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Found 24 papers, 2 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.
PDAML: A Pseudo Domain Adaptation Paradigm for Subject-independent EEG-based Emotion Recognition
Usually, the DA methods give relatively promising results than the DG methods but require additional computation resources each time a new subject comes.
Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous GNNs
Current graph neural networks (GNNs) lack generalizability with respect to scales (graph sizes, graph diameters, edge weights, etc..) when solving many graph analysis problems.
Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous Graph Neural Networks
Current graph neural networks (GNNs) lack generalizability with respect to scales (graph sizes, graph diameters, edge weights, etc..) when solving many graph analysis problems.
Document-level Neural Machine Translation with Document Embeddings
Standard neural machine translation (NMT) is on the assumption of document-level context independent.
Data Augmentation for Enhancing EEG-based Emotion Recognition with Deep Generative Models
Then, we augment the original training datasets with a different number of generated realistic-like EEG data.
Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016
Usually, a calibration session is needed to collect some training data for a new subject, which is time-consuming and user unfriendly.
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.
Document-level Neural Machine Translation with Associated Memory Network
Most existing document-level NMT approaches are satisfied with a smattering sense of global document-level information, while this work focuses on exploiting detailed document-level context in terms of a memory network.
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.
Judging Chemical Reaction Practicality From Positive Sample only Learning
Chemical reaction practicality is the core task among all symbol intelligence based chemical information processing, for example, it provides indispensable clue for further automatic synthesis route inference.
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.
A Novel Bilingual Word Embedding Method for Lexical Translation Using Bilingual Sense Clique
Most of the existing methods for bilingual word embedding only consider shallow context or simple co-occurrence information.
Connecting Phrase based Statistical Machine Translation Adaptation
Although more additional corpora are now available for Statistical Machine Translation (SMT), only the ones which belong to the same or similar domains with the original corpus can indeed enhance SMT performance directly.
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.
On the Reducibility of Submodular Functions
The scalability of submodular optimization methods is critical for their usability in practice.
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
