Closed-loop Brain Machine Interface System: Feedback with Encoding Forelimb Tactile Sensory Responses of Lever Pressing in Awake Rodent

• Main Authors: Pei-Chi Chuang, 莊佩琪
• Other Authors: You-Yin Chen
• Format: Others
• Language: en_US
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/9z77ns

碩士 === 國立陽明大學 === 生物醫學工程學系 === 106 === The encoder in closed-loop brain-machine interfaces (BMIs) plays an important role in establishing a direct communication link between the brain and the external world. There are two methods to build up an encoder, the psychometric equivalence approach and neurophysiological approach. The psychometric equivalence function (PEF) is established by assessing the same performance of detection toward both different parameter of intracortical microstimulation (ICMS) and the mechanical stimulation. However, it’s hard to observe the quality and the quantity of the sensation evoked by ICMS. In the recent research, scientists found out that ICMS could elicit the naturalistic cortical response. Besides, somatosensory cortex, whether in neural firing rate or local field potentials (LFPs), is sensitive to the different velocity of tactile stimulus. As the result, in our research, we propose a stimulus evoked potential (SEP)-based encoder of sensory cortical system which was built up by the concept of PEF. In the past research, compare with firing rate, the LFPs based decoding model is more robust in stimulus decoding for its comprehensive information. For establishing a stable and precise sensory SEP-based encoder of sensory cortical system for the real-time closed-loop BMI model, LFPs would be more suitable. In our study, we’re going to build up a SEP-based encoder in behavioral rat by recording the evoked potential from acceleration stimulus of lever-pressing and the ICMS. By extracting the features from LFP, we could find the stimulus-correlated features for the SEP-based encoder. The SEP-based encoder be established by the linear regression models, logistic regression model, and exponential regression model. Furthermore, we would discuss the result of our SEP-based encoder, and compare the stability and precision between spike-based and SEP-based encoder.