Adaptive deep feature representation learning for cross-subject EEG decoding.

Background: The collection of substantial amounts of electroencephalogram (EEG) data is typically time-consuming and labor-intensive, which adversely impacts the development of decoding models with strong generalizability, particularly when the available data is limited. Utilizing sufficient EEG data from other subjects to aid in modeling the target subject presents a potential solution, commonly referred to as domain adaptation. Most current domain adaptation techniques for EEG decoding primarily focus on learning shared feature representations through domain alignment strategies.

A Trustworthy Curriculum Learning Guided Multi-Target Domain Adaptation Network for Autism Spectrum Disorder Classification.

Domain adaptation has demonstrated success in classification of multi-center autism spectrum disorder (ASD). However, current domain adaptation methods primarily focus on classifying data in a single target domain with the assistance of one or multiple source domains, lacking the capability to address the clinical scenario of identifying ASD in multiple target domains. In response to this limitation, we propose a Trustworthy Curriculum Learning Guided Multi-Target Domain Adaptation (TCL-MTDA) network for identifying ASD in multiple target domains.

Domain-Generalized EEG Classification With Category-Oriented Feature Decorrelation and Cross-View Consistency Learning.

Generalizing the electroencephalogram (EEG) decoding methods to unseen subjects is an important research direction for realizing practical application of brain-computer interfaces (BCIs). Since distribution shifts across subjects, the performance of most current deep neural networks for decoding EEG signals degrades when dealing with unseen subjects. Domain generalization (DG) aims to tackle this issue by learning invariant representations across subjects.

Adaptive Multimodel Knowledge Transfer Matrix Machine for EEG Classification.

The emerging matrix learning methods have achieved promising performances in electroencephalogram (EEG) classification by exploiting the structural information between the columns or rows of feature matrices. Due to the intersubject variability of EEG data, these methods generally need to collect a large amount of labeled individual EEG data, which would cause fatigue and inconvenience to the subjects. Insufficient subject-specific EEG data will weaken the generalization capability of the matrix learning methods in neural pattern decoding.

A Novel Transfer Support Matrix Machine for Motor Imagery-Based Brain Computer Interface.

In recent years, emerging matrix learning methods have shown promising performance in motor imagery (MI)-based brain-computer interfaces (BCIs). Nonetheless, the electroencephalography (EEG) pattern variations among different subjects necessitates collecting a large amount of labeled individual data for model training, which prolongs the calibration session. From the perspective of transfer learning, the model knowledge inherent in reference subjects incorporating few target EEG data have the potential to solve the above issue.

Deep stacked support matrix machine based representation learning for motor imagery EEG classification.

Background And Objective: Electroencephalograph (EEG) classification is an important technology that can establish a mapping relationship between EEG features and cognitive tasks. Emerging matrix classifiers have been successfully applied to motor imagery (MI) EEG classification, but they belong to shallow classifiers, making powerful stacked generalization principle not exploited for automatically learning deep EEG features. To learn the high-level representation and abstraction, we proposed a novel deep stacked support matrix machine (DSSMM) to improve the performance of existing shallow matrix classifiers in EEG classification.

A Novel Negative-Transfer-Resistant Fuzzy Clustering Model With a Shared Cross-Domain Transfer Latent Space and its Application to Brain CT Image Segmentation.

Traditional clustering algorithms for medical image segmentation can only achieve satisfactory clustering performance under relatively ideal conditions, in which there is adequate data from the same distribution, and the data is rarely disturbed by noise or outliers. However, a sufficient amount of medical images with representative manual labels are often not available, because medical images are frequently acquired with different scanners (or different scan protocols) or polluted by various noises. Transfer learning improves learning in the target domain by leveraging knowledge from related domains.

Walking Imagery Evaluation in Brain Computer Interfaces via a Multi-View Multi-Level Deep Polynomial Network.

Brain-computer interfaces based on motor imagery (MI) have been widely used to support the rehabilitation of motor functions of the upper limbs rather than lower limbs. This is probably because it is more difficult to detect the brain activities of lower limb MI. In order to reliably detect the brain activities of lower limbs to restore or improve the walking ability of the disabled, we propose a new paradigm of walking imagery (WI) in a virtual environment (VE), in order to elicit the reliable brain activities and achieve a significant training effect.