SS-MSDA: Streamlined Sample-level Multi-source Domain Adaptation for EEG Emotion Recognition.

Affective EEG-based Brain-Computer Interface (BCI) offers extensive prospects. Yet, it grapples with notable challenges in consistently achieving accurate emotion recognition among new subjects. Mitigating this matter, Multi-Source Domain Adaptation (MSDA) has been advanced.

Bi-Stream Adaptation Network for Motor Imagery Decoding.

Neural activities in distinct brain regions variably contribute to the formation of motor imagery (MI). Utilizing the hidden contextual information can thereby enhance network performance by having a comprehensive understanding of MI. Besides, due to the non-stationarity of EEG, the global and local distributions of cross-session EEG from an individual vary in applications.

Narrowband-Enhanced Method for Improving Frequency Recognition in SSVEP-BCIs.

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCI) provide a non-invasive and effective means for communication and control, which fundamentally rely on the feature of frequency information. However, filter banks in conventional spatial filter classification methods do not effectively utilize narrowband information. This study proposed a narrowband-enhanced filter bank canonical correlation analysis (NE-FBCCA) to integrate narrowband signal processing with a broadband filter bank analysis.

Enhancing Detection of SSVEP-based BCIs Using Adjacent Frequencies Fusion Method.

Brain-computer interfaces (BCIs) have emerged as transformative technologies, enabling direct communication between the human brain and external devices. Steady-state visual evoked potentials (SSVEP) have gained particular attention due to their potential in BCIs. Current decoding algorithms do not take advantage of the correlation coefficients of adjacent frequencies.

A Novel SSVEP Modulation Method Utilizing VR-Based Binocular Vision.

This paper presents a novel method for modulating steady-state visual evoked potentials (SSVEP) based on binocular vision in virtual reality (VR). The method involves displaying monocular frequencies in the left and right view of VR to encode nine binocular targets using only two frequencies. We constructed a VR-BCI system and validated the effectiveness of this binocular-encoded paradigm through the task-related component analysis (TRCA) algorithm, which is a supervised approach based on individual templates.

Enhancing Word-Level Imagined Speech BCI Through Heterogeneous Transfer Learning.

In this study, we proposed a novel heterogeneous transfer learning approach named Focused Speech Feature Transfer Learning (FSFTL), aimed at enhancing the performance of electroencephalogram (EEG)-based word-level Imagined Speech (IS) Brain-Computer Interface (BCI). In IS BCI, the classification accuracy for imagining specific words is relatively low due to the inherent complexity in high-level feature variations. However, the binary classification accuracy for IS/rest is significantly higher.

EEG Emotion Recognition Based on 3D-CTransNet.

Emotion recognition is of great significance for brain-computer interface and emotion computing, and EEG plays a key role in this field. However, the current design of brain computer interface deep learning model is faced with algorithmic or structural constraints, and it is difficult to recognize the complex features in EEG signals with long-term dynamic changes. To solve this issue, a hybrid CNN-Transformer structure using 3D data input is proposed and named 3D-CTransNet in this paper, which solves the problem of performance degradation of the traditional CNN-LSTM hybrid structure in the recognition of long sequence signals.

Random Subset Multi-domain Feature Extraction for Attentional State Recognition.

Existing attentional state recognition methods achieve good results by utilizing frequency domain features, but spatial information has not been fully considered. In this paper, a random subset multi-domain feature extraction method is proposed. To exploit the spatial information, the training data is first divided into several non-overlapping subsets, and independent Riemannian manifolds are constructed within each subset.

Amplitude Modulation Depth Coding Method for SSVEP-based Brain-computer Interfaces.

In steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), the limited availability of frequency resources inherently constrains the scale of the instruction set, presenting a substantial challenge for efficient communication. As the number of stimuli increases, the comfort level of the stimulus interface also becomes increasingly demanding due to the expanded flickering area. To address these issues, we proposed a novel amplitude modulation depth coding (AMDC) method that employs Amplitude Shift Keying (ASK) technique to modulate the luminance level of stimuli dynamically.

