A Novel Transferability Attention Neural Network Model for EEG Emotion
Recognition
- URL: http://arxiv.org/abs/2009.09585v1
- Date: Mon, 21 Sep 2020 02:42:30 GMT
- Title: A Novel Transferability Attention Neural Network Model for EEG Emotion
Recognition
- Authors: Yang Li, Boxun Fu, Fu Li, Guangming Shi, Wenming Zheng
- Abstract summary: We propose a transferable attention neural network (TANN) for EEG emotion recognition.
TANN learns the emotional discriminative information by highlighting the transferable EEG brain regions data and samples adaptively.
This can be implemented by measuring the outputs of multiple brain-region-level discriminators and one single sample-level discriminator.
- Score: 51.203579838210885
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The existed methods for electroencephalograph (EEG) emotion recognition
always train the models based on all the EEG samples indistinguishably.
However, some of the source (training) samples may lead to a negative influence
because they are significant dissimilar with the target (test) samples. So it
is necessary to give more attention to the EEG samples with strong
transferability rather than forcefully training a classification model by all
the samples. Furthermore, for an EEG sample, from the aspect of neuroscience,
not all the brain regions of an EEG sample contains emotional information that
can transferred to the test data effectively. Even some brain region data will
make strong negative effect for learning the emotional classification model.
Considering these two issues, in this paper, we propose a transferable
attention neural network (TANN) for EEG emotion recognition, which learns the
emotional discriminative information by highlighting the transferable EEG brain
regions data and samples adaptively through local and global attention
mechanism. This can be implemented by measuring the outputs of multiple
brain-region-level discriminators and one single sample-level discriminator. We
conduct the extensive experiments on three public EEG emotional datasets. The
results validate that the proposed model achieves the state-of-the-art
performance.
Related papers
- A Supervised Information Enhanced Multi-Granularity Contrastive Learning Framework for EEG Based Emotion Recognition [14.199298112101802]
This study introduces a novel Supervised Info-enhanced Contrastive Learning framework for EEG based Emotion Recognition (SICLEER)
We propose a joint learning model combining self-supervised contrastive learning loss and supervised classification loss.
arXiv Detail & Related papers (2024-05-12T11:51:00Z) - Joint Contrastive Learning with Feature Alignment for Cross-Corpus EEG-based Emotion Recognition [2.1645626994550664]
We propose a novel Joint Contrastive learning framework with Feature Alignment to address cross-corpus EEG-based emotion recognition.
In the pre-training stage, a joint domain contrastive learning strategy is introduced to characterize generalizable time-frequency representations of EEG signals.
In the fine-tuning stage, JCFA is refined in conjunction with downstream tasks, where the structural connections among brain electrodes are considered.
arXiv Detail & Related papers (2024-04-15T08:21:17Z) - A Knowledge-Driven Cross-view Contrastive Learning for EEG
Representation [48.85731427874065]
This paper proposes a knowledge-driven cross-view contrastive learning framework (KDC2) to extract effective representations from EEG with limited labels.
The KDC2 method creates scalp and neural views of EEG signals, simulating the internal and external representation of brain activity.
By modeling prior neural knowledge based on neural information consistency theory, the proposed method extracts invariant and complementary neural knowledge to generate combined representations.
arXiv Detail & Related papers (2023-09-21T08:53:51Z) - Generate your neural signals from mine: individual-to-individual EEG
converters [0.0]
An ideal individual-to-individual neural converter is expected to generate real neural signals of one subject from those of another one.
We propose a novel individual-to-individual EEG converter, called EEG2EEG, inspired by generative models in computer vision.
arXiv Detail & Related papers (2023-04-21T04:13:16Z) - fMRI from EEG is only Deep Learning away: the use of interpretable DL to
unravel EEG-fMRI relationships [68.8204255655161]
We present an interpretable domain grounded solution to recover the activity of several subcortical regions from multichannel EEG data.
We recover individual spatial and time-frequency patterns of scalp EEG predictive of the hemodynamic signal in the subcortical nuclei.
arXiv Detail & Related papers (2022-10-23T15:11:37Z) - EEG2Vec: Learning Affective EEG Representations via Variational
Autoencoders [27.3162026528455]
We explore whether representing neural data, in response to emotional stimuli, in a latent vector space can serve to both predict emotional states.
We propose a conditional variational autoencoder based framework, EEG2Vec, to learn generative-discriminative representations from EEG data.
arXiv Detail & Related papers (2022-07-16T19:25:29Z) - Task-oriented Self-supervised Learning for Anomaly Detection in
Electroencephalography [51.45515911920534]
A task-oriented self-supervised learning approach is proposed to train a more effective anomaly detector.
A specific two branch convolutional neural network with larger kernels is designed as the feature extractor.
The effectively designed and trained feature extractor has shown to be able to extract better feature representations from EEGs.
arXiv Detail & Related papers (2022-07-04T13:15:08Z) - A Compact and Interpretable Convolutional Neural Network for
Cross-Subject Driver Drowsiness Detection from Single-Channel EEG [4.963467827017178]
We propose a compact and interpretable Convolutional Neural Network (CNN) to discover shared EEG features across different subjects for driver drowsiness detection.
Results show that the proposed model can achieve an average accuracy of 73.22% on 11 subjects for 2-class cross-subject EEG signal classification.
arXiv Detail & Related papers (2021-05-30T14:36:34Z) - Emotional EEG Classification using Connectivity Features and
Convolutional Neural Networks [81.74442855155843]
We introduce a new classification system that utilizes brain connectivity with a CNN and validate its effectiveness via the emotional video classification.
The level of concentration of the brain connectivity related to the emotional property of the target video is correlated with classification performance.
arXiv Detail & Related papers (2021-01-18T13:28:08Z) - Uncovering the structure of clinical EEG signals with self-supervised
learning [64.4754948595556]
Supervised learning paradigms are often limited by the amount of labeled data that is available.
This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG)
By extracting information from unlabeled data, it might be possible to reach competitive performance with deep neural networks.
arXiv Detail & Related papers (2020-07-31T14:34:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.