Alginate-based hydrogels as an alternative to electrolytic gels for rapid EEG monitoring and easy cleaning procedures

Pedrosa, Paulo; Fiedler, Patrique; Schinaia, Lorenzo; Vasconcelos, Beatriz; Martins, Ana; Amaral, Maria Helena; Comani, Silvia; Haueisen, Jens; Fonseca, C.

doi:10.1016/j.snb.2017.02.164

Cited by 44 publications

(54 citation statements)

References 40 publications

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“…Figure 2 illustrates the various Emotiv EPOC

helmet components used in the experimental step consisting of a headset, a fourteen-sensors box, a USB key with cable for battery recharging that ensures the connection between the headset and the Emotiv Pro software, and a saline solution [ 56 ] that ensures impedance and contact with the cortex. Compared to medical gel [ 57 ], the saline solution is easy to use and maintains effective contact with the scalp of men and women.…”

Section: Methodsmentioning

confidence: 99%

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Chaabene

Bouaziz

Boudaya

et al. 2021

Sensors

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Drowsiness detection (DD) has become a relevant area of active research in biomedical signal processing. Recently, various deep learning (DL) researches based on the EEG signals have been proposed to detect fatigue conditions. The research presented in this paper proposes an EEG classification system for DD based on DL networks. However, the proposed DD system is mainly realized into two procedures; (i) data acquisition and (ii) model analysis. For the data acquisition procedure, two key steps are considered, which are the signal collection using a wearable Emotiv EPOC+ headset to record 14 channels of EEG, and the signal annotation. Furthermore, a data augmentation (DA) step has been added to the proposed system to overcome the problem of over-fitting and to improve accuracy. As regards the model analysis, a comparative study is also introduced in this paper to argue the choice of DL architecture and frameworks used in our DD system. In this sense, The proposed DD protocol makes use of a convolutional neural network (CNN) architecture implemented using the Keras library. The results showed a high accuracy value (90.42%) in drowsy/awake discrimination and revealed the efficiency of the proposed DD system compared to other research works.

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“…Figure 2 illustrates the various Emotiv EPOC

Section: Methodsmentioning

confidence: 99%

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Chaabene

Bouaziz

Boudaya

et al. 2021

Sensors

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“…Silver/Silver-Chloride (Ag/AgCl) electrodes used with a conductive electrolyte gel for a long time have been the gold standard for every kind of (non-invasive) EEG signal recording due to their reliable electrochemical characteristics, and low level of intrinsic noise. Common limitations like high cleaning effort and limited recording time due to gel drying, have been addressed, e.g., in recent developments of novel hydrogels ( Pedrosa et al, 2017 ). Yet, gel-based EEG suffers from important limitations related to the gel use, e.g., a long preparation time requiring specifically trained technical personnel, risks of allergic reactions, and electrode short-circuits due to gel spreading ( Niedermeyer and Lopes da Silva, 2005 ; Kleffner-Canucci et al, 2012 ).…”

Section: Introductionmentioning

confidence: 99%

The Arch Electrode: A Novel Dry Electrode Concept for Improved Wearing Comfort

et al. 2021

Self Cite

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Electroencephalography (EEG) is increasingly used for repetitive and prolonged applications like neurofeedback, brain computer interfacing, and long-term intermittent monitoring. Dry-contact electrodes enable rapid self-application. A common drawback of existing dry electrodes is the limited wearing comfort during prolonged application. We propose a novel dry Arch electrode. Five semi-circular arches are arranged parallelly on a common baseplate. The electrode substrate material is a flexible thermoplastic polyurethane (TPU) produced by additive manufacturing. A chemical coating of Silver/Silver-Chloride (Ag/AgCl) is applied by electroless plating using a novel surface functionalization method. Arch electrodes were manufactured and validated in terms of mechanical durability, electrochemical stability, in vivo applicability, and signal characteristics. We compare the results of the dry arch electrodes with dry pin-shaped and conventional gel-based electrodes. 21-channel EEG recordings were acquired on 10 male and 5 female volunteers. The tests included resting state EEG, alpha activity, and a visual evoked potential. Wearing comfort was rated by the subjects directly after application, as well as at 30 min and 60 min of wearing. Our results show that the novel plating technique provides a well-adhering electrically conductive and electrochemically stable coating, withstanding repetitive strain and bending tests. The signal quality of the Arch electrodes is comparable to pin-shaped dry electrodes. The average channel reliability of the Arch electrode setup was 91.9 ± 9.5%. No considerable differences in signal characteristics have been observed for the gel-based, dry pin-shaped, and arch-shaped electrodes after the identification and exclusion of bad channels. The comfort was improved in comparison to pin-shaped electrodes and enabled applications of over 60 min duration. Arch electrodes required individual adaptation of the electrodes to the orientation and hairstyle of the volunteers. This initial preparation time of the 21-channel cap increased from an average of 5 min for pin-like electrodes to 15 min for Arch electrodes and 22 min for gel-based electrodes. However, when re-applying the arch electrode cap on the same volunteer, preparation times of pin-shaped and arch-shaped electrodes were comparable. In summary, our results indicate the applicability of the novel Arch electrode and coating for EEG acquisition. The novel electrode enables increased comfort for prolonged dry-contact measurement.

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“…In summary, from in-depth consideration of skin homeostasis (1,2,7,13), a novel biosensor with functions such as protection, self-regulation, biosafety, and sensation required for long-time measurement was conceived. Existing EP sensors (7)(8)(9)(10)(11)(12) developed with a focus on sensation or protection (table S2) are not compatible with skin homeostasis, causing skin allergies or contact dermatitis or are being affected by sweat (1,(13)(14)(15)(16)50).…”

Section: Discussionmentioning

confidence: 99%

“…This analysis revealed that the CM markedly transforms its properties to become a homeostatic material of self-regulation at approximately 85% of the swelling ( 27 , 28 ) process. In addition, we verified the long-term biosafety of CM based on in-depth dermatological tests.The performance of cellulose biosensors (CSs) is shown to compare well to that of conductive gel sensors (GSs) ( 7 , 8 ) that can establish a reliable conductive interface on the skin in the presence or absence of hair layers.…”

Section: Introductionmentioning

confidence: 99%

Leaf-inspired homeostatic cellulose biosensors

Kim

Yun

Jeong³

et al. 2021

Sci. Adv.

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An incompatibility between skin homeostasis and existing biosensor interfaces inhibits long-term electrophysiological signal measurement. Inspired by the leaf homeostasis system, we developed the first homeostatic cellulose biosensor with functions of protection, sensation, self-regulation, and biosafety. Moreover, we find that a mesoporous cellulose membrane transforms into homeostatic material with properties that include high ion conductivity, excellent flexibility and stability, appropriate adhesion force, and self-healing effects when swollen in a saline solution. The proposed biosensor is found to maintain a stable skin-sensor interface through homeostasis even when challenged by various stresses, such as a dynamic environment, severe detachment, dense hair, sweat, and long-term measurement. Last, we demonstrate the high usability of our homeostatic biosensor for continuous and stable measurement of electrophysiological signals and give a showcase application in the field of brain-computer interfacing where the biosensors and machine learning together help to control real-time applications beyond the laboratory at unprecedented versatility.

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Alginate-based hydrogels as an alternative to electrolytic gels for rapid EEG monitoring and easy cleaning procedures

Cited by 44 publications

References 40 publications

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

The Arch Electrode: A Novel Dry Electrode Concept for Improved Wearing Comfort

Leaf-inspired homeostatic cellulose biosensors

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