Fully portable and wireless universal brain–machine interfaces enabled by flexible scalp electronics and deep learning algorithm

Mahmood, Musa; Mzurikwao, Deogratias; Kim, Yun-Soung; Yong-Kuk, Lee; Mishra, Saswat; Herbert, Robert; Duarte, Audrey; Ang, Chee Siang; Yeo, Woon‐Hong

doi:10.1038/s42256-019-0091-7

Cited by 127 publications

(124 citation statements)

References 52 publications

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“…A pair of electrodes for sodium sensing were integrated onto a flexible circuit. The circuit design followed our prior works [33,34], including a Bluetooth low-energy chip, 2.45 GHz chip antenna, and a rechargeable battery. The flexible circuit was utilized for reading potential differences between electrodes changed in different sodium concentrations and wireless transmission of sensing data (details of the circuit design and list of components in Figure S1 and Table S1).…”

Section: Flexible-circuit Designmentioning

confidence: 99%

Wireless, Flexible, Ion-Selective Electrode System for Selective and Repeatable Detection of Sodium

Lim

Kim

Kwon

et al. 2020

Sensors

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Wireless, flexible, ion-selective electrodes (ISEs) are of great interest in the development of wearable health monitors and clinical systems. Existing film-based electrochemical sensors, however, still have practical limitations due to poor electrical contact and material–interfacial leakage. Here, we introduce a wireless, flexible film-based system with a highly selective, stable, and reliable sodium sensor. A flexible and hydrophobic composite with carbon black and soft elastomer serves as an ion-to-electron transducer offering cost efficiency, design simplicity, and long-term stability. The sensor package demonstrates repeatable analysis of selective sodium detection in saliva with good sensitivity (56.1 mV/decade), stability (0.53 mV/h), and selectivity coefficient of sodium against potassium (−3.0). The film ISEs have an additional membrane coating that provides reinforced stability for the sensor upon mechanical bending. Collectively, the comprehensive study of materials, surface chemistry, and sensor design in this work shows the potential of the wireless flexible sensor system for low-profile wearable applications.

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Section: Flexible-circuit Designmentioning

confidence: 99%

Wireless, Flexible, Ion-Selective Electrode System for Selective and Repeatable Detection of Sodium

Lim

Kim

Kwon

et al. 2020

Sensors

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“…Open-mesh structures or serpentine structures show conformal contact for gathering human biopotentials in the field of FHE. With the use of open-mesh structure, conformal skin contact was achieved to measure high-quality electroencephalograms (EEG) [29]. Serpentine structure of AgNP-printed sensor, encapsulated by Si elastomer, accommodates a high stretchability of up to 250% of radial stretching and a cyclic bending [23].…”

Section: Flexibility/stretchabilitymentioning

confidence: 99%

“…A fully portable and wireless, flexible scalp electronics system, comprised of a set of dry electrodes and a flexible membrane circuit, was developed ( Figure 10 g,h) [ 29 ]. The aerosol-printed skin-like electrode is mounted on mastoid ( Figure 10 g), and the other three flexible conductive electrodes are attached on the hairy scalp (occipital lobe).…”

Section: Applicationsmentioning

confidence: 99%

“…Reprinted with permission from reference [ 24 ], Copyright 2019, John Wiley and Sons; ( g ) a skin-like electrode with an open-mesh structure; ( h ) control of machines via classified EEG signals, including a wireless electric wheelchair with five classes (no action, forward, rotate anticlockwise (ACW), rotate clockwise (CW) and reverse), a wireless vehicle with the same commands as the wheelchair. Reproduced with permission from reference [ 29 ], Copyright 2019, Springer Nature; ( i ) an image of printed AgNW dry electrocardiogram (ECG) electrodes and ECG signals collected from the electrode. Reproduced with permission from reference [ 28 ], Copyright 2018, RSC; ( j ) illustration of the fabrication of an implantable flow sensor in an aneurysm model and its explosive view.…”

Section: Figurementioning

confidence: 99%

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Advanced Nanomaterials, Printing Processes, and Applications for Flexible Hybrid Electronics

Park

Kim

et al. 2020

Materials

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Recent advances in nanomaterial preparation and printing technologies provide unique opportunities to develop flexible hybrid electronics (FHE) for various healthcare applications. Unlike the costly, multi-step, and error-prone cleanroom-based nano-microfabrication, the printing of nanomaterials offers advantages, including cost-effectiveness, high-throughput, reliability, and scalability. Here, this review summarizes the most up-to-date nanomaterials, methods of nanomaterial printing, and system integrations to fabricate advanced FHE in wearable and implantable applications. Detailed strategies to enhance the resolution, uniformity, flexibility, and durability of nanomaterial printing are summarized. We discuss the sensitivity, functionality, and performance of recently reported printed electronics with application areas in wearable sensors, prosthetics, and health monitoring implantable systems. Collectively, the main contribution of this paper is in the summary of the essential requirements of material properties, mechanisms for printed sensors, and electronics.

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“…Research on Brain–Computer Interfaces (BCI) has a long history [ 1 ] and has attracted more attention for its extensive potential in the fields of neural engineering, clinical rehabilitation, daily communication and many other possible applications [ 2 , 3 , 4 ]. A typical non-invasive BCI uses electroencephalography (EEG) as it is inexpensive and easy to implement [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Silent Speech Decoding Using Spectrogram Features Based on Neuromuscular Activities

Wang

Zhang

et al. 2020

Brain Sciences

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Silent speech decoding is a novel application of the Brain–Computer Interface (BCI) based on articulatory neuromuscular activities, reducing difficulties in data acquirement and processing. In this paper, spatial features and decoders that can be used to recognize the neuromuscular signals are investigated. Surface electromyography (sEMG) data are recorded from human subjects in mimed speech situations. Specifically, we propose to utilize transfer learning and deep learning methods by transforming the sEMG data into spectrograms that contain abundant information in time and frequency domains and are regarded as channel-interactive. For transfer learning, a pre-trained model of Xception on the large image dataset is used for feature generation. Three deep learning methods, Multi-Layer Perception, Convolutional Neural Network and bidirectional Long Short-Term Memory, are then trained using the extracted features and evaluated for recognizing the articulatory muscles’ movements in our word set. The proposed decoders successfully recognized the silent speech and bidirectional Long Short-Term Memory achieved the best accuracy of 90%, outperforming the other two algorithms. Experimental results demonstrate the validity of spectrogram features and deep learning algorithms.

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Fully portable and wireless universal brain–machine interfaces enabled by flexible scalp electronics and deep learning algorithm

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 127 publications

References 52 publications

Wireless, Flexible, Ion-Selective Electrode System for Selective and Repeatable Detection of Sodium

Wireless, Flexible, Ion-Selective Electrode System for Selective and Repeatable Detection of Sodium

Advanced Nanomaterials, Printing Processes, and Applications for Flexible Hybrid Electronics

Silent Speech Decoding Using Spectrogram Features Based on Neuromuscular Activities

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