Motor imagery (MI) brain-computer interface (BCI) paradigms have been extensively used in neurological rehabilitation. However, due to the required long calibration time and non-stationary nature of electroencephalogram (EEG) signals, it is ...

Transfer learning (TL) has demonstrated its efficacy in addressing the cross-subject domain adaptation challenges in affective brain-computer interfaces (aBCI). However, previous TL methods usually use a stationary distance, such as Euclidean ...

• MSMMTL uses Mahalanobis distance to assess correlations between subjects and effectively screen suitable source domains.
• MSMMTL utilizes the supervised information from the source domain to learn a more generalized distance ...

Recent researches on emotion recognition suggests that domain adaptation, a form of transfer learning, has the capability to solve the cross-subject problem in Affective brain-computer interface (aBCI) field. However, traditional ...

• Our algorithm is to select more useful sources and treat each individual as a source instead of combining them into a unified source.

Transfer learning uses the knowledge in source domains to improve the learning performance in the target domain, which is useful in electroencephalogram (EEG) based brain-computer interfaces (BCIs) with small training datasets. However,...

This article is devoted to the intelligent optimization issue by means of PEO-FOCNN, i.e., the fractional-order convolutional neural networks (FOCNNs) with population extremal optimization (PEO). The Caputo fractional-order gradient ...

• Transfer learning can reduce the sample calibration time.
• Target alignment make ...

In the brain computer interface (BCI) field, using sub-band common spatial pattern (SBCSP) and filter bank common spatial pattern (FBCSP) can improve the accuracy of classification by selection a ...

Electroencephalography (EEG) signal classification is a crucial part in motor imagery brain–computer interface (BCI) system. Traditional supervised learning methods have performed well pleasing in EEG classification. Unfortunately, the unlabeled ...

Low-to-high hierarchical convolutional features can significantly improve edge detection. This paper proposes a feature decoder-based algorithm that employs a Feature Decoder Network (FDN) to extract more information within limited Convolutional ...

3D action recognition has attracted much attention in machine learning fields in recent years, and recurrent neural networks (RNNs) have been widely used for 3D action recognition due to their efficiency in processing sequential data. However, in ...

Recent technological advances have enabled researchers to collect large amounts of electroencephalography (EEG) signals in labeled and unlabeled datasets. It is expensive and time consuming to collect labeled EEG data for use in brain-computer interface (...

In the past decades, regularized least squares classification (RLSC) is a commonly used supervised classification method in the machine learning filed because it can be easily resolved through the simple matrix analysis and achieve a close-form ...

The rapid development of the automotive industry has brought great convenience to our life, which also leads to a dramatic increase in the amount of traffic accidents. A large proportion of traffic accidents were caused by driving fatigue. EEG is ...

Classification of motor imagery (MI) electroencephalogram (EEG) plays a vital role in brain-computer interface (BCI) systems. Recent research has shown that nonlinear classification algorithms perform better than their linear counterparts, but most of ...

The finite-time stability analysis of discrete-time fuzzy Hopfield neural networks is studied in this paper. Firstly, the concept of finite-time stability is generalized to the fuzzy neural networks. And then by the Lyapunov approach and linear matrix ...

Motor imagery electroencephalography is widely used in the brain-computer interface systems. Due to inherent characteristics of electroencephalography signals, accurate and real-time multiclass classification is always challenging. In order to solve ...

Bit error or packet loss is unavoidable in error-prone wireless networks, so error resilience and concealment are becoming important and necessary techniques for image or video remote monitoring. A simple and applied error resilient coding scheme is ...

In this paper, a modeling method of fuzzy membership based on data domain description is proposed for image coding by fuzzy support vector regression. The original image is divided into some non-overlapped rectangular blocks and their transform domain ...