scholarly journals Compact standalone platform for neural recording with real-time spike sorting and data logging

2018 ◽  
Vol 15 (4) ◽  
pp. 046014 ◽  
Author(s):  
Song Luan ◽  
Ian Williams ◽  
Michal Maslik ◽  
Yan Liu ◽  
Felipe De Carvalho ◽  
...  
Author(s):  
Anh Tuan Do ◽  
Seyed Mohammad Ali Zeinolabedin ◽  
Dongsuk Jeon ◽  
Dennis Sylvester ◽  
Tony Tae-Hyoung Kim

2017 ◽  
Author(s):  
Song Luan ◽  
Ian Williams ◽  
Michal Maslik ◽  
Yan Liu ◽  
Felipe de Carvalho ◽  
...  

Objective. Longitudinal observation of single unit neural activity from large numbers of cortical neurons in awake and mobile animals is often a vital step in studying neural network behaviour and towards the prospect of building effective Brain Machine Interfaces (BMIs). These recordings generate enormous amounts of data for transmission & storage, and typically require offline processing to tease out the behaviour of individual neurons. Our aim was to create a compact system capable of: 1) reducing the data bandwidth by circa 3 orders of magnitude (greatly improving battery lifetime and enabling low power wireless transmission); 2) producing real-time, low-latency, spike sorted data; and 3) long term untethered operation. Approach. We have developed a headstage that operates in two phases. In the short training phase a computer is attached and classic spike sorting is performed to generate templates. In the second phase the system is untethered and performs template matching to create an event driven spike output that is logged to a micro-SD card. To enable validation the system is capable of logging the high bandwidth raw data as well as the spike sorted data. Main results. The system can successfully record 32 channels of raw and/or spike sorted data for well over 24 hours at a time and is robust to power dropouts during battery changes as well as SD card replacement. A 24-hour initial recording in a non-human primate M1 showed consistent spike shapes with the expected changes in neural activity during awake behaviour and sleep cycles. Significance The presented platform allows neural activity to be unobtrusively monitored and processed in real-time in freely behaving untethered animals revealing insights that are not attainable through scheduled recording sessions and provides a robust, low-latency, low-bandwidth output suitable for BMIs, closed loop neuromodulation, wireless transmission and long term data logging.


Author(s):  
Florian Agbuya ◽  
Gerard Francesco Apolinario ◽  
Dianne Marie Ramos ◽  
JD Mark Villanueva ◽  
Princess Zafe ◽  
...  

Author(s):  
Yu-Hsiang Wu ◽  
Jingjing Xu ◽  
Elizabeth Stangl ◽  
Shareka Pentony ◽  
Dhruv Vyas ◽  
...  

Abstract Background Ecological momentary assessment (EMA) often requires respondents to complete surveys in the moment to report real-time experiences. Because EMA may seem disruptive or intrusive, respondents may not complete surveys as directed in certain circumstances. Purpose This article aims to determine the effect of environmental characteristics on the likelihood of instances where respondents do not complete EMA surveys (referred to as survey incompletion), and to estimate the impact of survey incompletion on EMA self-report data. Research Design An observational study. Study Sample Ten adults hearing aid (HA) users. Data Collection and Analysis Experienced, bilateral HA users were recruited and fit with study HAs. The study HAs were equipped with real-time data loggers, an algorithm that logged the data generated by HAs (e.g., overall sound level, environment classification, and feature status including microphone mode and amount of gain reduction). The study HAs were also connected via Bluetooth to a smartphone app, which collected the real-time data logging data as well as presented the participants with EMA surveys about their listening environments and experiences. The participants were sent out to wear the HAs and complete surveys for 1 week. Real-time data logging was triggered when participants completed surveys and when participants ignored or snoozed surveys. Data logging data were used to estimate the effect of environmental characteristics on the likelihood of survey incompletion, and to predict participants' responses to survey questions in the instances of survey incompletion. Results Across the 10 participants, 715 surveys were completed and survey incompletion occurred 228 times. Mixed effects logistic regression models indicated that survey incompletion was more likely to happen in the environments that were less quiet and contained more speech, noise, and machine sounds, and in the environments wherein directional microphones and noise reduction algorithms were enabled. The results of survey response prediction further indicated that the participants could have reported more challenging environments and more listening difficulty in the instances of survey incompletion. However, the difference in the distribution of survey responses between the observed responses and the combined observed and predicted responses was small. Conclusion The present study indicates that EMA survey incompletion occurs systematically. Although survey incompletion could bias EMA self-report data, the impact is likely to be small.


1999 ◽  
Vol 1999 (1) ◽  
pp. 1265-1267 ◽  
Author(s):  
Nir Barnea ◽  
Roger Laferriere

ABSTRACT SMART (Scientific Monitoring of Advanced Response Technologies) is a new monitoring program designed to provide the Unified Command with real-time field data when in situ burning and dispersants are used during oil spill response. For dispersant monitoring, SMART recommends a three-tiered approach. Tier I has visual observation by trained observers from vessels or from aerial platforms. Tier II combines visual observations with water-column sampling using a fluorometer at a single depth. Tier III expands the fluorometry monitoring to several water depths, and uses a water-quality lab. Water samples for later analysis and correlation of fluorometry readings are taken both in Tier II and Tier III. For in situ burning, SMART recommends deploying three or more monitoring teams, each equipped with a real-time particulate monitor with data-logging capability. The teams deploy downwind of the burn at sensitive locations, and report particulate concentration trends to the Unified Command.


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