A team of researchers has achieved a significant breakthrough in neurotechnology by developing a brain-computer interface (BCI) that can convert a person's thoughts directly into text.
The system works by using an electroencephalography (EEG) cap to capture brain signals when a person imagines speaking. These neural patterns are then processed by an artificial intelligence model that has been trained to recognize specific thought patterns associated with speech. A sophisticated language model then reconstructs these decoded signals into coherent sentences with over 70% accuracy.
"We are essentially intercepting signals where the thought is translated into articulation," explained one of the researchers. "What we're decoding is after a thought has happened, after we've decided what to say, after we've decided what words to use and how to move our vocal-tract muscles."
Unlike previous BCI systems that required invasive brain surgery, this technology uses non-invasive EEG, making it more accessible and practical for everyday use. The non-invasive approaches like EEG use electrodes placed on the scalp, offering safety and convenience, though with somewhat attenuated signals compared to invasive methods that place electrodes directly on the brain surface.
The system employs a hybrid brain-computer interface based on a two-stream convolutional neural network, combining multiple paradigms to improve decoding accuracy. This approach has demonstrated comparable performance across different scenarios, verifying its versatility and reliability.
A major challenge in BCIs has been that many users struggle to achieve reliable accuracy levels. Standard models often fail to capture the complexity of brain activity, preventing about 40% of users from reaching 70% accuracy, which is considered a key threshold for effective BCI use. The new system addresses this by adapting to each user's unique brain patterns.
The implications for people with severe neurological conditions are profound. For patients with aphasia or speech difficulties due to brain injury, this BCI can classify and recognize brain signals by identifying specific patterns of EEG activity, enabling them to control computer input devices such as spellers and speech synthesizers using their thoughts.
As research continues, scientists aim to improve the system's accuracy and expand its vocabulary range. The technology represents a significant step toward restoring communication abilities for those who have lost them due to paralysis, stroke, or neurodegenerative diseases.