Electroencephalography

Deep Neural Network-based Empirical Mode Decomposition for Motor Imagery EEG Classification
Deep Neural Network-based Empirical Mode Decomposition for Motor Imagery EEG Classification 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Motor imagery refers to the brain’s response during the mental simulation of physical activities, which can be detected through electroencephalogram (EEG) signals. However, EEG signals exhibit a low signal-to-noise ratio… read more

An Adaptive Hybrid Brain Computer Interface for Hand Function Rehabilitation of Stroke Patients
An Adaptive Hybrid Brain Computer Interface for Hand Function Rehabilitation of Stroke Patients 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Motor imagery (MI) based brain computer interface (BCI) has been extensively studied to improve motor recovery for stroke patients by inducing neuroplasticity. However, due to the lower spatial resolution and… read more

The Effect of Stimulation Intensity, Sampling Frequency, and Sample Synchronization in TMS-EEG on the TMS Pulse Artifact Amplitude and Duration
The Effect of Stimulation Intensity, Sampling Frequency, and Sample Synchronization in TMS-EEG on the TMS Pulse Artifact Amplitude and Duration 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Transcranial magnetic stimulation (TMS) coupled with electroencephalography (EEG) possesses diagnostic and therapeutic benefits. However, TMS provokes a large pulse artifact that momentarily obscures the cortical response, presenting a significant challenge… read more

Effect of Inverse Solutions, Connectivity Measures, and Node Sizes on EEG Source Network: A Simultaneous EEG Study
Effect of Inverse Solutions, Connectivity Measures, and Node Sizes on EEG Source Network: A Simultaneous EEG Study 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Brain network provides an essential perspective for studying normal and pathological brain activities. Reconstructing the brain network in the source space becomes more needed, for example, as a target in… read more

EEG-Based Brain Functional Network Analysis for Differential Identification of Dementia-Related Disorders and Their Onset
EEG-Based Brain Functional Network Analysis for Differential Identification of Dementia-Related Disorders and Their Onset 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Diagnosing and treating dementia, including mild cognitive impairment (MCI), is challenging due to diverse disease types and overlapping symptoms. Early MCI detection is vital as it can precede dementia, yet… read more

Optimizing Visual Stimulation Paradigms for User-Friendly SSVEP-Based BCIs
Optimizing Visual Stimulation Paradigms for User-Friendly SSVEP-Based BCIs 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
In steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems, traditional flickering stimulation patterns face challenges in achieving a trade-off in both BCI performance and visual comfort across various frequency… read more

Capsule Attention for Multimodal EEG-EOG Representation Learning with Application to Driver Vigilance Estimation
Author(s)3: Guangyi Zhang, Ali Etemad
Capsule Attention for Multimodal EEG-EOG Representation Learning with Application to Driver Vigilance Estimation 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

Driver vigilance estimation is an important task for transportation safety. Wearable and portable brain-computer interface devices provide a powerful means for real-time monitoring of the vigilance level of drivers to…

read more

Deep Pinsker and James-Stein Neural Networks for Decoding Motor Intentions from Limited Data
Author(s)3: Marko Angjelichinoski, Mohammadreza Soltani, John Choi, Bijan Pesaran, Vahid Tarokh
Deep Pinsker and James-Stein Neural Networks for Decoding Motor Intentions from Limited Data 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

Non-parametric regression has been shown to be useful in extracting relevant features from Local Field Potential (LFP) signals for decoding motor intentions. Yet, in many instances, brain-computer interfaces (BCIs) rely…

read more

VME-DWT: An Efficient Algorithm for Detection and Elimination of Eye Blink From Short Segments of Single EEG Channel
Author(s)3: Mohammad Shahbakhti, Matin Beiramvand, Mojtaba Nazari, Anna Broniec-Wójcik, Piotr Augustyniak, Ana Santos Rodrigues, Michal Wierzchon, Vaidotas Marozas
VME-DWT: An Efficient Algorithm for Detection and Elimination of Eye Blink From Short Segments of Single EEG Channel 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

Objective: Recent advances in development of low-cost single-channel electroencephalography (EEG) headbands have opened new possibilities for applications in health monitoring and brain-computer interface (BCI) systems. These recorded EEG signals, however,…

read more

Estimating Fugl-Meyer Upper Extremity Motor Score From Functional-Connectivity Measures
Author(s)3: Nader Riahi, Vasily A. Vakorin, Carlo Menon
Estimating Fugl-Meyer Upper Extremity Motor Score From Functional-Connectivity Measures 804 581 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

Fugl-Meyer assessment is an accepted method of evaluating motor function for people with stroke. A challenge associated with this assessment is the availability of trained examiners to carry out the…

read more
Search this website