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

Computer Science

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

A. T. Chowdhury, A. Hassanein, et al.

Inner speech recognition (ISR) aims to decode covert thought from neural signals using machine learning—ranging from SVMs and random forests to CNNs—combined with signal-preprocessing and cognitive modeling. This review synthesizes ISR methodologies, evaluates challenges and limitations, and outlines future applications in BCIs and assistive communication. Research conducted by Authors present in <Authors> tag.

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~3 min • Beginner • English
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