Researchers at Meta AI have developed a non-invasive method to decode speech from brain activity. By using magneto-encephalography (MEG) and electroencephalography (EEG), they recorded the brain waves of volunteers and identified the words associated with specific brain wave patterns. Although further work is needed to enable communication based on thought recognition, the study shows promise for assisting individuals with limited motor skills, such as ALS patients. The research findings have been made available for further exploration by other researchers. DailyAI has more details on the study.
AI Decodes Speech from Non-Invasive Brain Recordings
Researchers at Meta AI have made significant progress in decoding speech from brain activity without invasive brain-computer implants. By using magneto-encephalography (MEG) and electroencephalography (EEG) to record brain waves, they were able to identify specific words associated with those brain wave patterns.
This breakthrough has important implications for individuals with severely limited motor skills, such as ALS sufferers, who struggle to communicate effectively. Currently, individuals like Stephen Hawking rely on painstaking methods like eye movements or muscle twitches to construct sentences.
The researchers achieved an average accuracy of 41% and a remarkable 95.9% accuracy with their best participants. While the study focused on a limited set of words, the potential for broader communication is promising. By training a neural network to interpret brain waves, it may be possible to understand the words that an ALS sufferer is thinking of.
Although the process requires the use of an MEG device, the non-invasive nature of the research is a significant advantage. However, the results from EEG measurements were not as successful.
Meta AI has made their model and data publicly available, encouraging further research and development in this field.
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