Results 121 to 130 of about 2,171,414 (328)

Crack‐Growing Interlayer Design for Deep Crack Propagation and Ultrahigh Sensitivity Strain Sensing

Advanced Functional Materials, EarlyView.
A crack‐growing semi‐cured polyimide interlayer enabling deep cracks for ultrahigh sensitivity in low‐strain regimes is presented. The sensor achieves a gauge factor of 100 000 at 2% strain and detects subtle deformations such as nasal breathing, highlighting potential for minimally obstructive biomedical and micromechanical sensing applications ...
Minho Kim, Taewi Kim, Jieun Park, Insic Hong, Myungrae Hong, Seonggyeong Park, Beomjin Kim, Sehyuk Ahn, Siwoo Jang, Seungyong Han, Je‐Sung Koh, Daeshik Kang   +11 more
wiley   +1 more source

Brain–Computer Interface Spellers: A Review

Brain Science, 2018
A Brain–Computer Interface (BCI) provides a novel non-muscular communication method via brain signals. A BCI-speller can be considered as one of the first published BCI applications and has opened the gate for many advances in the field.
Aya Rezeika, Mihaly Benda, Piotr Stawicki, Felix Gembler, A. Saboor, Ivan Volosyak   +5 more
semanticscholar   +1 more source

Nanomaterial‐Integrated Fiber Neural Probes for Deep Brain Monitoring and Modulation: Challenges and Opportunities

Advanced Functional Materials, EarlyView.
The article presents nanomaterial‐integrated fiber neural probes as innovative tools for deep brain molecular sensing, neural stimulation, and temperature monitoring. It examines breakthroughs in SERS‐based biomolecule detection, thermoplasmonic activation, and luminescent thermometry, alongside strategies to overcome stability, specificity, and ...
Di Zheng, Yang Li, Muhammad Fayyaz Kashif, Linda Piscopo, Massimo De Vittorio, Ferruccio Pisanello   +5 more
wiley   +1 more source

Automatic Feature Selection for Sensorimotor Rhythms Brain-Computer Interface Fusing Expert and Data-Driven Knowledge

IEEE Transactions on Neural Systems and Rehabilitation Engineering
Early brain-computer interface (BCI) systems were mainly based on prior neurophysiological knowledge coupled with feedback training, while state-of-the-art interfaces rely on data-driven, machine learning (ML)-oriented methods.
Mushfika Sultana, Serafeim Perdikis
doaj   +1 more source

Electrosynthesis of Bioactive Chemicals, From Ions to Pharmaceuticals

Advanced Functional Materials, EarlyView.
This review discusses recent advances in electrosynthesis for biomedical and pharmaceutical applications. It covers key electrochemical materials enabling precise delivery of ions and small molecules for cellular modulation and disease treatment, alongside catalytic systems for pharmaceutical synthesis.
Gwangbin Lee, Sojin Kim, Myeongeun Lee, Kyoungsuk Jin, Jimin Park   +4 more
wiley   +1 more source

EEG Dataset for RSVP and P300 Speller Brain-Computer Interfaces

, 2022
Sung Chan Jun, Kyungho Won, Moonyoung Kwon, Minkyu Ahn   +3 more
openalex   +2 more sources

High-resolution SSVEP-based brain–computer interface

The Journal of Engineering, 2019
Steady-state visual evoked potential based brain–computer interface (BCI) is now attracting growing attention for its fast and efficient information transfer rate. However, human beings show excellent response only in a limited range of frequencies.
Shuailei Zhang, Shuai Wang, Dezhi Zheng, Kang Ma, Yajun Zhang, Wang Xiang   +5 more
doaj   +1 more source

Visual categorization of brain computer interface technologies [PDF]

, 2005
The multidisciplinary nature of Brain Computer Interfaces technologies demands scientists and researchers in the area to develop a homogeneous framework and classification of the existent BCI technologies in order to identify, facilitate and accelerate ...
Lawrence, E, Martin, D, Navarro, KF
core  

Review on brain-computer interface technologies in healthcare

Biophysical Reviews, 2023
Evelyn Karikari, Konstantin A. Koshechkin   +1 more
semanticscholar   +1 more source

Brain–computer interface robotics for hand rehabilitation after stroke: a systematic review

medRxiv, 2019
Background Hand rehabilitation is core to helping stroke survivors regain activities of daily living. Recent studies have suggested that the use of electroencephalography-based brain-computer interfaces (BCI) can promote this process. Here, we report the
P. D. E. Baniqued, E. C. Stanyer, Muhammad Awais, A. Alazmani, A. Jackson, M. Mon-Williams, F. Mushtaq, R. Holt   +7 more
semanticscholar   +1 more source