Novel Dry Polymer Foam Electrodes for Long-Term EEG Measurement

By Chin-Teng Lin, Lun-De Liao, Yu-Hang Liu, I-Jan Wang, B. Lin, Jyh-Yeong Chang. IEEE Transactions on Biomedical Engineering, 2011

332 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cognitive Neuroscience, Polymers and Plastics

View Paper: https://www.semanticscholar.org/paper/abfb7c28d478f07d04a78177f2eb68031cd07794

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

In Cognitive Neuroscience

In Polymers and Plastics

Citations in the corpus

Identifying Stable Patterns over Time for Emotion Recognition from EEG (2016, background): “Many advanceddry electrodes and embedded systems are developed to handle the wearability, portability, and practical use of these systems in real-world applications [20], [21].”
Dry EEG Electrodes (2014, methodology): “For instance in [5], the impedance of wet electrodes deteriorated from 5 to 15 KΩ within 5 hours after gel application.”
Soft, curved electrode systems capable of integration on the auricle as a persistent brain–computer interface (2015, result): “S10), consistent with the previous observations (19, 37, 38) of significant increases (2–4 times) in skin–electrode impedance due to the gel dehydration (within 6 h).”
BCILAB: a platform for brain–computer interface development (2013, background): “Aidedbytheemergenceofnewsensortechnology, inparticular dry (gel-free) EEG electrodes incorporated into wearable wireless EEG systems [7–9] such cognitive state assessment can now be done in increasingly natural environments.”
Committee report: publication guidelines and recommendations for studies using electroencephalography and magnetoencephalography. (2014, methodology): “It should also be stated whether the peak was defined as the absolute peak or the local peak (e.g., a point that was greater than the adjacent points even if the adjacent points fell outside the measurement window—see Luck, 2005).”
Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review (2017, neutral): “…unit (e.g., http://openbci.com), the performance, comfort and usability of the electrode-cap (e.g., [37-39]), new type of sensors (e.g., [40]), on-line EEG signal quality monitoring [41, 42], and all ergonomic matters such as accessibility , usability and acceptance of the technology…”
A Review on the Computational Methods for Emotional State Estimation from the Human EEG (2013, background): “…EEG sensor should meet some criteria such as stabilization of a signal to noise ratio (SNR), reduction of noise elicited from hair, optimization of active dry electrodes, development of multi-channel wireless communication, and sustainment of the quality of EEG signals over a long period [100–103].”