AIMC Topic: Electroencephalography

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3DCANN: A Spatio-Temporal Convolution Attention Neural Network for EEG Emotion Recognition.

IEEE journal of biomedical and health informatics Nov 10, 2022
Since electroencephalogram (EEG) signals can truly reflect human emotional state, emotion recognition based on EEG has turned into a critical branch in the field of artificial intelligence. Aiming at the disparity of EEG signals in various emotional ...
Humans Emotions Electroencephalography Attention Neural Networks, Computer Artificial Intelligence
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Emotion recognition while applying cosmetic cream using deep learning from EEG data; cross-subject analysis.

PloS one Nov 10, 2022
We report a deep learning-based emotion recognition method using EEG data collected while applying cosmetic creams. Four creams with different textures were randomly applied, and they were divided into two classes, "like (positive)" and "dislike (neg...
Emotions Humans Research Design Electroencephalography Neural Networks, Computer Deep Learning
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The Deep Learning Method Differentiates Patients with Bipolar Disorder from Controls with High Accuracy Using EEG Data.

Clinical EEG and neuroscience Nov 6, 2022
Bipolar disorder (BD) is a mental disorder characterized by depressive and manic or hypomanic episodes. The complexity in the diagnosis of Bipolar disorder (BD) due to its overlapping symptoms with other mood disorders prompted researchers and clini...
Electroencephalography Deep Learning Bipolar Disorder Humans Neural Networks, Computer Algorithms
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M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity.

Sensors (Basel, Switzerland) Nov 3, 2022
Emotion recognition, or the ability of computers to interpret people's emotional states, is a very active research area with vast applications to improve people's lives. However, most image-based emotion recognition techniques are flawed, as humans c...
Humans Electroencephalography Deep Learning Emotions Neural Networks, Computer Reproducibility of Results
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A review of critical challenges in MI-BCI: From conventional to deep learning methods.

Journal of neuroscience methods Oct 29, 2022
Brain-computer interfaces (BCIs) have achieved significant success in controlling external devices through the Electroencephalogram (EEG) signal processing. BCI-based Motor Imagery (MI) system bridges brain and external devices as communication tools...
Deep Learning Humans Electroencephalography Imagination Algorithms Signal Processing, Computer-Assisted Brain-Computer Interfaces
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Early diagnosis of Alzheimer's disease and mild cognitive impairment based on electroencephalography: From the perspective of event related potentials and deep learning.

International journal of psychophysiology : official journal of the International Organization of Psychophysiology Oct 27, 2022
Alzheimer's disease (AD), a neurodegenerative disorder characterized by progressive cognitive decline, is generally prevalent in elderly people with significant disability and mortality. There is no effective treatment for AD currently, but the early...
Evoked Potentials Cognitive Dysfunction Alzheimer Disease Aged Electroencephalography Early Diagnosis Deep Learning Humans
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Ensemble Support Vector Recurrent Neural Network for Brain Signal Detection.

IEEE transactions on neural networks and learning systems Oct 27, 2022
The brain-computer interface (BCI) P300 speller analyzes the P300 signals from the brain to achieve direct communication between humans and machines, which can assist patients with severe disabilities to control external machines or robots to complet...
Event-Related Potentials, P300 Brain Support Vector Machine Electroencephalography Neural Networks, Computer Brain-Computer Interfaces Algorithms Humans
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Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data With Spatial Information.

IEEE transactions on neural networks and learning systems Oct 27, 2022
Efficient processing of large-scale time-series data is an intricate problem in machine learning. Conventional sensor signal processing pipelines with hand-engineered feature extraction often involve huge computational costs with high dimensional dat...
Neural Networks, Computer Machine Learning Humans Signal Processing, Computer-Assisted Electroencephalography Seizures
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Deep Learning-Based Detection of Epileptiform Discharges for Self-Limited Epilepsy With Centrotemporal Spikes.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Oct 27, 2022
Centrotemporal spike-waves (CTSWs) are typical interictal epileptiform discharges (IEDs) observed in centrotemporal regions in self-limited epilepsy with centrotemporal spikes (SLECTS). This study aims to develop a deep learning-based approach for au...
Epilepsy Humans Deep Learning Scalp Neural Networks, Computer Electroencephalography
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Graph Theoretical Analysis of EEG Functional Connectivity Patterns and Fusion with Physiological Signals for Emotion Recognition.

Sensors (Basel, Switzerland) Oct 26, 2022
Emotion recognition is a key attribute for realizing advances in human-computer interaction, especially when using non-intrusive physiological sensors, such as electroencephalograph (EEG) and electrocardiograph. Although functional connectivity of EE...
Electroencephalography Algorithms Arousal Emotions Humans Neural Networks, Computer
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