AIMC Topic: Electroencephalography
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A Spatiotemporal Deep Learning Framework for Scalp EEG-Based Automated Pain Assessment in Children.
IEEE transactions on bio-medical engineering
May 20, 2024
OBJECTIVE: Common pain assessment approaches such as self-evaluation and observation scales are inappropriate for children as they require patients to have reasonable communication ability. Subjective, inconsistent, and discontinuous pain assessment ...
TASA: Temporal Attention With Spatial Autoencoder Network for Odor-Induced Emotion Classification Using EEG.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
May 20, 2024
The olfactory system enables humans to smell different odors, which are closely related to emotions. The high temporal resolution and non-invasiveness of Electroencephalogram (EEG) make it suitable to objectively study human preferences for odors. Ef...
Psychological and Brain Responses to Artificial Intelligence's Violation of Community Ethics.
Cyberpsychology, behavior and social networking
May 17, 2024
Human moral reactions to artificial intelligence (AI) agents' behavior constitute an important aspect of modern-day human-AI relationships. Although previous studies have mainly focused on autonomy ethics, this study investigates how individuals judg...
Self-supervised motor imagery EEG recognition model based on 1-D MTCNN-LSTM network.
Journal of neural engineering
May 17, 2024
Aiming for the research on the brain-computer interface (BCI), it is crucial to design a MI-EEG recognition model, possessing a high classification accuracy and strong generalization ability, and not relying on a large number of labeled training samp...
Optimal Channel Selection of Multiclass Motor Imagery Classification Based on Fusion Convolutional Neural Network with Attention Blocks.
Sensors (Basel, Switzerland)
May 16, 2024
The widely adopted paradigm in brain-computer interfaces (BCIs) involves motor imagery (MI), enabling improved communication between humans and machines. EEG signals derived from MI present several challenges due to their inherent characteristics, wh...
Multi-scale 3D-CRU for EEG emotion recognition.
Biomedical physics & engineering express
May 14, 2024
In this paper, we propose a novel multi-scale 3D-CRU model, with the goal of extracting more discriminative emotion feature from EEG signals. By concurrently exploiting the relative electrode locations and different frequency subbands of EEG signals,...
The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach.
Scientific reports
May 13, 2024
Epilepsy surgery is effective for patients with medication-resistant seizures, however 20-40% of them are not seizure free after surgery. Aim of this study is to evaluate the role of linear and non-linear EEG features to predict post-surgical outcome...
Multiclass motor imagery classification with Riemannian geometry and temporal-spectral selection.
Medical & biological engineering & computing
May 9, 2024
Motor imagery (MI) based brain-computer interfaces (BCIs) decode the users' intentions from electroencephalography (EEG) to achieve information control and interaction between the brain and external devices. In this paper, firstly, we apply Riemannia...
Diagnostic biomarker discovery from brain EEG data using LSTM, reservoir-SNN, and NeuCube methods in a pilot study comparing epilepsy and migraine.
Scientific reports
May 9, 2024
The study introduces a new online spike encoding algorithm for spiking neural networks (SNN) and suggests new methods for learning and identifying diagnostic biomarkers using three prominent deep learning neural network models: deep BiLSTM, reservoir...
Association Between Sleep Quality and Deep Learning-Based Sleep Onset Latency Distribution Using an Electroencephalogram.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
May 9, 2024
To evaluate sleep quality, it is necessary to monitor overnight sleep duration. However, sleep monitoring typically requires more than 7 hours, which can be inefficient in termxs of data size and analysis. Therefore, we proposed to develop a deep lea...
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