Subject Adaptive EEG-based Visual Recognition

• URL: http://arxiv.org/abs/2110.13470v1
• Date: Tue, 26 Oct 2021 08:06:55 GMT
• Title: Subject Adaptive EEG-based Visual Recognition
• Authors: Pilhyeon Lee, Sunhee Hwang, Seogkyu Jeon, Hyeran Byun
• Abstract summary: This paper focuses on EEG-based visual recognition, aiming to predict the visual object class observed by a subject based on his/her EEG signals. One of the main challenges is the large variation between signals from different subjects. We introduce a novel problem setting, namely subject adaptive EEG-based visual recognition.
• Score: 14.466626957417864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper focuses on EEG-based visual recognition, aiming to predict the visual object class observed by a subject based on his/her EEG signals. One of the main challenges is the large variation between signals from different subjects. It limits recognition systems to work only for the subjects involved in model training, which is undesirable for real-world scenarios where new subjects are frequently added. This limitation can be alleviated by collecting a large amount of data for each new user, yet it is costly and sometimes infeasible. To make the task more practical, we introduce a novel problem setting, namely subject adaptive EEG-based visual recognition. In this setting, a bunch of pre-recorded data of existing users (source) is available, while only a little training data from a new user (target) are provided. At inference time, the model is evaluated solely on the signals from the target user. This setting is challenging, especially because training samples from source subjects may not be helpful when evaluating the model on the data from the target subject. To tackle the new problem, we design a simple yet effective baseline that minimizes the discrepancy between feature distributions from different subjects, which allows the model to extract subject-independent features. Consequently, our model can learn the common knowledge shared among subjects, thereby significantly improving the recognition performance for the target subject. In the experiments, we demonstrate the effectiveness of our method under various settings. Our code is available at https://github.com/DeepBCI/Deep-BCI/tree/master/1_Intelligent_BCI/Subject_Adaptive_EEG_based_Visual_ Recognition.

Related papers

• Decoupling the Class Label and the Target Concept in Machine Unlearning [81.69857244976123]
  Machine unlearning aims to adjust a trained model to approximate a retrained one that excludes a portion of training data. Previous studies showed that class-wise unlearning is successful in forgetting the knowledge of a target class. We propose a general framework, namely, TARget-aware Forgetting (TARF)
  arXiv  Detail & Related papers  (2024-06-12T14:53:30Z)
• Zero-shot Degree of Ill-posedness Estimation for Active Small Object Change Detection [8.977792536037956]
  In everyday indoor navigation, robots often needto detect non-distinctive small-change objects. Existing techniques rely on high-quality class-specific object priors to regularize a change detector model. In this study, we explore the concept of degree-of-ill-posedness (DoI) to improve both passive and activevision.
  arXiv  Detail & Related papers  (2024-05-10T01:56:39Z)
• Combating Missing Modalities in Egocentric Videos at Test Time [92.38662956154256]
  Real-world applications often face challenges with incomplete modalities due to privacy concerns, efficiency needs, or hardware issues. We propose a novel approach to address this issue at test time without requiring retraining. MiDl represents the first self-supervised, online solution for handling missing modalities exclusively at test time.
  arXiv  Detail & Related papers  (2024-04-23T16:01:33Z)
• Source-free Subject Adaptation for EEG-based Visual Recognition [21.02197151821699]
  This paper focuses on subject adaptation for EEG-based visual recognition. It aims at building a visual stimuli recognition system customized for the target subject whose EEG samples are limited.
  arXiv  Detail & Related papers  (2023-01-20T07:01:01Z)
• Frugal Reinforcement-based Active Learning [12.18340575383456]
  We propose a novel active learning approach for label-efficient training. The proposed method is iterative and aims at minimizing a constrained objective function that mixes diversity, representativity and uncertainty criteria. We also introduce a novel weighting mechanism based on reinforcement learning, which adaptively balances these criteria at each training iteration.
  arXiv  Detail & Related papers  (2022-12-09T14:17:45Z)
• Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition [94.56304526014875]
  We propose the first Source-Free Unsupervised Domain Adaptation (SFUDA) method for Facial Expression Recognition (FER) Our method exploits self-supervised pretraining to learn good feature representations from the target data. We validate the effectiveness of our method in four adaptation setups, proving that it consistently outperforms existing SFUDA methods when applied to FER.
  arXiv  Detail & Related papers  (2022-10-11T08:24:50Z)
• Inter-subject Contrastive Learning for Subject Adaptive EEG-based Visual Recognition [20.866855009168606]
  This paper tackles the problem of subject adaptive EEG-based visual recognition. Its goal is to accurately predict the categories of visual stimuli based on EEG signals with only a handful of samples for the target subject during training. We introduce a novel method that allows for learning subject-independent representation by increasing the similarity of features sharing the same class but coming from different subjects.
  arXiv  Detail & Related papers  (2022-02-07T01:34:57Z)
• Few-Cost Salient Object Detection with Adversarial-Paced Learning [95.0220555274653]
  This paper proposes to learn the effective salient object detection model based on the manual annotation on a few training images only. We name this task as the few-cost salient object detection and propose an adversarial-paced learning (APL)-based framework to facilitate the few-cost learning scenario.
  arXiv  Detail & Related papers  (2021-04-05T14:15:49Z)
• Unsupervised and self-adaptative techniques for cross-domain person re-identification [82.54691433502335]
  Person Re-Identification (ReID) across non-overlapping cameras is a challenging task. Unsupervised Domain Adaptation (UDA) is a promising alternative, as it performs feature-learning adaptation from a model trained on a source to a target domain without identity-label annotation. In this paper, we propose a novel UDA-based ReID method that takes advantage of triplets of samples created by a new offline strategy.
  arXiv  Detail & Related papers  (2021-03-21T23:58:39Z)
• Invariant Feature Learning for Sensor-based Human Activity Recognition [11.334750079923428]
  We present an invariant feature learning framework (IFLF) that extracts common information shared across subjects and devices. Experiments demonstrated that IFLF is effective in handling both subject and device diversion across popular open datasets and an in-house dataset.
  arXiv  Detail & Related papers  (2020-12-14T21:56:17Z)
• Omni-supervised Facial Expression Recognition via Distilled Data [120.11782405714234]
  We propose omni-supervised learning to exploit reliable samples in a large amount of unlabeled data for network training. We experimentally verify that the new dataset can significantly improve the ability of the learned FER model. To tackle this, we propose to apply a dataset distillation strategy to compress the created dataset into several informative class-wise images.
  arXiv  Detail & Related papers  (2020-05-18T09:36:51Z)

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.