“This study demonstrates the potential of using DL-based decoders for online BCI decoding in challenging tasks and shows that subjects can achieve strong performance with these models,” wrote corresponding author Dr. Ben He, Trustee Professor of Biomedical Engineering, Professor of the Neuroscience Institute, and Professor by courtesy of Electrical and Computer Engineering at Carnegie Mellon University, along with co-authors Dylan Forenzo, Hao Zhua, Jenn Shanahan, and Jaehyun Lima.
Decades prior, the first people to receive brain-computer interfaces were an ALS patient and a brainstem stroke survivor. In 1998, Emory University neurosurgeon Dr. Roy E. Bakay andDr. Phillip R. Kennedy implanted neurotrophic electrodes in the motor cortex area of the patients which enabled them to control a cursor on an external computer using their thoughts to prompt computerized speech.to decode complex brain activity and predict the user’s intended action.
“Among noninvasive methods, EEG is particularly well suited for BCI systems due to its high temporal resolution, portability, and relatively low cost,” the CMU researchers wrote. The scientists compared their newly developed AI deep learning algorithm to existing deep learning decoders with encouraging results.
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