sort client software (plexon inc)
90
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plexon inc
sort client software
Sort Client Software, supplied by plexon inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/sort-client+software/sort+client+software/pmc03871627-70-0-3
Average 90 stars, based on 1 article reviews
Sort Client Software, supplied by plexon inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/sort-client+software/sort+client+software/pmc03871627-70-0-3
Average 90 stars, based on 1 article reviews
sort client software - by Bioz Stars,
2026-11
90/100 stars
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Amplification:Article Title: Normalization by Valence and Motivational Intensity in the Sensorimotor Cortices (PMd, rM1, and cS1) Article Snippet: Single- and multi-units were sorted based on their waveforms using principal component (PC)-based methods in Article Title: Toward an Autonomous Brain Machine Interface: Integrating Sensorimotor Reward Modulation and Reinforcement Learning Article Snippet: Neural signals were amplified, bandpass filtered (170 Hz to 8 kHz for single and multiunit activity and 0.7–300 Hz for LFPs), sampled at 40 kHz for single-unit/ multiunit activity activity and 2 KHz for LFP, thresholded and singleunit/multiunits were sorted based on their waveforms using principal component (PC)-based methods in Article Title: Normalization by valence and motivational intensity in the sensorimotor cortices (PMd, M1, and S1) Article Snippet: Single- and multi-units were sorted based on their waveforms using principal component (PC)-based methods in Article Title: Autonomous brain-machine interface Article Snippet: Signals were amplified, band pass filtered (for example, 170 Hz-8 kHz), sampled at, for example, 40 kHz, subjected to thresholding, and units (multi and single units, not differentiated in our analysis unless otherwise explicitly stated) were identified and sorted based on their waveforms using the Article Title: Autonomous brain-machine interface Article Snippet: Signals were amplified, band pass filtered (for example, in some embodiments, between 170 Hz and 8 kHz), sampled at a frequency (for example, in some embodiments, at 40 kHz), subjected to thresholding, and units were identified by sorting using principal component methods using a software, such as, for example, a Article Title: Event-related potentials associated with performance monitoring in non-human primates. Article Snippet: 17 18 19 20 21 22 23 24 25 Article history: Accepted 7 April 2014 Available online xxxx Article Title: A Long-Range Fronto-Parietal 5- to 10-Hz Network Predicts “Top-Down” Controlled Guidance in a Task-Switch Paradigm Article Snippet: EEG signals were monitored online using Article Title: Toward an Autonomous Brain Machine Interface: Integrating Sensorimotor Reward Modulation and Reinforcement Learning Article Snippet: Neural signals were amplified, bandpass filtered (170 Hz to 8 kHz for single and multiunit activity and 0.7–300 Hz for LFPs), sampled at 40 kHz for single-unit/multiunit activity activity and 2 KHz for LFP, thresholded and single-unit/multiunits were sorted based on their waveforms using principal component (PC)-based methods in Software:Article Title: Normalization by Valence and Motivational Intensity in the Sensorimotor Cortices (PMd, rM1, and cS1) Article Snippet: Single- and multi-units were sorted based on their waveforms using principal component (PC)-based methods in Article Title: Toward an Autonomous Brain Machine Interface: Integrating Sensorimotor Reward Modulation and Reinforcement Learning Article Snippet: Neural signals were amplified, bandpass filtered (170 Hz to 8 kHz for single and multiunit activity and 0.7–300 Hz for LFPs), sampled at 40 kHz for single-unit/ multiunit activity activity and 2 KHz for LFP, thresholded and singleunit/multiunits were sorted based on their waveforms using principal component (PC)-based methods in Article Title: Normalization by valence and motivational intensity in the sensorimotor cortices (PMd, M1, and S1) Article Snippet: Single- and multi-units were sorted based on their waveforms using principal component (PC)-based methods in Article