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Towards Identifying Optimal Biased Feedback for Various User States and Traits in Motor Imagery BCI
Author(s)3: Jelena Mladenović, Jérémy Frey, Smeety Pramij, Jérémie Mattout, Fabien Lotte
Towards Identifying Optimal Biased Feedback for Various User States and Traits in Motor Imagery BCI 170 177 IEEE Transactions on Biomedical Engineering (TBME)
This work aims to prescribe biased feedback optimal for user’s psychological factors in order to increase performance and learning of a motor imagery brain-computer interface (MI-BCI). For instance, presenting negative biased feedback to a user in a low workload state can substantially increase performance, while positive bias is generally detrimental for short-term learning. We present a novel method to continuously alter the visual feedback bias in real-time of an immersive video-game, revealing the potential of an adaptive bias across sessions. This paper can serve as a guideline to tailor feedback bias to each MI-BCI user. read more

CCi-MOBILE: A Portable Real Time Speech Processing Platform for Cochlear Implant and Hearing Research
Author(s)3: Ria Ghosh, Hussnain Ali, John. H. L. Hansen
CCi-MOBILE: A Portable Real Time Speech Processing Platform for Cochlear Implant and Hearing Research 170 177 IEEE Transactions on Biomedical Engineering (TBME)
This work presents the design, development, clinical evaluation, and applications of CCi-MOBILE, a computationally powerful signal processing testing platform built for researchers in the hearing-impaired field. The custom-made, portable research platform allows researchers to design and perform complex speech processing algorithm assessment offline and in real-time. It can be operated through user-friendly, open-source software and is compatible with implants manufactured by Cochlear Corporation. The FPGA design and hardware processing pipeline for CI stimulation is discussed followed by results from an acute study with implant users’ speech intelligibility in quiet and noisy conditions. read more

Channel Characterization of Magnetic Human Body Communication
Author(s)3: Erda Wen, Daniel F. Sievenpiper, Patrick P. Mercier
Channel Characterization of Magnetic Human Body Communication 170 177 IEEE Transactions on Biomedical Engineering (TBME)
This paper aims to validate, analytically and experimentally, the benefits of the magnetic human body communication (mHBC) method using small form-factor-accurate antennas operating under realistic conditions. We show that by adopting resonant coils that couple by magnetic-dominant near-field at a few hundreds of MHz, low path loss and extra robustness to antenna misalignment across the body can be achieved compared to conventional far-field RF schemes. In best-case scenarios, the mHBC channel exhibits 100000x better efficiency than Bluetooth utilizing antennas of similar sizes. The extremely high efficiency provides a potential solution to the ever-present energy problem for miniaturized wearables. read more

Align and Pool for EEG Headset Domain Adaptation (ALPHA) to Facilitate Dry Electrode Based SSVEP-BCI
Author(s)3: Bingchuan Liu, Xiaogang Chen, Xiang Li, Yijun Wang, Xiaorong Gao, Shangkai Gao
Align and Pool for EEG Headset Domain Adaptation (ALPHA) to Facilitate Dry Electrode Based SSVEP-BCI 170 177 IEEE Transactions on Biomedical Engineering (TBME)
This study leverages transfer learning to improve the performance for steady-state visual evoked potential based brain-computer interface (SSVEP-BCI) implemented by dry electrodes. We utilize auxiliary individual electroencephalogram (EEG) recorded from wet electrode for cross-device transfer learning via the proposed framework named ALign and Pool for EEG Headset domain Adaptation (ALPHA), which aligns the SSVEP features by domain adaptation. ALPHA significantly outperformed the competing methods in two transfer directions, and boosted the dry-electrode systems using wet-electrode EEG. The cross-device transfer learning by ALPHA could increase the utility and potentially promote the use of dry electrode based SSVEP-BCIs in practical applications. read more