MP: A steady-state visual evoked potential dataset based on multiple paradigms.

In the field of steady-state visual evoked potential (SSVEP), stimulus paradigms are regularly arranged or mimic the style of a keyboard with the same size. However, stimulation paradigms have important effects on the performance of SSVEP systems, which correlate with the electroencephalogram (EEG) signal amplitude and recognition accuracy. This paper provides MP dataset that was acquired using a 12-target BCI speller.

Group-member selection for RSVP-based collaborative brain-computer interfaces.

Objective: The brain-computer interface (BCI) systems based on rapid serial visual presentation (RSVP) have been widely utilized for the detection of target and non-target images. Collaborative brain-computer interface (cBCI) effectively fuses electroencephalogram (EEG) data from multiple users to overcome the limitations of low single-user performance in single-trial event-related potential (ERP) detection in RSVP-based BCI systems. In a multi-user cBCI system, a superior group mode may lead to better collaborative performance and lower system cost.

Riemannian Locality Preserving Method for Transfer Learning With Applications on Brain-Computer Interface.

Brain-computer interfaces (BCIs) have been widely focused and extensively studied in recent years for their huge prospect of medical rehabilitation and commercial applications. Transfer learning exploits the information in the source domain and applies in another different but related domain (target domain), and is therefore introduced into the BCIs to figure out the inter-subject variances of electroencephalography (EEG) signals. In this article, a novel transfer learning method is proposed to preserve the Riemannian locality of data structure in both the source and target domains and simultaneously realize the joint distribution adaptation of both domains to enhance the effectiveness of transfer learning.

A comparative study of stereo-dependent SSVEP targets and their impact on VR-BCI performance.

Steady-state visual evoked potential brain-computer interfaces (SSVEP-BCI) have attracted significant attention due to their ease of deployment and high performance in terms of information transfer rate (ITR) and accuracy, making them a promising candidate for integration with consumer electronics devices. However, as SSVEP characteristics are directly associated with visual stimulus attributes, the influence of stereoscopic vision on SSVEP as a critical visual attribute has yet to be fully explored. Meanwhile, the promising combination of virtual reality (VR) devices and BCI applications is hampered by the significant disparity between VR environments and traditional 2D displays.

A Subject-Specific Attention Index Based on the Weighted Spectral Power.

As an essential cognitive function, attention has been widely studied and various indices based on EEG have been proposed for its convenience and easy availability for real-time attention monitoring. Although existing indices based on spectral power of empirical frequency bands are able to describe the attentional state in some way, the reliability still needs to be improved. This paper proposed a subject-specific attention index based on the weighted spectral power.

Average Time Consumption per Character: A Practical Performance Metric for Generic Synchronous BCI Spellers.

Objective: The information transfer rate (ITR) is widely accepted as a performance metric for generic brain-computer interface (BCI) spellers, while it is noticeable that the communication speed given by ITR is actually an upper bound which however can never be reached in real systems. A new performance metric is therefore needed.

Methods: In this paper, a new metric named average time consumption per character (ATCPC) is proposed.

A Novel Hybrid Brain-Computer Interface Combining the Illusion-Induced VEP and SSVEP.

Traditional single-modality brain-computer interface (BCI) systems are limited by their reliance on a single characteristic of brain signals. To address this issue, incorporating multiple features from EEG signals can provide robust information to enhance BCI performance. In this study, we designed and implemented a novel hybrid paradigm that combined illusion-induced visual evoked potential (IVEP) and steady-state visual evoked potential (SSVEP) with the aim of leveraging their features simultaneously to improve system efficiency.

Attentional State Classification Using Amplitude and Phase Feature Extraction Method Based on Filter Bank and Riemannian Manifold.

