MIT researchers have developed a computer interface that can transcribe words that the user verbalizes internally but does not actually speak aloud.
The system consists of a wearable device and an associated computing system. Electrodes in the device pick up neuromuscular signals in the jaw and face that are triggered by internal verbalizations — saying words “in your head” — but are undetectable to the human eye. The signals are fed to a machine-learning system that has been trained to correlate particular signals with particular words.
The device also includes a pair of bone-conduction headphones, which transmit vibrations through the bones of the face to the inner ear. Because they don’t obstruct the ear canal, the headphones enable the system to convey information to the user without interrupting conversation or otherwise interfering with the user’s auditory experience.
Using the prototype wearable interface, the researchers conducted a usability study in which 10 subjects spent about 15 minutes each customizing the arithmetic application to their own neurophysiology, then spent another 90 minutes using it to execute computations. In that study, the system had an average transcription accuracy of about 92 percent.
But, Kapur says, the system’s performance should improve with more training data, which could be collected during its ordinary use. Although he hasn’t crunched the numbers, he estimates that the better-trained system he uses for demonstrations has an accuracy rate higher than that reported in the usability study.
Sci-fi movies shaped the collective imaginary about neural interfaces as some sort of hardware port or dongle sticking out of the neck and connecting the human brain to the Internet. But that approach, assuming it’s even possible, is still far away into the future.
This approach is much more feasible. Imagine if this object, AlterEgo, would become the main computer peripheral, replacing keyboard and mouse.
The question is not just about the accuracy, but also how its speed compared to existing input methods.
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