Electro-optical classification of pollen grains via microfluidics and machine learning
Author(s)3: Michele D’Orazio, Riccardo Reale, Adele De Ninno, Maria A. Brighetti, Arianna Mencattini, Luca Businaro, Eugenio Martinelli, Paolo Bisegna, Alessandro Travaglini, Federica Caselli
Electro-optical classification of pollen grains via microfluidics and machine learning 340 354 IEEE Transactions on Biomedical Engineering (TBME)
This interdisciplinary work involves sensor science, microfluidics, machine learning, and palynology. Palynology – i.e., the study of pollen and fungal spores – finds applications in high-impact fields like air quality control, allergology, and agriculture. Traditionally, the study of pollen takes place through microscopic analysis performed by specialized operators, after staining of the sample. The procedure requires long times and is prone to human errors. Therefore, there is an unmet need for accurate, label-free, and automated systems for the analysis of pollen, ideally within a field-portable and cost-effective platform. In this framework, we propose an original multimodal approach. read more

Chirp-Evoked Auditory Steady-State Response: The Effect of Repetition Rate
Author(s)3: Christian Bech Christensen, Thomas Lunner, James Michael Harte, Mike Lind Rank, Preben Kidmose
Chirp-Evoked Auditory Steady-State Response: The Effect of Repetition Rate 170 177 IEEE Transactions on Biomedical Engineering (TBME)
The auditory steady-state response (ASSR) is commonly used in clinical pediatric audiology to provide an electrophysiological estimate of hearing thresholds, and has the potential to be used in unsupervised mobile EEG applications. Enhancement of the ASSR amplitude through optimization of the stimulation and recording methods shortens the required testing time or reduce the offset between the electrophysiological and behavioral thresholds. In this study, the spatial distribution of the ASSR to broadband chirp stimuli is investigated across a wide range of repetition rates on the scalp and in the ears. Moreover, the ASSR amplitude is compared for commonly used electrode configurations. read more

Millimeter-wave heating in vitro: local microscale temperature measurements correlated to heat shock cellular response
Author(s)3: Rosa Orlacchio, Denys Nikolayev, Yann Le Page, Yves Le Dréan, Maxim Zhadobov
Millimeter-wave heating in vitro: local microscale temperature measurements correlated to heat shock cellular response 170 177 IEEE Transactions on Biomedical Engineering (TBME)
Millimeter-wave (MMW) induced heating can be potentially used to treat superficial skin cancer, including spreading melanoma. The aim of this work is to assess the cellular sensitivity of the A375 melanoma cell line to continuous MMW (58.4 GHz) induced heating between 37 and 47°C. Phosphorylation of heat shock protein 27 (HSP27) was used as a marker of the heat- induced cellular stress. Numerical and experimental electromagnetic and thermal dosimetry were carried out in detail to guarantee the correct interpretation of the biological outcomes. Results obtained may contribute to the design and optimization of clinical thermal treatment of superficial melanoma. read more

Predictive statistical model of early cranial development
Author(s)3: Antonio R. Porras, Robert F. Keating, Janice S. Lee, Marius George Linguraru
Predictive statistical model of early cranial development 170 177 IEEE Transactions on Biomedical Engineering (TBME)
This work introduces a data-driven model of pediatric cranial bone development during the first two years of life. We present an automatic algorithmic pipeline to segment the cranial bones from a large retrospective cross-sectional dataset of computed tomography images of normative pediatric subjects, establish local anatomical correspondences between crania guided by the cranial sutures, and create a statistical model of the anatomical variability of the calvaria and its normal temporal changes during development. Our data-driven statistical approach assumes temporal continuity of cranial development to avoid assumptions about the biophysical processes involved with bone growth. read more

Evolutional Neural Architecture Search for Optimization of Spatiotemporal Brain Network Decomposition
Author(s)3: Qing Li, Wei Zhang, Xia Wu, Tianming Liu
Evolutional Neural Architecture Search for Optimization of Spatiotemporal Brain Network Decomposition 452 480 IEEE Transactions on Biomedical Engineering (TBME)
Using deep neural networks (DNNs) to decompose spatiotemporal brain network has been an important yet challenging problem because the architectures are hard to be designed manually. The previous studies, e.g., deep sparse recurrent auto-encoder (DSRAE), are not optimal in various senses. We employ the evolutionary algorithms to optimize the architecture of DSRAE, named eNAS-DSRAE (i.e., evolutionary Neural Architecture Search on DSRAE). With the validation experiments, our framework can successfully identify the spatiotemporal features and perform better than the hand-crafted DNNs. To our best knowledge, the proposed eNAS-DSRAE is among the earliest NAS models that can extract meaningful spatiotemporal brain networks. read more
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