Neural signals, machine learning, and the future of inner speech recognition

Area of Science:

• Neuroscience
• Computer Science
• Artificial Intelligence

Background:

• Inner speech recognition (ISR) is an emerging field with potential for brain-computer interfaces (BCIs) and assistive technologies.
• Decoding inner speech relies on analyzing complex neural signals.
• Machine learning (ML) offers powerful tools for interpreting these signals.

Purpose of the Study:

• To review and synthesize ML techniques for ISR.
• To analyze traditional and deep learning methods for neural signal classification.
• To discuss challenges, preprocessing, and future directions in ML for ISR.

Main Methods:

• Comparative analysis of ML algorithms, including Support Vector Machines (SVMs), random forests, and Convolutional Neural Networks (CNNs).
• Review of signal acquisition and preprocessing techniques for neural data.
• Synthesis of existing ISR methodologies within a mathematical framework.

Main Results:

• Deep learning approaches like CNNs are effective for capturing dynamic, non-linear brain activity patterns in ISR.
• Various ML techniques enhance the analysis and classification of neural signals for ISR.
• Identified challenges in signal acquisition and quality, with discussed preprocessing solutions.

Conclusions:

• ML is critical for advancing ISR, with significant implications for assistive communication, BCIs, and cognitive monitoring.
• Future advancements in ML hold promise for overcoming current technological limitations in ISR.
• This review provides a structured framework and comparative analysis to guide future ISR research.