Feasibility analysis of genetically-encoded calcium indicators as a neural signal source for all-optical brain-machine interfaces

2017 ◽  
Author(s):  
Xulu Sun ◽  
Jonathan C. Kao ◽  
James H. Marshel ◽  
Stephen I. Ryu ◽  
Krishna V. Shenoy
Keyword(s):  
Feasibility Analysis ◽  
Signal Source ◽  
Brain Machine Interfaces ◽  
Neural Signal ◽  
Calcium Indicators ◽  
All Optical ◽  
Genetically Encoded Calcium Indicators

scholarly journals Gesture Decoding Using ECoG Signals from Human Sensorimotor Cortex: A Pilot Study

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Yue Li ◽  
Shaomin Zhang ◽  
Yile Jin ◽  
Bangyu Cai ◽  
Marco Controzzi ◽  
...  
Keyword(s):  
Sensorimotor Cortex ◽  
Signal Source ◽  
Central Sulcus ◽  
Brain Machine Interfaces ◽  
Hand Gestures ◽  
Neural Signal ◽  
Hybrid Frequency ◽  
Offline Analysis ◽  
Online Tests ◽  
Decoding Accuracy

Electrocorticography (ECoG) has been demonstrated as a promising neural signal source for developing brain-machine interfaces (BMIs). However, many concerns about the disadvantages brought by large craniotomy for implanting the ECoG grid limit the clinical translation of ECoG-based BMIs. In this study, we collected clinical ECoG signals from the sensorimotor cortex of three epileptic participants when they performed hand gestures. The ECoG power spectrum in hybrid frequency bands was extracted to build a synchronous real-time BMI system. High decoding accuracy of the three gestures was achieved in both offline analysis (85.7%, 84.5%, and 69.7%) and online tests (80% and 82%, tested on two participants only). We found that the decoding performance was maintained even with a subset of channels selected by a greedy algorithm. More importantly, these selected channels were mostly distributed along the central sulcus and clustered in the area of 3 interelectrode squares. Our findings of the reduced and clustered distribution of ECoG channels further supported the feasibility of clinically implementing the ECoG-based BMI system for the control of hand gestures.

Genetically Encoded Calcium Indicators

2008 ◽  
Vol 108 (5) ◽  
pp. 1550-1564 ◽  
Author(s):  
Marco Mank ◽  
Oliver Griesbeck
Keyword(s):  
Calcium Indicators ◽  
Genetically Encoded Calcium Indicators

scholarly journals A Reconfigurable Neural Signal Processor (NSP) for Brain Machine Interfaces

2006 ◽  
Author(s):  
Shalom Darmanjian ◽  
Grzegorz Cieslewski ◽  
Scott Morrison ◽  
Benjamin Dang ◽  
Karl Gugel ◽  
...  
Keyword(s):  
Brain Machine Interfaces ◽  
Neural Signal ◽  
Signal Processor

scholarly journals CamelliA-based simultaneous imaging of Ca2+ dynamics in subcellular compartments

2021 ◽  
Author(s):  
Jingzhe Guo ◽  
Jiangman He ◽  
Katayoon Dehesh ◽  
Zhenbiao Yang
Keyword(s):  
High Resolution ◽  
Second Messenger ◽  
Wide Applicability ◽  
Simultaneous Imaging ◽  
Subcellular Compartments ◽  
Calcium Indicators ◽  
External Cues ◽  
Genetically Encoded Calcium Indicators ◽  
Arabidopsis Cells

As a universal second messenger, calcium (Ca2+) transmits specific cellular signals via a spatiotemporal signature generated from its extracellular source and internal stores. Our knowledge of the mechanisms underlying generation of a Ca2+ signature is hampered by limited tools enabling simultaneous monitoring of the dynamics of Ca2+ levels in multiple subcellular compartments. To overcome the limitation and to further improve spatiotemporal resolutions, here we have assembled a molecular toolset (the CamelliA lines) in Arabidopsis that enables simultaneous and high-resolution monitoring of Ca2+ dynamics in multiple subcellular compartments through imaging analyses of different single-colored GECIs (Genetically Encoded Calcium Indicators). Indeed, the uncovering of the previously unrecognized Ca2+ signatures in three types of Arabidopsis cells in response to internal and external cues is a testimony to the wide applicability of the newly generated toolset for elucidating the subcellular sources contributing to the Ca2+signatures in plants.

Sign in / Sign up

Export Citation Format

Share Document