Novel methods for elucidating modality importance in multimodal electrophysiology classifiers
Introduction Multimodal classification is increasingly common in electrophysiology studies.
Many studies use deep learning classifiers with raw time-series data, which makes …
Many studies use deep learning classifiers with raw time-series data, which makes …
A Systematic Approach for Explaining Time and Frequency Features Extracted by Convolutional Neural Networks From Raw Electroencephalography Data
In recent years, the use of convolutional neural networks (CNNs) for raw resting-state
electroencephalography (EEG) analysis has grown increasingly common. However, relative …
electroencephalography (EEG) analysis has grown increasingly common. However, relative …
Examining reproducibility of EEG schizophrenia biomarkers across explainable machine learning models
Schizophrenia (SZ) is a neuropsychiatric disorder that adversely effects millions of
individuals globally. Current diagnostic efforts are symptom based and hampered due to the …
individuals globally. Current diagnostic efforts are symptom based and hampered due to the …
A Framework for Systematically Evaluating the Representations Learned by A Deep Learning Classifier from Raw Multi-Channel Electroencephalogram Data
The application of deep learning methods to raw electroencephalogram (EEG) data is
growing increasingly common. While these methods offer the possibility of improved …
growing increasingly common. While these methods offer the possibility of improved …
Novel Approach Explains Spatio-Spectral Interactions in Raw Electroencephalogram Deep Learning Classifiers
The application of deep learning classifiers to resting-state electroencephalography (rs-
EEG) data has become increasingly common. However, relative to studies using traditional …
EEG) data has become increasingly common. However, relative to studies using traditional …
A Model Visualization-based Approach for Insight into Waveforms and Spectra Learned by CNNs
Recent years have shown a growth in the application of deep learning architectures such as
convolutional neural networks (CNNs), to electrophysiology analysis. However, using neural …
convolutional neural networks (CNNs), to electrophysiology analysis. However, using neural …
Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder via Transfer Learning with Single Channel Sleep Stage Data
As the field of deep learning has grown in recent years, its application to the domain of raw
resting-state electroencephalography (EEG) has also increased. Relative to traditional …
resting-state electroencephalography (EEG) has also increased. Relative to traditional …
Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning …
Y Statsenko, V Babushkin, T Talako, T Kurbatova… - Biomedicines, 2023 - mdpi.com
Deep learning (DL) is emerging as a successful technique for automatic detection and
differentiation of spontaneous seizures that may otherwise be missed or misclassified …
differentiation of spontaneous seizures that may otherwise be missed or misclassified …
Feature Analysis Network: An Interpretable Idea in Deep Learning
Deep Learning (DL) stands out as a leading model for processing high-dimensional data,
where the nonlinear transformation of hidden layers effectively extracts features. However …
where the nonlinear transformation of hidden layers effectively extracts features. However …
Improving Explainability for Single-Channel EEG Deep Learning Classifiers via Interpretable Filters and Activation Analysis
Deep learning methods are increasingly being applied to raw electroencephalography
(EEG) data. Relative to traditional machine learning methods, deep learning methods can …
(EEG) data. Relative to traditional machine learning methods, deep learning methods can …