Research by David A. Moses and colleagues studies the concept of Neuroprosthesis for decoding speech in a paralyzed person with anarthria. Those who are paralyzed may benefit from technology that enables them to communicate again. It's conceivable that a system that decodes words and phrases directly from such people's cerebral cortex activity would be a step forward. In patient’s whose spastic quadriparesis and anarthria were caused by a brain stem stroke, the authors recorded 22 hours of brain activity over 48 sessions as a participant attempted to pronounce words from a vocabulary list of 50. Deep-learning algorithms identified and classified words by analyzing brain activity patterns. These computational models and a natural-language model were employed in his research to compute next-word probabilities based on words that came before them in a sequence. With a 25.6 percent word error rate, words were decoded from brain activity in real time (Moses et al., 2021).
The authors were able to detect 98 percent of individuals' attempts to create particular phrases by monitoring their brain activity for 81 weeks. Words and phrases were decoded directly from cerebral activity during attempted speaking in persons suffering from anarthria (the inability to talk) and spastic quadriparesis as a result of a brain-stem stroke. Many disabled individuals may communicate by using restricted lip motions and undifferentiated vocalizations. Due to severe movement limitations, paralyzed individuals may be unable to utilize assistive equipment.
Individuals with speech problems may now use a computer cursor to spell out messages, thanks to brain–computer link based technology. Letter-by-letter selection may take a long time when using brain signal recordings to drive interfaces. It may be more efficient and natural to decode whole words from voice control areas. Th authors now have a better understanding of how the sensorimotor cortex's speech-controlling area orchestrates rapid vocal tract articulatory movements. These advances in neurobiology and machine learning have shown that speech can be decoded from brain activity in individuals who do not have speech difficulties.
As shown in this work, previously reported brain-computer interface systems need daily calibration of decoding models prior to user deployment, which may increase decoder performance variability across days and impede long-term interface acceptance for real-world use. Due of the high signal stability and potential for large data accumulation associated with electrocorticographic recordings, he used cortical activity captured by implanted electrodes over months of recording to train his decoding algorithms. As a result, high-density electrocorticography may be an ideal option for long-term neuroprosthetic direct speech applications such as direct voice prosthesis (Moses et al., 2021).
References
MOSES, D. A., METZGER, S. L., LIU, J. R., ANUMANCHIPALLI, G. K., MAKIN, J. G., SUN, P. F., CHARTIER, J., DOUGHERTY, M. E., LIU, P. M. & ABRAMS, G. M. J. N. E. J. O. M. 2021. Neuroprosthesis for decoding speech in a paralyzed person with anarthria. 385, 217-227.
Individuals with speech problems may now use a computer cursor to spell out messages, thanks to brain–computer link based technology. Letter-by-letter selection may take a long time when using brain signal recordings to drive interfaces. It may be more efficient and natural to decode whole words from voice control areas. Th authors now have a better understanding of how the sensorimotor cortex's speech-controlling area orchestrates rapid vocal tract articulatory movements. These advances in neurobiology and machine learning have shown that speech can be decoded from brain activity in individuals who do not have speech difficulties.
As shown in this work, previously reported brain-computer interface systems need daily calibration of decoding models prior to user deployment, which may increase decoder performance variability across days and impede long-term interface acceptance for real-world use. Due of the high signal stability and potential for large data accumulation associated with electrocorticographic recordings, he used cortical activity captured by implanted electrodes over months of recording to train his decoding algorithms. As a result, high-density electrocorticography may be an ideal option for long-term neuroprosthetic direct speech applications such as direct voice prosthesis (Moses et al., 2021).
References
MOSES, D. A., METZGER, S. L., LIU, J. R., ANUMANCHIPALLI, G. K., MAKIN, J. G., SUN, P. F., CHARTIER, J., DOUGHERTY, M. E., LIU, P. M. & ABRAMS, G. M. J. N. E. J. O. M. 2021. Neuroprosthesis for decoding speech in a paralyzed person with anarthria. 385, 217-227.
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