Electroencephalography

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…

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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…

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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,…

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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…

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Associations Among Emotional State, Sleep Quality, and Resting-State EEG Spectra: A Longitudinal Study in Graduate Students
Author(s)3: Oleksii Komarov, Li-Wei Ko, Tzyy-Ping Jung
Associations Among Emotional State, Sleep Quality, and Resting-State EEG Spectra: A Longitudinal Study in Graduate Students 651 619 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

University students are routinely influenced by a variety of natural stressors and experience irregular sleep-wake cycles caused by the necessity to trade sleep for studying while dealing with academic assignments.…

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Decoding Movement-Related Cortical Potentials Based on Subject-Dependent and Section-Wise Spectral Filtering
Author(s)3: Ji-Hoon Jeong, No-Sang Kwak, Seong-Whan Lee, Cuntai Guan
Decoding Movement-Related Cortical Potentials Based on Subject-Dependent and Section-Wise Spectral Filtering 1610 928 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

An important challenge in developing a movement-related cortical potential (MRCP)-based brain-machine interface (BMI) is an accurate decoding of the user intention for real-world environments. However, the performance remains insufficient for…

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From Group-Level Statistics to Single-Subject Prediction: Machine Learning Detection of Concussion in Retired Athletes

From Group-Level Statistics to Single-Subject Prediction: Machine Learning Detection of Concussion in Retired Athletes
Author(s)3: Rober Boshra, Kiret Dhindsa, Omar Boursalie, Kyle I. Ruiter, Ranil Sonnadara, Reza Samavi, Thomas E. Doyle, James P. Reilly, John F. Connolly
From Group-Level Statistics to Single-Subject Prediction: Machine Learning Detection of Concussion in Retired Athletes 780 435 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

         There has been increased effort to understand the neurophysiological effects of concussion aimed to move diagnosis and identification beyond current subjective behavioral assessments that suffer from poor sensitivity. Recent…

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Mining Within-Trial Oscillatory Brain Dynamics to Address the Variability of Optimized Spatial Filters

Mining Within-Trial Oscillatory Brain Dynamics to Address the Variability of Optimized Spatial Filters
Author(s)3: Andreas Meinel, Henrich Kolkhorst, Michael Tangermann
Mining Within-Trial Oscillatory Brain Dynamics to Address the Variability of Optimized Spatial Filters 780 435 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

   Data-driven spatial filtering algorithms optimize scores, such as the contrast between two conditions to extract oscillatory brain signal components. Most machine learning approaches for the filter estimation, however, disregard…

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TNSRE serves the community of biomedical engineers and clinical researchers who work at the intersection of neuroscience and physical medicine. We publish novel approaches and technologies for better understanding neural systems, human movement, and the relationships between them, with a focus on assistive devices that improve life for patients, for practicing clinicians, and for everyday use.

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