Non-invasive BCIs let you harness tech benefits and enhance cognition without implanting a brain chip.
The problem with conventional non-invasive BCIs is that they are not as accurate as invasive BCIs. They collect data using external sensors that are not in direct contact with brain tissues, and any disturbance in a user’s surroundings could affect their function.
According to the CMU researchers, AI-based deep neural networks can solve this problem. They are more advanced than artificial neural networks used for facial recognition, speech recognition, and various other simple tasks.
A deep neural network has more layers and nodes compared to ANN, and therefore they are used for more complicated tasks. They can allow a BCI to extract accurate results even from complex and large data sets with distortion and noise.
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