Wireless Multichannel Neural Recording With a 128-Mbps UWB Transmitter for an Implantable Brain-Machine Interfaces

2016 ◽  
Vol 10 (6) ◽  
pp. 1068-1078 ◽  
Author(s):  
H. Ando ◽  
K. Takizawa ◽  
T. Yoshida ◽  
K. Matsushita ◽  
M. Hirata ◽  
...  
Keyword(s):  
Neural Recording ◽  
Brain Machine Interfaces

scholarly journals Making brain–machine interfaces robust to future neural variability

2016 ◽  
Vol 7 (1) ◽  
Author(s):  
David Sussillo ◽  
Sergey D. Stavisky ◽  
Jonathan C. Kao ◽  
Stephen I. Ryu ◽  
Krishna V. Shenoy
Keyword(s):  
State Of The Art ◽  
Neural Recording ◽  
Training Data ◽  
Data Sets ◽  
Brain Machine Interfaces ◽  
Recording Conditions ◽  
Data History ◽  
Neural Variability ◽  
Synthetic Training Data ◽  
Human Primate

Abstract A major hurdle to clinical translation of brain–machine interfaces (BMIs) is that current decoders, which are trained from a small quantity of recent data, become ineffective when neural recording conditions subsequently change. We tested whether a decoder could be made more robust to future neural variability by training it to handle a variety of recording conditions sampled from months of previously collected data as well as synthetic training data perturbations. We developed a new multiplicative recurrent neural network BMI decoder that successfully learned a large variety of neural-to-kinematic mappings and became more robust with larger training data sets. Here we demonstrate that when tested with a non-human primate preclinical BMI model, this decoder is robust under conditions that disabled a state-of-the-art Kalman filter-based decoder. These results validate a new BMI strategy in which accumulated data history are effectively harnessed, and may facilitate reliable BMI use by reducing decoder retraining downtime.

scholarly journals A Wireless Multichannel Neural Recording system for Implantable Brain-Machine Interfaces

2015 ◽  
Vol 9 ◽  
Author(s):  
Ando Hiroshi ◽  
Takizawa Kenichi ◽  
Yoshida Takeshi ◽  
Matsushita Kojiro ◽  
Hirata Masayuki ◽  
...  
Keyword(s):  
Neural Recording ◽  
Recording System ◽  
Brain Machine Interfaces ◽  
Neural Recording System

scholarly journals Direct measurement of vagal tone in rats does not show correlation to HRV

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Joseph T. Marmerstein ◽  
Grant A. McCallum ◽  
Dominique M. Durand
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Neural Activity ◽  
Vagal Tone ◽  
Neural Recording ◽  
The Body ◽  
Autonomic Nerve ◽  
Vagal Activity ◽  
Recording Technology

AbstractThe vagus nerve is the largest autonomic nerve, innervating nearly every organ in the body. “Vagal tone” is a clinical measure believed to indicate overall levels of vagal activity, but is measured indirectly through the heart rate variability (HRV). Abnormal HRV has been associated with many severe conditions such as diabetes, heart failure, and hypertension. However, vagal tone has never been directly measured, leading to disagreements in its interpretation and influencing the effectiveness of vagal therapies. Using custom carbon nanotube yarn electrodes, we were able to chronically record neural activity from the left cervical vagus in both anesthetized and non-anesthetized rats. Here we show that tonic vagal activity does not correlate with common HRV metrics with or without anesthesia. Although we found that average vagal activity is increased during inspiration compared to expiration, this respiratory-linked signal was not correlated with HRV either. These results represent a clear advance in neural recording technology but also point to the need for a re-interpretation of the link between HRV and “vagal tone”.

Brain-Machine Interfaces: Electrophysiological Challenges and Limitations

2011 ◽  
Vol 39 (1) ◽  
pp. 5-28 ◽  
Author(s):  
Mijail D. Serruya ◽  
Bradley C. Lega ◽  
Kareem Zaghloul
Keyword(s):  
Brain Machine Interfaces
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