Brain-computer interfaces, or BCIs, hold immense potential for individuals with a wide range of neurological conditions, but the road to implementation is long and nuanced for both the invasive and noninvasive versions of the technology. Bin He of Carnegie Mellon University is highly driven to improve noninvasive BCIs, and his lab uses an innovative electroencephalogram (EEG) wearable to push the boundaries of what’s possible. For the first time on record, the group successfully integrated a novel focused ultrasound stimulation to realize bidirectional BCI that both encodes and decodes brain waves using machine learning in a study with 25 human subjects. This work opens up a new avenue to significantly enhance not only the signal quality, but also, overall nonivasive BCI performance by stimulating targeted neural circuits.

Noninvasive BCI is lauded for its merits of being cheap, safe, and virtually applicable to everyone, but because signals are recorded over the scalp versus inside the brain, low signal quality presents some limitations. The He group is exploring ways to improve the effectiveness of noninvasive BCIs and, over time, has used deep learning approaches to decode what an individual was thinking and then facilitate control of a cursor or robotic arm… Continue reading.