Title: Autonomous brain-machine interface Article Snippet: Signals were amplified, band pass filtered (for example, 170 Hz-8 kHz), sampled at, for example, 40 kHz, subjected to thresholding, and units (multi and single units, not differentiated in our analysis unless otherwise explicitly stated) were identified and sorted based on their waveforms using the Article Title: Autonomous brain-machine interface Article Snippet: Signals were amplified, band pass filtered (for example, in some embodiments, between 170 Hz and 8 kHz), sampled at a frequency (for example, in some embodiments, at 40 kHz), subjected to thresholding, and units were identified by sorting using principal component methods using a software, such as, for example, a Article Title: Event-related potentials associated with performance monitoring in non-human primates. Article Snippet: 17 18 19 20 21 22 23 24 25 Article history: Accepted 7 April 2014 Available online xxxx Article Title: A Long-Range Fronto-Parietal 5- to 10-Hz Network Predicts “Top-Down” Controlled Guidance in a Task-Switch Paradigm Article Snippet: EEG signals were monitored online using Article Title: Toward an Autonomous Brain Machine Interface: Integrating Sensorimotor Reward Modulation and Reinforcement Learning Article Snippet: Neural signals were amplified, bandpass filtered (170 Hz to 8 kHz for single and multiunit activity and 0.7–300 Hz for LFPs), sampled at 40 kHz for single-unit/multiunit activity activity and 2 KHz for LFP, thresholded and single-unit/multiunits were sorted based on their waveforms using principal component (PC)-based methods in Activity Assay:Article Title: Normalization by Valence and Motivational Intensity in the Sensorimotor Cortices (PMd, rM1, and cS1) Article Snippet: Single- and multi-units were sorted based on their waveforms using principal component (PC)-based methods in Article Title: Toward an Autonomous Brain Machine Interface: Integrating Sensorimotor Reward Modulation and Reinforcement Learning Article Snippet: Neural signals were amplified, bandpass filtered (170 Hz to 8 kHz for single and multiunit activity and 0.7–300 Hz for LFPs), sampled at 40 kHz for single-unit/ multiunit activity activity and 2 KHz for LFP, thresholded and singleunit/multiunits were sorted based on their waveforms using principal component (PC)-based methods in Article Title: Normalization by valence and motivational intensity in the sensorimotor cortices (PMd, M1, and S1) Article Snippet: Single- and multi-units were sorted based on their waveforms using principal component (PC)-based methods in Article Title: Autonomous brain-machine interface Article Snippet: Signals were amplified, band pass filtered (for example, 170 Hz-8 kHz), sampled at, for example, 40 kHz, subjected to thresholding, and units (multi and single units, not differentiated in our analysis unless otherwise explicitly stated) were identified and sorted based on their waveforms using the Article Title: Autonomous brain-machine interface Article Snippet: Signals were amplified, band pass filtered (for example, in some embodiments, between 170 Hz and 8 kHz), sampled at a frequency (for example, in some embodiments, at 40 kHz), subjected to thresholding, and units were identified by sorting using principal component methods using a software, such as, for example, a Article Title: Event-related potentials associated with performance monitoring in non-human primates. Article Snippet: 17 18 19 20 21 22 23 24 25 Article history: Accepted 7 April 2014 Available online xxxx Article Title: A Long-Range Fronto-Parietal 5- to 10-Hz Network Predicts “Top-Down” Controlled Guidance in a Task-Switch Paradigm Article Snippet: EEG signals were monitored online using Article Title: Toward an Autonomous Brain Machine Interface: Integrating Sensorimotor Reward Modulation and Reinforcement Learning Article Snippet: Neural signals were amplified, bandpass filtered (170 Hz to 8 kHz for single and multiunit activity and 0.7–300 Hz for LFPs), sampled at 40 kHz for single-unit/multiunit activity activity and 2 KHz for LFP, thresholded and single-unit/multiunits were sorted based on their waveforms using principal component (PC)-based methods in |