ArticleCognitive neurodynamics2024
STEADYNet: Spatiotemporal EEG analysis for dementia detection using convolutional neural network.
Article in Cognitive neurodynamics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- The AHEPA EEG benchmark: setting the standard for machine learning in dementia diagnosis, a scoping review.Cognitive neurodynamics · 2026Article
- EEG channel selection using metaheuristic algorithms in alcoholism detection using optimal wavelet transform.Scientific reports · 2026Article
- Learning patient-specific spatial biomarker dynamics via operator learning for Alzheimer's disease progression.NPJ systems biology and applications · 2026Article
- Predictive modeling for early diagnosis of dementia using sequential data analysis and data mining.Scientific reports · 2026Article
- Beyond peak accuracy: a stability-centric framework for reliable multimodal student engagement assessment.Scientific reports · 2026Article
- A Multidimensional Benchmark of Public EEG Datasets for Driver State Monitoring in Brain-Computer Interfaces.Sensors (Basel, Switzerland) · 2025Review
- A Bioinspired Multimodal CNN-LSTM Network for EEG Analysis of Patients in Coma.Sensors (Basel, Switzerland) · 2025Article
- Emotion Recognition Using Temporal Facial Skin Temperature and Eye-Opening Degree During Digital Content Viewing for Japanese Older Adults.Sensors (Basel, Switzerland) · 2025Article
- A novel approach hybrid of ensemble learning and 3-D CNN mechanism: early-stage diagnosis of Alzheimer's disease using EEG signals.Scientific reports · 2025Article
- A comprehensive EEG dataset and performance assessment for Autism Spectrum Disorder.Scientific reports · 2025Article
- SPA-IoT with MCSV-CNN: a novel IoT-enabled method for robust pre-ictal seizure prediction.BMC medical informatics and decision making · 2025Article
- Improving EEG based brain computer interface emotion detection with EKO ALSTM model.Scientific reports · 2025Article
- A novel EEG artifact removal algorithm based on an advanced attention mechanism.Scientific reports · 2025Article
- ECGEL: a multimodal 12-lead ECG classification model for heart failure prediction.Biomedical engineering letters · 2025Article
- An explainable and efficient deep learning framework for EEG-based diagnosis of Alzheimer's disease and frontotemporal dementia.Frontiers in medicine · 2025Article
- Article
- Contribution of Scalp Regions to Machine Learning-Based Classification of Dementia Utilizing Resting-State qEEG Signals.Neuropsychiatric disease and treatment · 2024Article
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5 authors.
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Abstract
Dementia is a neuro-degenerative disorder with a high death rate, mainly due to high human error, time, and cost of the current clinical diagnostic techniques. The existing dementia detection methods using hand-crafted electroencephalogram (EEG) signal features are unreliable. A convolution neural network using spatiotemporal EEG signals (STEADYNet) is presented to improve the dementia detection. The STEADYNet uses a multichannel temporal EEG signal as input. The network is grouped into feature extraction and classification components. The feature extraction comprises two convolution layers to generate complex features, a max-pooling layer to reduce the EEG signal's spatiotemporal redundancy, and a dropout layer to improve the network's generalization. The classification processes the feature extraction output nonlinearly using two fully-connected layers to generate salient features and a softmax layer to generate disease probabilities. Two publicly available multiclass datasets of dementia are used for evaluation. The STEADYNet outperforms existing automatic dementia detection methods with accuracies of
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