Learning Robust Deep Visual Representations from EEG Brain Recordings

• URL: http://arxiv.org/abs/2310.16532v1
• Date: Wed, 25 Oct 2023 10:26:07 GMT
• Title: Learning Robust Deep Visual Representations from EEG Brain Recordings
• Authors: Prajwal Singh, Dwip Dalal, Gautam Vashishtha, Krishna Miyapuram, Shanmuganathan Raman
• Abstract summary: 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.
• Score: 13.768240137063428
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Decoding the human brain has been a hallmark of neuroscientists and Artificial Intelligence researchers alike. Reconstruction of visual images from brain Electroencephalography (EEG) signals has garnered a lot of interest due to its applications in brain-computer interfacing. This study proposes a two-stage method where the first step is to obtain EEG-derived features for robust learning of deep representations and subsequently utilize the learned representation for image generation and classification. We demonstrate the generalizability of our feature extraction pipeline across three different datasets using deep-learning architectures with supervised and contrastive learning methods. We have performed the zero-shot EEG classification task to support the generalizability claim further. We observed that a subject invariant linearly separable visual representation was learned using EEG data alone in an unimodal setting that gives better k-means accuracy as compared to a joint representation learning between EEG and images. Finally, we propose a novel framework to transform unseen images into the EEG space and reconstruct them with approximation, showcasing the potential for image reconstruction from EEG signals. Our proposed image synthesis method from EEG shows 62.9% and 36.13% inception score improvement on the EEGCVPR40 and the Thoughtviz datasets, which is better than state-of-the-art performance in GAN.

Related papers

• Neuro-3D: Towards 3D Visual Decoding from EEG Signals [49.502364730056044]
  We introduce a new neuroscience task: decoding 3D visual perception from EEG signals. We first present EEG-3D, a dataset featuring multimodal analysis data and EEG recordings from 12 subjects viewing 72 categories of 3D objects rendered in both videos and images. We propose Neuro-3D, a 3D visual decoding framework based on EEG signals.
  arXiv  Detail & Related papers  (2024-11-19T05:52:17Z)
• 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)
• Reconstructing Visual Stimulus Images from EEG Signals Based on Deep Visual Representation Model [5.483279087074447]
  We propose a novel image reconstruction method based on EEG signals in this paper. To satisfy the high recognizability of visual stimulus images in fast switching manner, we build a visual stimuli image dataset. Deep visual representation model(DVRM) consisting of a primary encoder and a subordinate decoder is proposed to reconstruct visual stimuli.
  arXiv  Detail & Related papers  (2024-03-11T09:19:09Z)
• 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)
• 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)
• Seeing through the Brain: Image Reconstruction of Visual Perception from Human Brain Signals [27.92796103924193]
  We propose a comprehensive pipeline, named NeuroImagen, for reconstructing visual stimuli images from EEG signals. We incorporate a novel multi-level perceptual information decoding to draw multi-grained outputs from the given EEG data.
  arXiv  Detail & Related papers  (2023-07-27T12:54:16Z)
• Joint fMRI Decoding and Encoding with Latent Embedding Alignment [77.66508125297754]
  We introduce a unified framework that addresses both fMRI decoding and encoding. Our model concurrently recovers visual stimuli from fMRI signals and predicts brain activity from images within a unified framework.
  arXiv  Detail & Related papers  (2023-03-26T14:14:58Z)
• 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)
• Joint Deep Learning of Facial Expression Synthesis and Recognition [97.19528464266824]
  We propose a novel joint deep learning of facial expression synthesis and recognition method for effective FER. The proposed method involves a two-stage learning procedure. Firstly, a facial expression synthesis generative adversarial network (FESGAN) is pre-trained to generate facial images with different facial expressions. In order to alleviate the problem of data bias between the real images and the synthetic images, we propose an intra-class loss with a novel real data-guided back-propagation (RDBP) algorithm.
  arXiv  Detail & Related papers  (2020-02-06T10:56:00Z)

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.