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Found 7 papers, 1 papers with code
Seeing through the Brain: Image Reconstruction of Visual Perception from Human Brain Signals
Seeing is believing, however, the underlying mechanism of how human visual perceptions are intertwined with our cognitions is still a mystery.
Investigating EEG-Based Functional Connectivity Patterns for Multimodal Emotion Recognition
The discrimination ability of the EEG connectivity features in emotion recognition is evaluated on three public emotion EEG datasets: SEED, SEED-V, and DEAP.
Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis
We evaluate the performance of DCCA on five multimodal datasets: the SEED, SEED-IV, SEED-V, DEAP, and DREAMER datasets.
Semi-supervised Bayesian Deep Multi-modal Emotion Recognition
In this paper, we first build a multi-view deep generative model to simulate the generative process of multi-modality emotional data.
Multimodal Emotion Recognition Using Multimodal Deep Learning
To enhance the performance of affective models and reduce the cost of acquiring physiological signals for real-world applications, we adopt multimodal deep learning approach to construct affective models from multiple physiological signals.
Identifying Stable Patterns over Time for Emotion Recognition from EEG
In this paper, we investigate stable patterns of electroencephalogram (EEG) over time for emotion recognition using a machine learning approach.
Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks
To investigate critical frequency bands and channels, this paper introduces deep belief networks (DBNs) to constructing EEG-based emotion recognition models for three emotions: positive, neutral and negative.
Ranked #1 on
Electroencephalogram (EEG)
on SEED
