A Probabilistic Decoding Approach to a Neural Prosthesis for Speech

摘要：

Neural prosthetic systems for motor control and communication have produced striking results in recent studies with non-human primates and human volunteers. We describe a new approach in our ongoing work toward developing an intracortical neural prosthesis for speech restoration with a 26 year old human volunteer with tetraplegia (including loss of vocal and facial muscle control). We propose to use hidden Markov models (HMMs) to decode neural firing activity in speech motor cortex. We show how classical and recent approaches to automatic speech recognition (ASR) apply directly to the decoding stage of a neural prosthesis. We outline a series of experiments in collecting cortical neural firing data from our human volunteer, and discuss important challenges and considerations in implementing an HMM framework for a neural speech prosthesis.

作者: Brett Matthews Jonathan Kim Jonathan S.Brumberg Mark Clements

作者单位: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia USA Department of Cognitive and Neural Systems, Boston University, Boston, MA USA

会议类型: 国际会议

会议名称: The 4th International Conference on Bioinformatics and Biomedical Engineering(第四届IEEE生物信息与生物医学工程国际会议 iCBBE 2010)

会议地点: 成都

会议语种:英文

页码: 1-4

在线出版日期: 2010-06-18（万方平台首次上网日期，不代表论文的发表时间）