OOIR: Observatory of International Research

Papers

(The H4-Index of IEEE Transactions on Neural Systems and Rehabilitation Engineering is 45. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-04-01 to 2024-04-01.)

Article	Citations
An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG	233
Brain-Controlled Robotic Arm System Based on Multi-Directional CNN-BiLSTM Network Using EEG Signals	182
Epileptic Seizure Detection in EEG Signals Using a Unified Temporal-Spectral Squeeze-and-Excitation Network	164
A Computerized Method for Automatic Detection of Schizophrenia Using EEG Signals	112
Performance Evaluation of Lower Limb Exoskeletons: A Systematic Review	105
Multi-View Spatial-Temporal Graph Convolutional Networks With Domain Generalization for Sleep Stage Classification	102
Bispectrum-Based Channel Selection for Motor Imagery Based Brain-Computer Interfacing	93
Transcutaneous Spinal Cord Stimulation Restores Hand and Arm Function After Spinal Cord Injury	93
Manifold Embedded Knowledge Transfer for Brain-Computer Interfaces	92
A New Framework for Automatic Detection of Patients With Mild Cognitive Impairment Using Resting-State EEG Signals	82
Improving the Performance of Individually Calibrated SSVEP-BCI by Task- Discriminant Component Analysis	75
Learning Common Time-Frequency-Spatial Patterns for Motor Imagery Classification	69
Wearable Assistive Tactile Communication Interface Based on Integrated Touch Sensors and Actuators	68
Neural Decoding of Imagined Speech and Visual Imagery as Intuitive Paradigms for BCI Communication	67
FS-HGR: Few-Shot Learning for Hand Gesture Recognition via Electromyography	67
A Multi-Scale Fusion Convolutional Neural Network Based on Attention Mechanism for the Visualization Analysis of EEG Signals Decoding	66
A Temporal-Spectral-Based Squeeze-and- Excitation Feature Fusion Network for Motor Imagery EEG Decoding	65
Detecting High-Functioning Autism in Adults Using Eye Tracking and Machine Learning	65
Physics-Informed Deep Learning for Musculoskeletal Modeling: Predicting Muscle Forces and Joint Kinematics From Surface EMG	64
Dreem Open Datasets: Multi-Scored Sleep Datasets to Compare Human and Automated Sleep Staging	61
How Sensitive Are EEG Results to Preprocessing Methods: A Benchmarking Study	61
Combination of Augmented Reality Based Brain- Computer Interface and Computer Vision for High-Level Control of a Robotic Arm	60
Design and Experimental Evaluation of a Semi-Passive Upper-Limb Exoskeleton for Workers With Motorized Tuning of Assistance	59
Motor Imagery EEG Decoding Method Based on a Discriminative Feature Learning Strategy	55
Enhancing EEG-Based Classification of Depression Patients Using Spatial Information	54

A Transformer-Based Approach Combining Deep Learning Network and Spatial-Temporal Information for Raw EEG Classification	53
Brain Functional Networks Based on Resting-State EEG Data for Major Depressive Disorder Analysis and Classification	53
Computer Vision to Automatically Assess Infant Neuromotor Risk	53
Symbitron Exoskeleton: Design, Control, and Evaluation of a Modular Exoskeleton for Incomplete and Complete Spinal Cord Injured Individuals	53
Speech Vision: An End-to-End Deep Learning-Based Dysarthric Automatic Speech Recognition System	52
RobustSleepNet: Transfer Learning for Automated Sleep Staging at Scale	52
EEG-Inception: A Novel Deep Convolutional Neural Network for Assistive ERP-Based Brain-Computer Interfaces	51
Deep Temporal-Spatial Feature Learning for Motor Imagery-Based Brain–Computer Interfaces	51
Seizure Prediction Using Directed Transfer Function and Convolution Neural Network on Intracranial EEG	51
Observing Actions Through Immersive Virtual Reality Enhances Motor Imagery Training	51
DeepSleepNet-Lite: A Simplified Automatic Sleep Stage Scoring Model With Uncertainty Estimates	49
An Effective Dual Self-Attention Residual Network for Seizure Prediction	49
A Low-Cost Lower-Limb Brain-Machine Interface Triggered by Pedaling Motor Imagery for Post-Stroke Patients Rehabilitation	49
Inter- and Intra-Subject Transfer Reduces Calibration Effort for High-Speed SSVEP-Based BCIs	48
Different Set Domain Adaptation for Brain-Computer Interfaces: A Label Alignment Approach	48
Open Access Dataset, Toolbox and Benchmark Processing Results of High-Density Surface Electromyogram Recordings	46
An Improved Performance of Deep Learning Based on Convolution Neural Network to Classify the Hand Motion by Evaluating Hyper Parameter	46
Motor Imagery Hand Movement Direction Decoding Using Brain Computer Interface to Aid Stroke Recovery and Rehabilitation	45
Enhanced Motor Imagery Based Brain- Computer Interface via FES and VR for Lower Limbs	45
Self-Aligning Mechanism Improves Comfort and Performance With a Powered Knee Exoskeleton	45