Comfortable Flower Electrodes for Dry EEG in Epilepsy and Clinical Neurophysiology Diagnostics

Dry electroencephalography (EEG) electrodes enable rapid, gel-free setups, which are crucial for point-of-care diagnostics, but often face challenges with comfort and signal quality-especially in a clinical context. Novel "flower" dry electrodes are a special type of reusable scalp electrodes for dry EEG, featuring a distinct flower-like shape with angled pins in three intertwined layers. While the new electrode design has been validated in an in vivo study on healthy volunteers, we tested its clinical applicability in a proof-of-concept study involving three patients diagnosed with epilepsy and delirium. The recordings were of high diagnostic quality, enabling the reliable identification of pathological patterns, such as generalized spike-wave complexes and intermittent delta activity, with a signal-to-noise ratio comparable to prior reports for sponge-based EEG systems (limited case series). The signal-to-noise ratio (SNR) proved to be sufficiently high for clinical diagnostic purposes, resulting in visually clear and interpretable EEG data that enabled effective assessment of patients' neurophysiological signals. Consequently, our findings demonstrate that the comfortable flower-electrode design is a viable and effective tool for epilepsy diagnostics, extended recording, and clinical neurophysiology. It represents a significant step towards patient-centered and gel-free EEG technology, specifically in point-of-care and emergency applications, without compromising the diagnostic quality of the recordings.

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Publication Details

Journal
Sensors
Published
2026-03-31
DOI
https://doi.org/10.3390/s26072146
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
0.00
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article

Comfortable Flower Electrodes for Dry EEG in Epilepsy and Clinical Neurophysiology Diagnostics

Joji B. Kuramatsu, Justus Carl Marquetand, Dimitrios Dimitrakopoulos, Patrique Fiedler et al.
Sensors
EEG and Brain-Computer Interfaces
article

Comfortable Flower Electrodes for Dry EEG in Epilepsy and Clinical Neurophysiology Diagnostics

Joji B. Kuramatsu, Justus Carl Marquetand, Dimitrios Dimitrakopoulos, Patrique Fiedler, Johannes Lang
article en

Abstract

Dry electroencephalography (EEG) electrodes enable rapid, gel-free setups, which are crucial for point-of-care diagnostics, but often face challenges with comfort and signal quality-especially in a clinical context. Novel "flower" dry electrodes are a special type of reusable scalp electrodes for dry EEG, featuring a distinct flower-like shape with angled pins in three intertwined layers. While the new electrode design has been validated in an in vivo study on healthy volunteers, we tested its clinical applicability in a proof-of-concept study involving three patients diagnosed with epilepsy and delirium. The recordings were of high diagnostic quality, enabling the reliable identification of pathological patterns, such as generalized spike-wave complexes and intermittent delta activity, with a signal-to-noise ratio comparable to prior reports for sponge-based EEG systems (limited case series). The signal-to-noise ratio (SNR) proved to be sufficiently high for clinical diagnostic purposes, resulting in visually clear and interpretable EEG data that enabled effective assessment of patients' neurophysiological signals. Consequently, our findings demonstrate that the comfortable flower-electrode design is a viable and effective tool for epilepsy diagnostics, extended recording, and clinical neurophysiology. It represents a significant step towards patient-centered and gel-free EEG technology, specifically in point-of-care and emergency applications, without compromising the diagnostic quality of the recordings.

SensorsVol. 26(7)
University of Stuttgart (DE), Technische Universität Ilmenau (DE), Friedrich-Alexander-Universität Erlangen-Nürnberg (DE), Jain Irrigation Systems (Israel) (IL), RoMed Kliniken (DE), Hertie Institute for Clinical Brain Research (DE)
Openalex Percentile: Top 38%
EEG and Brain-Computer Interfaces
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