A novel approach selects EEGs for better brain disease diagnosis.
problem Invalid/noisy EEGs degrade diagnosis performance.
method mwcEEGs: maximum weight clique-based approach.
result Improves classification performance by selecting intra-clique and inter-clique EEGs.
ConvNets improve EEG pathology detection accuracy.
problem Improving automated EEG diagnosis accuracy.
method Two ConvNet architectures, shallow and deep, trained on EEG data.
result ConvNets achieved ~6% better accuracy than previous methods.
Survey of EEG market and machine learning applications.
problem Improving neurology through data-driven research.
method Comprehensive survey of EEG applications and market.
result Machine learning enhances EEG applications and market growth.
A new framework decouples instance representation learning from subject-level supervision in EEG-based disease diagnosis.
problem Inherently assigning subject labels to all instances in EEG-based disease diagnosis leads to unreliable representations.
method BridgeMIL, a two-stage framework that pretrains an encoder without inherited instance labels and then applies subject-level supervision.
result BridgeMIL achieves the highest mean accuracy in 14 of 15 dataset-backbone settings, with an overall mean accuracy of 76.57%.
Study uses EEG features HFD and SampEn to detect depression with high accuracy.
problem Diagnosing depression reliably and accurately.
method Applied Higuchi Fractal Dimension and Sample Entropy on EEG signals using seven machine learning algorithms.
result Good classification possible even with small EEG data, achieving high accuracy.
Paper proposes semi-supervised learning for EEG analysis.
problem Reducing workload and delays in analyzing large unlabeled EEG datasets.
method Semi-supervised deep learning algorithm using minimal labeled data.
result Predictions can be made with as little as 5 labeled examples.
EEG measures brain activity to diagnose ASD more efficiently.
problem Lack of objective measures for early ASD diagnosis.
method Use EEG to classify ASD using machine learning.
result EEG can be a biomarker for ASD.
Scoping review finds EEG key in MCI research, identifying ERP/EEG, QEEG, and machine learning.
problem Identifying MCI early and accurately.
method Scoping review with co-occurrence analysis and PAGER framework.
result Main research themes identified: ERP/EEG, QEEG, and EEG-based machine learning.
A new method detects epileptic events in EEG signals by integrating labeler categories.
problem Human oversight of brief epileptic events in EEG signals leads to inaccurate diagnoses.
method Integrates EEG signal features with one-hot encoded labeler categories for improved detection.
result The method outperforms consensus-trained detectors and maintains confidence bounds.
Wavelet-based CFC improves EEG seizure classification.
problem Improving accuracy in distinguishing ictal seizures from normal brain activity.
method Wavelet-based cross frequency coupling (CFC) for feature extraction, followed by t-test and QDA for classification.
result Wavelet-based CFC enhances classification accuracy of epileptic EEG signals.
Study improves conformal prediction for EEG classification in healthcare, enhancing coverage.
problem Uncertainty quantification in clinical predictions, especially in distribution-shifted settings.
method Personalized calibration strategies to improve coverage of prediction sets.
result Coverage improved by over 20 percentage points with comparable prediction set sizes.
Telemonitoring of electroencephalogram (EEG) through wireless body-area networks is an evolving direction in personalized medicine. Among various constraints in designing such a system, three important constraints are energy consumption, data compression, and device cost. Conventional data compression methodologies, al…
Tool interprets EEGs with high sensitivity and low false alarms.
problem Improving real-time diagnosis of EEGs for clinicians.
method Hybrid machine learning system combining HMM and deep learning.
result Delivers sensitivity above 90% with specificity below 5%.
New framework uses EEG to detect brain atrophy in AD, validated on large AD trial.
problem Diagnosis of Alzheimer's disease relies on subjective clinical interpretations.
method Combines Riemannian tangent space mapping and elastic net regression.
result Developed brain atrophy markers validated on large AD trial.
Machine learning detects epilepsy development from EEG before seizures.
problem Early detection of epilepsy development (epileptogenesis) before seizures.
method Deep CNN combined with prediction aggregation for EEG data analysis.
result Deep learning achieves 99% AUC for EPG detection from EEG recordings.
Novel NDL framework improves spike detection accuracy and channel localization in EEG/MEG data.
problem Manual spike identification is time-consuming and requires specialized training.
method Nested Deep Learning (NDL) framework that combines signals across all channels.
result Improves prediction accuracy and achieves better channel localization.
