Journal of Neuroscience Methods

Papers
(The H4-Index of Journal of Neuroscience Methods is 34. 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 2020-11-01 to 2024-11-01.)
ArticleCitations
Data augmentation for deep-learning-based electroencephalography235
NODDI in clinical research157
Grad-CAM helps interpret the deep learning models trained to classify multiple sclerosis types using clinical brain magnetic resonance imaging77
High-pass filtering artifacts in multivariate classification of neural time series data76
A major depressive disorder classification framework based on EEG signals using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis71
A comprehensive assessment of Brain Computer Interfaces: Recent trends and challenges65
Animal models of pain: Diversity and benefits63
The rt-TEP tool: real-time visualization of TMS-Evoked Potentials to maximize cortical activation and minimize artifacts62
TMS-induced silent periods: A review of methods and call for consistency61
TAAC - TMS Adaptable Auditory Control: A universal tool to mask TMS clicks59
MI-EEGNET: A novel convolutional neural network for motor imagery classification58
The sensitivity of diffusion MRI to microstructural properties and experimental factors58
A convolutional-recurrent neural network approach to resting-state EEG classification in Parkinson’s disease56
CWT Based Transfer Learning for Motor Imagery Classification for Brain computer Interfaces55
Efficient whole brain transduction by systemic infusion of minimally purified AAV-PHP.eB53
Through the looking glass: A review of cranial window technology for optical access to the brain53
Towards in vivo g-ratio mapping using MRI: Unifying myelin and diffusion imaging48
Non-invasive brain stimulation for Parkinson’s disease: Clinical evidence, latest concepts and future goals: A systematic review46
Neural anatomy and optical microscopy (NAOMi) simulation for evaluating calcium imaging methods46
Model-based geometrical optimisation and in vivo validation of a spatially selective multielectrode cuff array for vagus nerve neuromodulation46
Automatic classification methods for detecting drowsiness using wavelet packet transform extracted time-domain features from single-channel EEG signal45
The role of electroencephalography electrical reference in the assessment of functional brain–heart interplay: From methodology to user guidelines45
Gradient waveform design for tensor-valued encoding in diffusion MRI44
A novel approach for automated alcoholism detection using Fourier decomposition method42
EEG Integrated Platform Lossless (EEG-IP-L) pre-processing pipeline for objective signal quality assessment incorporating data annotation and blind source separation41
Classification of Parkinson's disease based on multi-modal features and stacking ensemble learning41
Single-trial EEG emotion recognition using Granger Causality/Transfer Entropy analysis41
A novel approach to correlate the salivary exosomes and their protein cargo in the progression of cognitive impairment into Alzheimer’s disease39
Reproducibility of power spectrum, functional connectivity and network construction in resting-state EEG39
A review of critical challenges in MI-BCI: From conventional to deep learning methods36
Use of common average reference and large-Laplacian spatial-filters enhances EEG signal-to-noise ratios in intrinsic sensorimotor activity36
A drug-vs-food “choice” self-administration procedure in rats to investigate pharmacological and environmental mechanisms of substance use disorders35
Neural tissue-microelectrode interaction: Brain micromotion, electrical impedance, and flexible microelectrode insertion35
Machine learning applications on neuroimaging for diagnosis and prognosis of epilepsy: A review34
Use of the CatWalk gait analysis system to assess functional recovery in rodent models of peripheral nerve injury – a systematic review34
Diagnostic classification of autism using resting-state fMRI data improves with full correlation functional brain connectivity compared to partial correlation34
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