Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Complete Pipeline

• URL: http://arxiv.org/abs/2007.03746v3
• Date: Fri, 22 Jan 2021 20:37:14 GMT
• Title: Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Complete Pipeline
• Authors: Dongrui Wu and Xue Jiang and Ruimin Peng and Wanzeng Kong and Jian Huang and Zhigang Zeng
• Abstract summary: Transfer learning (TL) has been widely used in motor imagery (MI) based brain-computer interfaces (BCIs) to reduce the calibration effort for a new subject. This paper proposes that TL could be considered in all three components (spatial filtering, feature engineering, and classification) of MI-based BCIs.
• Score: 54.73337667795997
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transfer learning (TL) has been widely used in motor imagery (MI) based brain-computer interfaces (BCIs) to reduce the calibration effort for a new subject, and demonstrated promising performance. While a closed-loop MI-based BCI system, after electroencephalogram (EEG) signal acquisition and temporal filtering, includes spatial filtering, feature engineering, and classification blocks before sending out the control signal to an external device, previous approaches only considered TL in one or two such components. This paper proposes that TL could be considered in all three components (spatial filtering, feature engineering, and classification) of MI-based BCIs. Furthermore, it is also very important to specifically add a data alignment component before spatial filtering to make the data from different subjects more consistent, and hence to facilitate subsequential TL. Offline calibration experiments on two MI datasets verified our proposal. Especially, integrating data alignment and sophisticated TL approaches can significantly improve the classification performance, and hence greatly reduces the calibration effort.

Related papers

• A Temporal-Spectral Fusion Transformer with Subject-Specific Adapter for Enhancing RSVP-BCI Decoding [15.000487099591776]
  RSVP-based Brain-Computer Interface (BCI) is an efficient technology for target retrieval using electroencephalography (EEG) signals. Traditional decoding methods rely on a substantial amount of training data from new test subjects. We propose a subject-specific adapter to rapidly transfer the knowledge of the model trained on data from existing subjects to decode data from new subjects.
  arXiv  Detail & Related papers  (2024-01-12T03:18:51Z)
• Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Framework [70.83511997272457]
  We propose a semi-federated learning (SemiFL) paradigm to leverage both the base station (BS) and devices for a hybrid implementation of centralized learning (CL) and FL. We propose a two-stage algorithm to solve this intractable problem, in which we provide the closed-form solutions to the beamformers.
  arXiv  Detail & Related papers  (2023-10-04T03:32:39Z)
• Phase Synchrony Component Self-Organization in Brain Computer Interface [3.2116198597240846]
  Phase synchrony information plays a crucial role in analyzing functional brain connectivity and identifying brain activities. We propose the concept of phase synchrony component self-organization, which enables the adaptive learning of data-dependent spatial filters. Based on this concept, the first deep learning end-to-end network is developed, which directly extracts phase synchrony-based features from raw EEG signals.
  arXiv  Detail & Related papers  (2023-09-21T09:42:16Z)
• Salient Object Detection in Optical Remote Sensing Images Driven by Transformer [69.22039680783124]
  We propose a novel Global Extraction Local Exploration Network (GeleNet) for Optical Remote Sensing Images (ORSI-SOD) Specifically, GeleNet first adopts a transformer backbone to generate four-level feature embeddings with global long-range dependencies. Extensive experiments on three public datasets demonstrate that the proposed GeleNet outperforms relevant state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-09-15T07:14:43Z)
• Weakly Supervised Change Detection Using Guided Anisotropic Difusion [97.43170678509478]
  We propose original ideas that help us to leverage such datasets in the context of change detection. First, we propose the guided anisotropic diffusion (GAD) algorithm, which improves semantic segmentation results. We then show its potential in two weakly-supervised learning strategies tailored for change detection.
  arXiv  Detail & Related papers  (2021-12-31T10:03:47Z)
• Confidence-Aware Subject-to-Subject Transfer Learning for Brain-Computer Interface [3.2550305883611244]
  The inter/intra-subject variability of electroencephalography (EEG) makes the practical use of the brain-computer interface (BCI) difficult. We propose a BCI framework using only high-confidence subjects for TL training. In our framework, a deep neural network selects useful subjects for the TL process and excludes noisy subjects, using a co-teaching algorithm based on the small-loss trick.
  arXiv  Detail & Related papers  (2021-12-15T15:23:23Z)
• A SPA-based Manifold Learning Framework for Motor Imagery EEG Data Classification [2.4727719996518487]
  This paper proposes a manifold learning framework to classify two types of EEG data from motor imagery (MI) tasks. For feature extraction, it is implemented by Common Spatial Pattern (CSP) from the preprocessed EEG signals. In the neighborhoods of the features for classification, the local approximation to the support of the data is obtained, and then the features are assigned to the classes with the closest support.
  arXiv  Detail & Related papers  (2021-07-30T06:18:05Z)
• End-to-End Object Detection with Fully Convolutional Network [71.56728221604158]
  We introduce a Prediction-aware One-To-One (POTO) label assignment for classification to enable end-to-end detection. A simple 3D Max Filtering (3DMF) is proposed to utilize the multi-scale features and improve the discriminability of convolutions in the local region. Our end-to-end framework achieves competitive performance against many state-of-the-art detectors with NMS on COCO and CrowdHuman datasets.
  arXiv  Detail & Related papers  (2020-12-07T09:14:55Z)
• Multilinear Compressive Learning with Prior Knowledge [106.12874293597754]
  Multilinear Compressive Learning (MCL) framework combines Multilinear Compressive Sensing and Machine Learning into an end-to-end system. Key idea behind MCL is the assumption of the existence of a tensor subspace which can capture the essential features from the signal for the downstream learning task. In this paper, we propose a novel solution to address both of the aforementioned requirements, i.e., How to find those tensor subspaces in which the signals of interest are highly separable?
  arXiv  Detail & Related papers  (2020-02-17T19:06:05Z)

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.