Brain-to-speech technology refers to a cutting-edge interface that directly translates neural activity in the brain into spoken language. This technology relies on decoding the patterns of brain signals associated with speech planning, articulation, or imagined speech, allowing individuals to communicate without physically speaking.
- Sensors, such as EEG (electroencephalography) electrodes or invasive devices like ECoG (electrocorticography), record brain activity. These signals are often captured from regions involved in speech processing, such as the motor cortex or Broca's area.
- Advanced algorithms, often powered by machine learning or deep learning models, analyze and interpret the neural signals. These models are trained to identify patterns corresponding to phonemes, words, or complete sentences.
- The decoded neural signals are converted into audible speech using text-to-speech (TTS) engines or other voice synthesis technologies.
- Users may receive feedback to adjust or refine their thought processes, improving the accuracy and fluency of the system over time.
- Assisting individuals with speech impairments caused by conditions like ALS (amyotrophic lateral sclerosis) or stroke.
- Providing a communication channel for people who cannot speak due to physical disabilities.
- Enhancing brain-computer interfaces (BCIs) for efficient, intuitive communication in various settings.
- Neural signals are complex and often noisy, requiring sophisticated processing.
- Each individual’s brain activity is unique, necessitating tailored models.
- Privacy and misuse concerns regarding access to and interpretation of neural data.
Acknowledgement: This project was supported by the Institute of Information & Communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT) (No. RS-2024-00336673, AI Technology for Interactive Communication of Language Impaired Individuals)
