Description
The biosignalsplux Electroencephalography (EEG) sensor has been specifically designed with both classic and localized EEG measurement applications in mind. This single-lead EEG sensor is ideal for experimental setups for which an EEG cap is too intrusive, only a limited number of EEG channels are needed, or when multiple sensor types are being used in combination with localized EEG monitoring.
The bipolar configuration, with two measurement electrodes, detects the electrical potentials in the specific scalp region and is used in combination with a reference electrode, placed at a region of low muscular activity. The resulting signal is the amplified difference between these two signals and the result of the brain’s electrical activity in the monitored scalp region.
This sensor enables non-invasive, unobtrusive, and localized EEG monitoring, which, for example, can be used to track changes in cerebral activity as a result of visual or auditory stimuli.
Note: This sensor comes with our Headband to help keep the sensor in place during the acquisition. Additionally, this EEG Sensor works with a biosignalsplux hub, which is sold separately. Make sure you have one to use with this sensor!
Features
- Single-channel bipolar sensor
- Pre-conditioned analog output
- High signal-to-noise ratio
- Medical-grade raw data output
- Ready-to-use
- Miniaturized form-factor
Specifications
- Gain: 47.780
- Range: ±37.5μV (with VCC=3V)
- Bandwidth: 0.80-48.23Hz
- Input Impedance: >100GOhm
- CMRR: 100dB
- Cable Length: 100cm±0.5cm (customizable; extra costs may apply)
- Connector Type: UC-E6 (male; must be connected to a biosignalsplux acquisition unit)
- This sensor requires a biosignalsplux data acquisition system to function, which is sold separately.
Proven in Biosignals Research
Used in 50+ peer-reviewed studies, including leading journals and institutions. Some examples:
"On Biosignals Analysis for the Effectiveness of Essential Oils with Virtual Reality for Stress and Anxiety Relief" | IEEE Conference Publication | IEEE Xplore (2023)
"AI for health: Using AI to identify stress from wearable devices data" | Webthesis (2023)
"Attention Classification Based on Biosignals during Standard Cognitive Tasks for Occupational Domains" | (2022)
"New Frontiers in Postural Control and Motion Sickness Assessment: The BioVRSea Paradigm | IEEE Conference Publication" | IEEE Xplore (2024)
"Multimodal emotion classification using machine learning in immersive and non-immersive virtual reality" | Virtual Reality (2024)