EEG-ImageNet: An Electroencephalogram Dataset and Benchmarks with Image Visual Stimuli of Multi-Granularity Labels

• URL: http://arxiv.org/abs/2406.07151v1
• Date: Tue, 11 Jun 2024 10:52:17 GMT
• Title: EEG-ImageNet: An Electroencephalogram Dataset and Benchmarks with Image Visual Stimuli of Multi-Granularity Labels
• Authors: Shuqi Zhu, Ziyi Ye, Qingyao Ai, Yiqun Liu,
• Abstract summary: We introduce EEG-ImageNet, a novel EEG dataset comprising recordings from 16 subjects exposed to 4000 images selected from the ImageNet dataset. EEG-ImageNet consists of 5 times EEG-image pairs larger than existing similar EEG benchmarks. Based on it, we establish benchmarks for object classification and image reconstruction. Experiments with several commonly used models show that the best models can achieve object classification with accuracy around 60% and image reconstruction with two-way identification around 64%.
• Score: 12.783945503890962
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Identifying and reconstructing what we see from brain activity gives us a special insight into investigating how the biological visual system represents the world. While recent efforts have achieved high-performance image classification and high-quality image reconstruction from brain signals collected by Functional Magnetic Resonance Imaging (fMRI) or magnetoencephalogram (MEG), the expensiveness and bulkiness of these devices make relevant applications difficult to generalize to practical applications. On the other hand, Electroencephalography (EEG), despite its advantages of ease of use, cost-efficiency, high temporal resolution, and non-invasive nature, has not been fully explored in relevant studies due to the lack of comprehensive datasets. To address this gap, we introduce EEG-ImageNet, a novel EEG dataset comprising recordings from 16 subjects exposed to 4000 images selected from the ImageNet dataset. EEG-ImageNet consists of 5 times EEG-image pairs larger than existing similar EEG benchmarks. EEG-ImageNet is collected with image stimuli of multi-granularity labels, i.e., 40 images with coarse-grained labels and 40 with fine-grained labels. Based on it, we establish benchmarks for object classification and image reconstruction. Experiments with several commonly used models show that the best models can achieve object classification with accuracy around 60% and image reconstruction with two-way identification around 64%. These results demonstrate the dataset's potential to advance EEG-based visual brain-computer interfaces, understand the visual perception of biological systems, and provide potential applications in improving machine visual models.

Related papers

• NECOMIMI: Neural-Cognitive Multimodal EEG-informed Image Generation with Diffusion Models [0.0]
  NECOMIMI introduces a novel framework for generating images directly from EEG signals using advanced diffusion models. The proposed NERV EEG encoder demonstrates state-of-the-art (SoTA) performance across multiple zero-shot classification tasks. We introduce the CAT Score as a new metric tailored for EEG-to-image evaluation and establish a benchmark on the ThingsEEG dataset.
  arXiv  Detail & Related papers  (2024-10-01T14:05:30Z)
• Mind's Eye: Image Recognition by EEG via Multimodal Similarity-Keeping Contrastive Learning [2.087148326341881]
  This paper introduces a MUltimodal Similarity-keeping contrastivE learning framework for zero-shot EEG-based image classification. We develop a series of multivariate time-series encoders tailored for EEG signals and assess the efficacy of regularized contrastive EEG-Image pretraining. Our method achieves state-of-the-art performance, with a top-1 accuracy of 19.3% and a top-5 accuracy of 48.8% in 200-way zero-shot image classification.
  arXiv  Detail & Related papers  (2024-06-05T16:42:23Z)
• Alleviating Catastrophic Forgetting in Facial Expression Recognition with Emotion-Centered Models [49.3179290313959]
  The proposed method, emotion-centered generative replay (ECgr), tackles this challenge by integrating synthetic images from generative adversarial networks. ECgr incorporates a quality assurance algorithm to ensure the fidelity of generated images. The experimental results on four diverse facial expression datasets demonstrate that incorporating images generated by our pseudo-rehearsal method enhances training on the targeted dataset and the source dataset.
  arXiv  Detail & Related papers  (2024-04-18T15:28:34Z)
• Psychometry: An Omnifit Model for Image Reconstruction from Human Brain Activity [60.983327742457995]
  Reconstructing the viewed images from human brain activity bridges human and computer vision through the Brain-Computer Interface. We devise Psychometry, an omnifit model for reconstructing images from functional Magnetic Resonance Imaging (fMRI) obtained from different subjects.
  arXiv  Detail & Related papers  (2024-03-29T07:16:34Z)
• Learning Robust Deep Visual Representations from EEG Brain Recordings [13.768240137063428]
  This study proposes a two-stage method where the first step is to obtain EEG-derived features for robust learning of deep representations. We demonstrate the generalizability of our feature extraction pipeline across three different datasets using deep-learning architectures. We propose a novel framework to transform unseen images into the EEG space and reconstruct them with approximation.
  arXiv  Detail & Related papers  (2023-10-25T10:26:07Z)
• Decoding visual brain representations from electroencephalography through Knowledge Distillation and latent diffusion models [0.12289361708127873]
  We present an innovative method that employs to classify and reconstruct images from the ImageNet dataset using electroencephalography (EEG) data. We analyzed EEG recordings from 6 participants, each exposed to 50 images spanning 40 unique semantic categories. We incorporated an image reconstruction mechanism based on pre-trained latent diffusion models, which allowed us to generate an estimate of the images which had elicited EEG activity.
  arXiv  Detail & Related papers  (2023-09-08T09:13:50Z)
• Decoding Natural Images from EEG for Object Recognition [8.411976038504589]
  This paper presents a self-supervised framework to demonstrate the feasibility of learning image representations from EEG signals. We achieve a top-1 accuracy of 15.6% and a top-5 accuracy of 42.8% in challenging 200-way zero-shot tasks. These findings yield valuable insights for neural decoding and brain-computer interfaces in real-world scenarios.
  arXiv  Detail & Related papers  (2023-08-25T08:05:37Z)
• A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications [56.458448869572294]
  We introduce DETRtime, a novel framework for time-series segmentation of EEG data. Our end-to-end deep learning-based framework brings advances in Computer Vision to the forefront. Our model generalizes well in the task of EEG sleep stage segmentation.
  arXiv  Detail & Related papers  (2022-06-17T10:17:24Z)
• OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing [62.993663757843464]
  Optoacoustic (OA) imaging is based on excitation of biological tissues with nanosecond-duration laser pulses followed by detection of ultrasound waves generated via light-absorption-mediated thermoelastic expansion. OA imaging features a powerful combination between rich optical contrast and high resolution in deep tissues. No standardized datasets generated with different types of experimental set-up and associated processing methods are available to facilitate advances in broader applications of OA in clinical settings.
  arXiv  Detail & Related papers  (2022-06-17T08:11:26Z)
• Identity-Aware CycleGAN for Face Photo-Sketch Synthesis and Recognition [61.87842307164351]
  We first propose an Identity-Aware CycleGAN (IACycleGAN) model that applies a new perceptual loss to supervise the image generation network. It improves CycleGAN on photo-sketch synthesis by paying more attention to the synthesis of key facial regions, such as eyes and nose. We develop a mutual optimization procedure between the synthesis model and the recognition model, which iteratively synthesizes better images by IACycleGAN.
  arXiv  Detail & Related papers  (2021-03-30T01:30:08Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)

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.