ArticleLife (Basel, Switzerland)2022
Deep Convolutional Neural Network-Based Visual Stimuli Classification Using Electroencephalography Signals of Healthy and Alzheimer's Disease Subjects.
Article in Life (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 49 citations in OpenAlex.
- The EEG analysis and identification of Alzheimer's disease: a review.Frontiers in aging neuroscience · 2025Pooled it
- Harnessing Few-Shot Learning for EEG signal classification: a survey of state-of-the-art techniques and future directions.Frontiers in human neuroscience · 2024Pooled it
- Cumulative blood pressure exposure and cognition: the potential mediating role of brain volume.Hypertension research : official journal of the Japanese Society of Hypertension · 2026Article
- Generative Adversarial Networks for Modeling Bio-Electric Fields in Medicine: A Review of EEG, ECG, EMG, and EOG Applications.Bioengineering (Basel, Switzerland) · 2026Review
- Artificial intelligence-based analysis of visual electrophysiological signals for clinical interpretation support.Frontiers in neuroscience · 2026Review
- Deep learning-based classification of dementia using image representation of subcortical signals.BMC medical informatics and decision making · 2025Article
- Cognitive load detection through EEG lead wise feature optimization and ensemble classification.Scientific reports · 2025Article
- Lobe-wise cognitive load detection using empirical Fourier decomposition and optimized machine learning.Frontiers in physiology · 2025Article
- A multi-graph convolutional network method for Alzheimer's disease diagnosis based on multi-frequency EEG data with dual-mode connectivity.Frontiers in neuroscience · 2025Article
- Classification of Known and Unknown Study Items in a Memory Task Using Single-Trial Event-Related Potentials and Convolutional Neural Networks.Brain sciences · 2024Article
- A Data Augmentation Method for Motor Imagery EEG Signals Based on DCGAN-GP Network.Brain sciences · 2024Article
- Deep Learning-Based Prediction of Alzheimer's Disease Using Microarray Gene Expression Data.Biomedicines · 2023Article
- Personality-Based Emotion Recognition Using EEG Signals with a CNN-LSTM Network.Brain sciences · 2023Article
- Enhancing the Breast Histopathology Image Analysis for Cancer Detection Using Variational Autoencoder.International journal of environmental research and public health · 2023Article
- On the Influence of Aging on Classification Performance in the Visual EEG Oddball Paradigm Using Statistical and Temporal Features.Life (Basel, Switzerland) · 2023Article
- Electroencephalography and mild cognitive impairment research: A scoping review and bibliometric analysis (ScoRBA).AIMS neuroscience · 2023Article
- qEEG as Biomarker for Alzheimer's Disease: Investigating Relative PSD Difference and Coherence Analysis.Neuropsychiatric disease and treatment · 2023Article
- CNN for a Regression Machine Learning Algorithm for Predicting Cognitive Impairment Using qEEG.Neuropsychiatric disease and treatment · 2023Article
- An Automatic Premature Ventricular Contraction Recognition System Based on Imbalanced Dataset and Pre-Trained Residual Network Using Transfer Learning on ECG Signal.Diagnostics (Basel, Switzerland) · 2022Article
- Framework to Detect Schizophrenia in Brain MRI Slices with Mayfly Algorithm-Selected Deep and Handcrafted Features.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Visual perception is an important part of human life. In the context of facial recognition, it allows us to distinguish between emotions and important facial features that distinguish one person from another. However, subjects suffering from memory loss face significant facial processing problems. If the perception of facial features is affected by memory impairment, then it is possible to classify visual stimuli using brain activity data from the visual processing regions of the brain. This study differentiates the aspects of familiarity and emotion by the inversion effect of the face and uses convolutional neural network (CNN) models (EEGNet, EEGNet SSVEP (steady-state visual evoked potentials), and DeepConvNet) to learn discriminative features from raw electroencephalography (EEG) signals. Due to the limited number of available EEG data samples, Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE) are introduced to generate synthetic EEG signals. The generated data are used to pretrain the models, and the learned weights are initialized to train them on the real EEG data. We investigate minor facial characteristics in brain signals and the ability of deep CNN models to learn them. The effect of face inversion was studied, and it was observed that the N170 component has a considerable and sustained delay. As a result, emotional and familiarity stimuli were divided into two categories based on the posture of the face. The categories of upright and inverted stimuli have the smallest incidences of confusion. The model's ability to learn the face-inversion effect is demonstrated once more.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.