVMs), and advanced noise reduction techniques
have been introduced, the specific challenges these
methods present have not been thoroughly addressed.
This study aimed to identify and analyze the key
challenges in predicting and decoding words using
brain wave patterns. Our methodology involved
identifying relevant keywords, selecting standard
academic databases like IEEE, Google Scholar, and
Elsevier, and critically reviewing the most pertinent
research papers. Through a comprehensive analysis,
the study highlighted the most common challenges
faced by researchers in this domain, such as individual
variability in brain wave patterns and the limitations
of current machine learning models. Our findings
underscore the need for further research and
development to overcome these challenges, ultimately
enhancing the communication capabilities of
individuals with severe speech impairments. | en_US |
