AI Decodes Brain Activity Into Speech With High Accuracy
A new study demonstrates how a brain-computer interface uses artificial intelligence deep learning to accurately translate brain activity to speech.
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Posted 9/4/2023, 6:35:51 PM
psychologytoday.com
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Researchers at MIT have developed a deep learning model capable of correcting motion artifacts in brain MRI scans, improving diagnostic accuracy and potentially reducing healthcare costs associated with repeated imaging sessions.
Neural networks and deep learning are related AI technologies, with deep learning being a more advanced form of neural networks that requires more time and resources to set up and analyze, while neural networks are more widely used across various AI applications.
Researchers at Stanford University have successfully used an intracortical brain-computer interface (iBCI) combined with artificial intelligence to help a paralyzed woman with amyotrophic lateral sclerosis (ALS) regain the ability to communicate by converting her brain signals into words on a computer screen, marking a significant advancement in restoring rapid communication for nonverbal individuals.
Researchers from Radboud University and the UMC Utrecht have successfully transformed brain signals into audible speech using brain implants and artificial intelligence, achieving an accuracy of 92-100% in predicting spoken words, with the goal of giving a voice back to paralyzed individuals in a locked-in state.
Speech AI is being implemented across various industries, including banking, telecommunications, quick-service restaurants, healthcare, energy, the public sector, automotive, and more, to deliver personalized customer experiences, streamline operations, and enhance overall customer satisfaction.