Machine-Learning Methods for Speech and Handwriting Detection Using Neural Signals: A Review

Machine-Learning Methods for Speech and Handwriting Detection Using Neural Signals: A Review

Brain-Computer Interfaces (BCIs) have become increasingly popular in recent years due to their potential applications in diverse fields, ranging from the medical sector (people with motor and/or communication disabilities), cognitive training, gaming, and Augmented Reality/Virtual Reality (AR/VR), a...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 23; no. 12; p. 5575
Main Authors: Sen, Ovishake, Sheehan, Anna M, Raman, Pranay R, Khara, Kabir S, Khalifa, Adam, Chatterjee, Baibhab
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 14-06-2023
MDPI
Subjects:
Analysis
Aphasia
Augmented Reality
Brain research
Communication
Communications software
Electrodes
Electroencephalography
Handwriting
Handwriting recognition
Human-computer interface
Internet of Things
Machine learning
neural signals
Review
Signal processing
Signal to noise ratio
Software
Speaking
Speech
Speech recognition
Surgery
Virtual reality
Writing
speech recognition
handwriting recognition
machine learning
signal processing
neural signals
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Summary:Brain-Computer Interfaces (BCIs) have become increasingly popular in recent years due to their potential applications in diverse fields, ranging from the medical sector (people with motor and/or communication disabilities), cognitive training, gaming, and Augmented Reality/Virtual Reality (AR/VR), among other areas. BCI which can decode and recognize neural signals involved in speech and handwriting has the potential to greatly assist individuals with severe motor impairments in their communication and interaction needs. Innovative and cutting-edge advancements in this field have the potential to develop a highly accessible and interactive communication platform for these people. The purpose of this review paper is to analyze the existing research on handwriting and speech recognition from neural signals. So that the new researchers who are interested in this field can gain thorough knowledge in this research area. The current research on neural signal-based recognition of handwriting and speech has been categorized into two main types: invasive and non-invasive studies. We have examined the latest papers on converting speech-activity-based neural signals and handwriting-activity-based neural signals into text data. The methods of extracting data from the brain have also been discussed in this review. Additionally, this review includes a brief summary of the datasets, preprocessing techniques, and methods used in these studies, which were published between 2014 and 2022. This review aims to provide a comprehensive summary of the methodologies used in the current literature on neural signal-based recognition of handwriting and speech. In essence, this article is intended to serve as a valuable resource for future researchers who wish to investigate neural signal-based machine-learning methods in their work.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s23125575