Results 111 to 120 of about 3,887,920 (279)

Classification of bruxism based on time-frequency and nonlinear features of single channel EEG

BMC Oral Health
Background In the classification of bruxism patients based on electroencephalogram (EEG), feature extraction is essential. The method of using multi-channel EEG fusing electrocardiogram (ECG) and Electromyography (EMG) signal features has been proved to ...
Chunwu Wang, Ajay K. Verma, Bijay Guragain, Xin Xiong, Chunling Liu   +4 more
doaj   +1 more source

Subject Conditioning for Motor Imagery Using Attention Mechanism

IEEE Access
This paper presents an advanced approach for enhancing electroencephalography (EEG) classification accuracy in motor tasks through the integration of subject-specific features.
Adam Gyula Nemes, Gyorgy Eigner
doaj   +1 more source

Incorporation of a language model into a Brain Computer Interface based speller through HMMs [PDF]

, 2012
Brain computer interface (BCI) research deals with the problem of establishing direct communication pathways between the brain and external devices. The primary motivation is to enable patients with limited or no muscular control to use external devices ...
Çetin, Müjdat, Ulaş, Çağdaş, Mujdat Cetin, Cagdas Ulas   +3 more
core   +1 more source

Neuromorphic Devices and Computing for Sensing, Memory, and Control

Advanced Science, EarlyView.
This review introduces neuromorphic devices made from diverse materials. These devices mimic neuronal functions and architectures and, when integrated with artificial or biological computing, can form closed loops with neurons for pressure, optical, acoustic, and biochemical sensing and modulation.
Zhengguang Zhu, Nicholas Schaffer, Xiao Yang   +2 more
wiley   +1 more source

Classification of EEG Signals using adaptive weighted distance nearest neighbor algorithm

Journal of King Saud University: Computer and Information Sciences, 2014
Electroencephalogram (EEG) signals are often used to diagnose diseases such as seizure, alzheimer, and schizophrenia. One main problem with the recorded EEG samples is that they are not equally reliable due to the artifacts at the time of recording.
E. Parvinnia, M. Sabeti, M. Zolghadri Jahromi, R. Boostani   +3 more
doaj   +1 more source

Automated robust human emotion classification system using hybrid EEG features with ICBrainDB dataset

, 2022
Emotion identification is an essential task for human–computer interaction systems. Electroencephalogram (EEG) signals have been widely used in emotion recognition.
Deniz, Erkan, Sobahi, Nebras, Omar, Naaman, Sengur, Abdulkadir, Acharya, U. Rajendra   +4 more
core   +1 more source

Encapsulated Leptin‐Producing Cells Facilitate Entrainment of Circadian Rhythms in Rodents and Nonhuman Primates

Advanced Science, EarlyView.
A clinically translatable cell line can be engineered to produce leptin, encapsulated in biomaterial and safely implanted to produce and deliver the metabolic regulating protein within the body. Implantation of these encapsulated cells decreases the time it takes for mice and non‐human primates to adjust to circadian disruptions similar to those ...
Samantha T. Fleury, Xuanyi Lin, Peter D. Rios, Christopher Olker, Eun Joo Song, Alejandra Cobos Perez, Cody Fell, Daisy Lopez, Ira Joshi, Hafsa Nasir, Cecelia Curtis, Danna Muringi, Kaiyuan Wang, José Oberholzer, Fred W. Turek, Isaac B. Hilton, Jonathan Rivnay, Martha Hotz Vitaterna, Omid Veiseh   +18 more
wiley   +1 more source

Translational Barriers and AI‐Driven Challenges of Microfluidics‐Enabled Wearables and Implantable Systems in Personalized Medicine

Advanced Science, EarlyView.
An integrative review of microfluidics‐enabled wearables and implantable systems reveals a single‐track translation pipeline, bridging functional biomaterials with clinical utility. Dynamic feedback loops driven by artificial intelligence advance diagnostics toward personalized closed‐loop theranostics.
Ke Huang, Ching Yin Fong, Xin Huang, Bee Luan Khoo   +3 more
wiley   +1 more source

Discriminative methods for classification of asynchronous imaginary motor tasks from EEG data [PDF]

, 2013
In this work, two methods based on statistical models that take into account the temporal changes in the electroencephalographic (EEG) signal are proposed for asynchronous brain-computer interfaces (BCI) based on imaginary motor tasks. Unlike the current
Çetin, Müjdat, Delgado Saa, Jaime Fernando   +1 more
core   +1 more source

Tumor Exposomics: A New Paradigm for Individualized Continuous Exposure Monitoring

Advanced Science, EarlyView.
Tumor exposomics integrates continuous monitoring of environmental exposures, endogenous biological responses, and behavioral factors within a unified temporal framework. By combining multimodal sensing technologies with AI‐enabled causal modeling, this emerging paradigm reconstructs exposure‐damage trajectories and supports individualized dynamic risk
Kaicheng Shen, Weiyi Wang, Yang Wang, Yiqiang Wu, Xiaohong Liu, Wei Zhang, Juanjuan Ou   +6 more
wiley   +1 more source