Results 141 to 150 of about 4,586,794 (296)

Genetic algorithm based deep learning model adaptation for improvising the motor imagery classification

Deep learning methods have proved a promising performance for electroencephalography-based brain-computer interfaces (EEG-BCI). It is particularly encouraging that a subject-independent model can be trained using a large amount of other subjects’ data ...
M, Ramasubba Reddy, Robinson, Neethu, R, Vishnupriya   +2 more
core   +1 more source

A Geometrically Transient Platform for Bioelectronic Implants

Advanced Materials, EarlyView.
Minimally invasive bioelectronic implants often compromise performance for smaller sizes. To resolve this optimization dilemma, a wireless bioelectronic implant with a transient geometry is introduced (MiFi). The origami‐inspired device miniaturizes up to sixfold for syringe insertion and autonomously unfolds post‐implantation.
Selin Olenik, John D. Goodwin, Atharv Naik, Philip Coatsworth, Ioi Chit Cheung, Farbod Amirghasemi, Anies Sohi, Ehsan Abedi, Tayeb Tadlaoui, Maral P. S. Mousavi, Andriy S. Kozlov, Victoria Salem, Andrew S. Cowburn, Firat Guder   +13 more
wiley   +1 more source

Error-Related EEG Potentials in Brain-Computer Interfaces

Brain-computer interfaces (BCI), as any other interaction modality based on physiological signals and body channels (e.g., muscular activity, speech and gestures), are prone to errors in the recognition of subject's intent. An elegant approach to improve
Ferrez, Pierre W., Millán, José del R.
core   +1 more source

Ethical issues with brain-computer interfaces

Frontiers in Systems Neuroscience, 2014
Walter eGlannon
doaj   +1 more source

1‐D Collocated Dual‐Gradient Sensory Fibers for Comprehensive Sensing and Decoding of Complex Human Motion and Physiology

Advanced Materials, EarlyView.
Coupled materials design enables a monolithic fiber that integrates complementary sensing regimes into a single wearable strand. By preserving informative signal features across subtle physiological deformation, large body motion, and mixed mechanical inputs, the dual‐gradient architecture generates synchronized, less redundant outputs that improve ...
Yunheum Lee, Sungha Jeon, Min Kim, Joonhee Won, Jinwook Yeo, Kyunghyun Jo, Soyeon Lee, Kieun Park, Yechan Yang, Chul Kim, Gun‐Hee Lee, Seunghwa Ryu, Sungho Jo, Seongjun Park   +13 more
wiley   +1 more source

Adaptation in Brain-Computer Interfaces

One major challenge in Brain-Computer Interface (BCI) research is to cope with the inherent nonstationarity of the recorded brain signals caused by changes in the subjects brain processes during an experiment. Online adaptation of the classifier embedded
Krauledat, M., Cabeza, R., Shenoy, P., Blankertz, B., Müller, K. -R., Schlögl, A., Rao, R.P.N., Pfurtscheller, Gert, Vidaurre, C., Buttfield, Anna, Millán, José del R.   +10 more
core  

Illiteracy of Brain-Computer Interface [PDF]

Journal of Korean Medical Science, 2019
openaire   +2 more sources

Soft Skins With Reversible Thickness Morphing: Materials, Mechanisms, and Applications

Advanced Materials, EarlyView.
Evolution of electronic skin (e‐skin) technologies toward adaptive, multifunctional soft skins. Phase I highlights early rigid and discrete sensory interfaces. Phase II shows the transition toward flexible, stretchable, and large‐area e‐skin. Phase III captures the emergence of computational e‐skin.
Oliver Ozioko, Chiamaka Akah, Ravinder Dahiya   +2 more
wiley   +1 more source

Metal Oxide Nano‐Interface Boosting the Deep Ultraviolet Adjustable Noise‐Filtering In‐Sensor Computing

Advanced Materials, EarlyView.
We show that sol‐gel‐fractured indium–magnesium oxide combines deep‐ultraviolet responsivity, high carrier mobility, and an excellent memory dynamic range. This unique materials platform enables deep‐ultraviolet long‐afterglow light‐emitting devices with multifunctional integration.
Zhongshi Ju, Peng Li, Jingsong Yuan, Boyuan Yu, Bin Han, Yusheng Chen, Jiangang Ma, Haiyang Xu, Yichun Liu, Paolo Samorì   +9 more
wiley   +1 more source

Intracortical Microstimulation in Brain-Computer Interfaces: Evoking Perception and Plasticity. [PDF]

Cyborg Bionic Syst
Hu P, Chen C, Zang Y, Li X, Ming D.
europepmc   +1 more source