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capacitive touch sensor  (SparkFun Electronics)

 
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    Structured Review

    SparkFun Electronics capacitive touch sensor
    Capacitive Touch Sensor, supplied by SparkFun Electronics, 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/capacitance+touch+sensor/capacitive+touch+sensor/pmc07237886-27-0-4
    Average 90 stars, based on 1 article reviews
    capacitive touch sensor - by Bioz Stars, 2026-10
    90/100 stars

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    Related Articles

    other:

    Article Title: Phase synchrony between prefrontal noradrenergic and cholinergic signals indexes inhibitory control
    Article Snippet: A capacitance touch sensor (AT42QT, SparkFun) was connected to the water spout to detect licks.

    Article Title: Cortical Observation by Synchronous Multifocal Optical Sampling Reveals Widespread Population Encoding of Actions
    Article Snippet: Licks were detected independently for each spout, with a lickometer built using a capacitive touch sensor (Sparkfun Tinkerkit) and a microcontroller (Arduino Uno).

    Article Title: Pattern Completion and Rate Remapping in Retrosplenial Cortex
    Article Snippet: The animal’s licks in anticipation or during consumption of reward were detected by a capacitive lick sensor (Sparkfun), and also routed into the data acquisition board.

    Article Title: Exploratory rearing is governed by hypothalamic MCH neurons according to<i>locus coeruleus</i>
    Article Snippet: Research Articles | Behavioral/Cognitive Exploratory rearing is governed by hypothalamic MCH neurons according to locus coeruleus https://doi.org/10.1523/JNEUROSCI.0015-24.2024 Received: 3 January 2024 Revised: 20 March 2024 Accepted: 25 March 2024 Copyright © 2024 Concetti et al.. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.. This Early Release article has been peer reviewed and accepted, but has not been through the composition and copyediting processes.The final version may differ slightly in style or formatting and will contain links to any extended data.

    Article Title: Innate and plastic mechanisms for maternal behaviour in auditory cortex.
    Article Snippet: The operant chamber (25 × 20 × 15 cm) consisted of an electrostatic speaker and metal lever connected to a capacitive touch sensor (SparkFun).

    Article Title: Odor comparator
    Article Snippet: Licking was detected using a capacitive touch sensor (Sparkfun, SEN-1204).

    Article Title: Learning enhances behaviorally relevant representations in apical dendrites
    Article Snippet: Licks for rewards were detected with a capacitance-based touch sensor (Sparkfun).

    Article Title: Disentangling the role of NAc D1 and D2 cells in hedonic eating.
    Article Snippet: Licks were detected using a capacitive sensor (Capacitive Touch Breakout, Sparkfun) and recorded at 500 Hz throughout the sessions using a National Instruments board (USB-X Series DAQ, NI-6343) and custom built LabView software (National Instruments).



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    Image Search Results


    (a–d) Computer-aided design (CAD) renderings showing the modular enclosure, camera mount, and removable front panel. (e) Top-mounted USB3 machine-vision camera with visible and 850 nm infrared (IR) LED illumination for light and dark recordings. (f–g) Configurable arena fixtures for pellet wells and capacitive lickometers supporting fluid-access or operant tasks. (h) System wiring diagram linking the NVIDIA Jetson Orin Nano Super (6-core ARM CPU, 1024 CUDA cores, 32 tensor cores, 8 GB LPDDR5, 67 INT8 TOPS) to an RP2040 controller and MPR121 capacitive sensors. (i) Example configuration showing a two-bottle choice and fixed-ratio 1 (FR1) feeding task. Each unit operates as an independent edge device performing on-board YOLO inference, sensor synchronization via TTL, and bidirectional task control—enabling scalable, low-cost, networked behavioral experiments without workstation-grade GPUs.

    Journal: bioRxiv

    Article Title: SqueakPose Studio: An end-to-end platform for pose estimation and real-time edge-AI deployment

    doi: 10.64898/2026.01.24.700912

    Figure Lengend Snippet: (a–d) Computer-aided design (CAD) renderings showing the modular enclosure, camera mount, and removable front panel. (e) Top-mounted USB3 machine-vision camera with visible and 850 nm infrared (IR) LED illumination for light and dark recordings. (f–g) Configurable arena fixtures for pellet wells and capacitive lickometers supporting fluid-access or operant tasks. (h) System wiring diagram linking the NVIDIA Jetson Orin Nano Super (6-core ARM CPU, 1024 CUDA cores, 32 tensor cores, 8 GB LPDDR5, 67 INT8 TOPS) to an RP2040 controller and MPR121 capacitive sensors. (i) Example configuration showing a two-bottle choice and fixed-ratio 1 (FR1) feeding task. Each unit operates as an independent edge device performing on-board YOLO inference, sensor synchronization via TTL, and bidirectional task control—enabling scalable, low-cost, networked behavioral experiments without workstation-grade GPUs.

    Article Snippet: Capacitive sensing for fluid intake was handled via Adafruit MPR121 12-key capacitive touch sensors, communicating with the RP2040 over I2C.

    Techniques: Control