Deep learning improves automated detection of epileptic seizures.
problem Automated detection of epileptic seizures using traditional methods is limited.
method Deep learning techniques for feature extraction and classification.
result Deep learning enhances accuracy in diagnosing epileptic seizures.
Bayesian neural networks predict AD severity from EEG data.
problem Developing low-cost, non-invasive biomarkers for AD diagnosis and progression.
method Bayesian deep neural networks using QEEG markers.
result Bayesian approach provides uncertainty bounds for AD severity prediction.
Deep learning models predict epileptic seizures with high accuracy.
problem Predicting epileptic seizures for better patient care.
method Developed Temporal Multi-Channel Transformer (TMC-T) and Vision Transformer (TMC-ViT) models for EEG signals.
result TMC-ViT model outperformed CNN in seizure prediction.
SeizureNet classifies EEG seizures with high accuracy.
problem Challenges in classifying epileptic seizures due to signal quality and patient variability.
method Deep learning framework using multi-spectral feature embeddings and knowledge distillation.
result SeizureNet achieves high F1 scores for seizure and patient-wise classification.
DOSED detects sleep micro-architecture events in EEG signals.
problem Manual annotation of sleep micro-architecture events is time-consuming and prone to variability.
method DOSED is a deep learning architecture that jointly predicts event locations, durations, and types in EEG time series.
result DOSED outperforms current state-of-the-art detection methods on 4 datasets and 3 types of events (spindles, K-complexes, arousals).
Deep neural network improves EEG-based epilepsy diagnosis.
problem Automated identification of seizure onset zones in epilepsy patients.
method Residual deep convolutional neural network trained on raw EEG data.
result State-of-the-art performance on epilepsy classification benchmarks.
Model predicts epileptic seizures with high accuracy using EEG signals.
problem Predicting epileptic seizures with high accuracy for diagnosis and treatment.
method Pearson's product-moment correlation coefficient with a linear classifier on generalized Gaussian modeling.
result 100% effectiveness for sensitivity and specificity greater than 83%.
Deep learning classifies sleep stages from EEG, EOG, and EMG signals.
problem Sleep stage classification by sleep experts is time-consuming and prone to errors.
method End-to-end deep learning model using multivariate and multimodal PSG signals.
result Deep learning model achieves state-of-the-art performance on PSG records.
Speech synthesis from EEG features using RNN.
problem Speech synthesis from EEG data.
method Recurrent Neural Network (RNN) regression model to predict acoustic features from EEG features.
result Feasibility of synthesizing speech directly from EEG features demonstrated.
Paper predicts EEG features from acoustic features using RNN and GAN.
problem Predicting EEG features from acoustic features.
method Recurrent Neural Network (RNN) and Generative Adversarial Network (GAN).
result Lower RMSE and normalized RMSE values compared to generating acoustic features from EEG features.
Mobile app for neonatal EEG interpretation helps non-experts diagnose brain health.
problem Limited EEG interpretation skills among neonatal healthcare professionals.
method Low-cost, low-power EEG acquisition system with AI-assisted sonification.
result Improves diagnostic capabilities of non-expert clinicians.
The monitoring of sleep patterns without patient's inconvenience or involvement of a medical specialist is a clinical question of significant importance. To this end, we propose an automatic sleep stage monitoring system based on an affordable, unobtrusive, discreet, and long-term wearable in-ear sensor for recording t…
Adversarial deep learning improves EEG-based person identification.
problem Exploiting temporally correlated structures and session variability in EEG data.
method Adversarial inference approach to learn session-invariant representations.
result Improvements in person identification robustness from longitudinal EEG data.
Deep invertible networks decode EEG signals better than chance.
problem Decoding brain signals from EEG data.
method Deep invertible networks for generating and classifying brain signals.
result Deep invertible networks generate realistic EEG signals and classify novel signals above chance.
EEG signals enhance speaker verification system robustness.
problem Improving speaker verification in noisy environments.
method Used end-to-end deep learning model with EEG and speech features.
result EEG signals improve speaker verification robustness, especially in noisy conditions.
Study compares EEG and fMRI systems, finding tradeoffs in artifact removal and classification accuracy.
problem Dealing with artifacts introduced by simultaneous EEG and fMRI recordings.
method Comparison of three MR compatible EEG recording systems, assessing their performance in single-trial EEG classification.
result Tradeoffs across systems, including setup ease and artifact removal methods.
