Rethinking Saliency Map: An Context-aware Perturbation Method to Explain EEG-based Deep Learning Model

• URL: http://arxiv.org/abs/2205.14976v1
• Date: Mon, 30 May 2022 10:25:20 GMT
• Title: Rethinking Saliency Map: An Context-aware Perturbation Method to Explain EEG-based Deep Learning Model
• Authors: Hanqi Wang, Xiaoguang Zhu, Tao Chen, Chengfang Li, Liang Song
• Abstract summary: We conduct a review to summarize the existing works explaining the EEG-based deep learning model. We suggest a context-aware method to generate a saliency map from the perspective of the raw EEG signal. We also justify that the context information can be used to suppress the artifacts in the EEG-based deep learning model.
• Score: 7.693117960747748
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Deep learning is widely used to decode the electroencephalogram (EEG) signal. However, there are few attempts to specifically investigate how to explain the EEG-based deep learning models. We conduct a review to summarize the existing works explaining the EEG-based deep learning model. Unfortunately, we find that there is no appropriate method to explain them. Based on the characteristic of EEG data, we suggest a context-aware perturbation method to generate a saliency map from the perspective of the raw EEG signal. Moreover, we also justify that the context information can be used to suppress the artifacts in the EEG-based deep learning model. In practice, some users might want a simple version of the explanation, which only indicates a few features as salient points. To this end, we propose an optional area limitation strategy to restrict the highlighted region. To validate our idea and make a comparison with the other methods, we select three representative EEG-based models to implement experiments on the emotional EEG dataset DEAP. The results of the experiments support the advantages of our method.

Related papers

• Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation [52.51005875755718]
  We focus on EEG-to-text decoding and address its hallucination issue through the lens of posterior collapse.<n>Acknowledging the underlying mismatch in information capacity between EEG and text, we reframe the decoding task as semantic summarization of core meanings.<n>Experiments on the public ZuCo dataset demonstrate that GLIM consistently generates fluent, EEG-grounded sentences.
  arXiv  Detail & Related papers  (2025-05-21T05:29:55Z)
• EEG-CLIP : Learning EEG representations from natural language descriptions [1.03590082373586]
  Deep networks for electroencephalogram decoding are often trained to only solve a specific task like pathology or gender decoding. A more general approach leveraging the medical reports of clinical EEG recordings is to learn mappings between medical reports and EEG recordings. We develop a contrastive learning framework EEG-CLIP that aligns EEG time series and their corresponding clinical text descriptions in a shared embedding space.
  arXiv  Detail & Related papers  (2025-03-18T11:12:15Z)
• Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future [119.88454942558485]
  Underwater object detection (UOD) aims to identify and localise objects in underwater images or videos. In recent years, artificial intelligence (AI) based methods, especially deep learning methods, have shown promising performance in UOD.
  arXiv  Detail & Related papers  (2024-10-08T00:25:33Z)
• Towards Linguistic Neural Representation Learning and Sentence Retrieval from Electroencephalogram Recordings [27.418738450536047]
  We propose a two-step pipeline for converting EEG signals into sentences. We first confirm that word-level semantic information can be learned from EEG data recorded during natural reading. We employ a training-free retrieval method to retrieve sentences based on the predictions from the EEG encoder.
  arXiv  Detail & Related papers  (2024-08-08T03:40:25Z)
• 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)
• Opening the Black-Box: A Systematic Review on Explainable AI in Remote Sensing [51.524108608250074]
  Black-box machine learning approaches have become a dominant modeling paradigm for knowledge extraction in remote sensing. We perform a systematic review to identify the key trends in the field and shed light on novel explainable AI approaches. We also give a detailed outlook on the challenges and promising research directions.
  arXiv  Detail & Related papers  (2024-02-21T13:19:58Z)
• 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)
• DGSD: Dynamical Graph Self-Distillation for EEG-Based Auditory Spatial Attention Detection [49.196182908826565]
  Auditory Attention Detection (AAD) aims to detect target speaker from brain signals in a multi-speaker environment. Current approaches primarily rely on traditional convolutional neural network designed for processing Euclidean data like images. This paper proposes a dynamical graph self-distillation (DGSD) approach for AAD, which does not require speech stimuli as input.
  arXiv  Detail & Related papers  (2023-09-07T13:43:46Z)
• Towards Best Practice of Interpreting Deep Learning Models for EEG-based Brain Computer Interfaces [1.5670669686642233]
  We evaluate different deep interpretation techniques on EEG datasets. The results reveal the importance of selecting a proper interpretation technique as the initial step. We propose a set of procedures that allow the interpretation results to be presented in an understandable and trusted way.
  arXiv  Detail & Related papers  (2022-02-12T07:54:12Z)
• EEG-based Cross-Subject Driver Drowsiness Recognition with an Interpretable Convolutional Neural Network [0.0]
  We develop a novel convolutional neural network combined with an interpretation technique that allows sample-wise analysis of important features for classification. Results show that the model achieves an average accuracy of 78.35% on 11 subjects for leave-one-out cross-subject recognition.
  arXiv  Detail & Related papers  (2021-05-30T14:47:20Z)
• A Novel Transferability Attention Neural Network Model for EEG Emotion Recognition [51.203579838210885]
  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.
  arXiv  Detail & Related papers  (2020-09-21T02:42:30Z)
• Deep learning denoising for EOG artifacts removal from EEG signals [0.5243460995467893]
  One of the most challenging issues in EEG denoising processes is removing the ocular artifacts. In this paper, we build and train a deep learning model to deal with this challenge and remove the ocular artifacts effectively. We proposed three different schemes and made our U-NET based models learn to purify contaminated EEG signals.
  arXiv  Detail & Related papers  (2020-09-12T23:28:12Z)
• 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.