Machine learning improves EEG pathology classification.
problem Automating clinical EEG analysis using machine learning.
method Developed a comprehensive feature-based framework and compared it to deep neural networks.
result Feature-based framework achieves accuracies similar to deep neural networks.
Generative model for EEG signals using GANs.
problem Generating realistic EEG signals for research and applications.
method Modified Wasserstein GANs for time series generation, including up- and down-sampling.
result Generated naturalistic EEG signals with metrics like Inception score and Frechet inception distance.
Improved speech recognition using EEG and video.
problem Enhancing continuous speech recognition systems.
method Implemented a CTC-based ASR model using EEG features.
result EEG features improve continuous visual speech recognition.
Goal: This paper deals with the problems that some EEG signals have no good sparse representation and single channel processing is not computationally efficient in compressed sensing of multi-channel EEG signals. Methods: An optimization model with L0 norm and Schatten-0 norm is proposed to enforce cosparsity and low r…
Generative model creates EEG data for RSVP experiments.
problem Limited EEG data for training deep learning models.
method Wasserstein Generative Adversarial Network (WGAN-GP) with gradient penalty.
result Improved event classification performance with class-conditioned WGAN-GP.
Enhances spoken speech quality using EEG signals.
problem Improves speech clarity in noisy environments.
method Generative adversarial network (GAN), gated recurrent unit (GRU), temporal convolutional network (TCN) regression models.
result Significant improvement in speech enhancement quality compared to traditional methods.
Framework evaluates deep learning EEG architectures on 100 datasets.
problem Evaluating different deep learning architectures for EEG signal decoding.
method Large-scale evaluation framework with 100 EEG datasets and multiple decoders.
result Comparison of three CNN architectures on different EEG tasks.
Mixed DNN approach improves EEG-based speech imagery recognition.
problem Automatic identification of imagined speech from EEG.
method Hierarchical deep neural network strategy combining CNN, RNN, and autoencoders.
result 23.45% improvement in accuracy over baseline method.
Paper proposes a deep learning method for automatic seizure detection.
problem Manual seizure identification is time-consuming, labor-intensive, and error-prone.
method Leverages attention mechanism and BiLSTM to capture spatial and temporal features.
result Average sensitivity, specificity, and precision of 87.00%, 88.60%, and 88.63% respectively.
Continuous speech recognition from brain activity without vocalization.
problem Recognizing silent speech from EEG signals.
method Implemented a CTC ASR model using EEG signals.
result Demonstrated feasibility of EEG for continuous silent speech recognition.
DSF improves EEG model robustness to missing channels and noise.
problem Robust learning from corrupted EEG data with missing channels.
method Dynamic Spatial Filtering (DSF) as a multi-head attention module.
result DSF achieves up to 29.4% accuracy improvement over baseline models in noisy conditions.
End-to-end neural network extracts graph structure from EEG signals for improved emotional video classification.
problem Challenges in achieving accurate EEG classification for emotional video analysis.
method Proposes an end-to-end neural network model that learns an appropriate multi-layer graph structure from raw EEG signals.
result Improves performance in emotional video classification compared to manually defined connectivity structures.
Semi-supervised GAN for seizure prediction using EEG and unlabeled data.
problem Improving seizure prediction accuracy with limited labeled data.
method Generative Adversarial Network (GAN) trained on unlabeled EEG data with data fusion.
result Seizure prediction accuracy of 77.68% and 75.47% on two datasets.
Paper explores using EEG for better speaker identification, even in noisy environments.
problem Speaker identification performance degrades in background noise.
method Uses EEG signals to enhance speaker identification systems, comparing with acoustic features.
result Speaker identification system using only EEG features outperforms one using only acoustic features in high background noise.
Paper shows continuous speech recognition with EEG features, no speech input.
problem Continuous speech recognition with limited vocabulary and noisy/no speech input.
method Connectionist temporal classification (CTC) model, EEG features, new deep learning architecture.
result Continuous speech recognition achieved on limited vocabulary with noisy/no speech input.
Improved EEG event classification using differential energy.
problem Automatic classification of EEG signals from time frequency representations.
method Comparison of feature extraction techniques, including differential energy and derivatives.
result 24% absolute reduction in error rate, improved discrimination between signal events and noise.