As a significant aspect of cognition, attention has been extensively studied and numerous measurements have been developed based on brain signal processing. Although existing attentional state classification methods have achieved good accuracy by extracting a variety of handcrafted features, spatial features have not been fully explored. This paper proposes an attentional state classification method based on Riemannian manifold to utilize spatial information.

Joint User Association and Deployment Optimization for Energy-Efficient Heterogeneous UAV-Enabled MEC Networks.

Unmanned aerial vehicles (UAVs) providing additional on-demand communication and computing services have become a promising technology. However, the limited energy supply of UAVs, which constrains their service duration, has emerged as an obstacle in UAV-enabled networks. In this context, a novel task offloading framework is proposed in UAV-enabled mobile edge computing (MEC) networks.

A Sliced Parabolic Equation Method to Characterize Maritime Radio Propagation.

For maritime broadband communications, atmospheric ducts can enable beyond line-of-sight communications or cause severe interference. Due to the strong spatial-temporal variability of atmospheric conditions in near-shore areas, atmospheric ducts have inherent spatial heterogeneity and suddenness. This paper aims to evaluate the effect of horizontally inhomogeneous ducts on maritime radio propagation through theoretical analysis and measurement validation.

MDTL: A Novel and Model-Agnostic Transfer Learning Strategy for Cross-Subject Motor Imagery BCI.

In recent years, deep neural network-based transfer learning (TL) has shown outstanding performance in EEG-based motor imagery (MI) brain-computer interface (BCI). However, due to the long preparation for pre-trained models and the arbitrariness of source domain selection, using deep transfer learning on different datasets and models is still challenging. In this paper, we proposed a multi-direction transfer learning (MDTL) strategy for cross-subject MI EEG-based BCI.

Remote Interference Discrimination Testbed Employing AI Ensemble Algorithms for 6G TDD Networks.

The Internet-of-Things (IoT) massive access is a significant scenario for sixth-generation (6G) communications. However, low-power IoT devices easily suffer from remote interference caused by the atmospheric duct under the 6G time-division duplex (TDD) mode. It causes distant downlink wireless signals to propagate beyond the designed protection distance and interfere with local uplink signals, leading to a large outage probability.

Driving Mode Selection through SSVEP-Based BCI and Energy Consumption Analysis.

Background: The brain-computer interface (BCI) is a highly cross-discipline technology and its successful application in various domains has received increasing attention. However, the BCI-enabled automobile industry is has been comparatively less investigated. In particular, there are currently no studies focusing on brain-controlled driving mode selection.

Prevalence of Azole-Resistant is Highly Associated with Azole Fungicide Residues in the Fields.

Triazole resistance in is a growing public health concern. In addition to its emergence in the therapy of invasive aspergillosis by triazole medicines, it has been frequently detected in agricultural fields all over the world. Here, we explore the potential link between residues of azole fungicides with similar chemical structure to triazole medicines in soil and the emergence of resistant (RAF) through 855 500 km monitoring survey in Eastern China covering 6 provinces.

Determination and distribution of pesticides and antibiotics in agricultural soils from northern China.

Different types of soil samples from a typical farmland in northern China were collected and evaluated for the presence of the pesticides and antibiotics. 47 pesticides were extracted with a quick, easy, cheap, effective, rugged, and safe (QuEChERS) preparation method and cleanup with 50 mg C, while 10 antibiotics were extracted with methanol/EDTA-McIlvaine buffer solution (v/v = 1/1), then both of them were analyzed with high performance liquid chromatography-tandem mass spectrometer (HPLC-MS/MS). Total concentrations of the 47 pesticides in the soil samples ranged from not detectable (ND) to 3.

Dynamic modeling of famoxadone and oxathiapiprolin residue on cucumber and Chinese cabbage based on tomato and lettuce archetypes.

We analyzed the uptake and distribution of two pesticides (famoxadone and oxathiapiprolin) in herbaceous vegetables (cucumber and tomato) and leafy vegetables (Chinese cabbage and lettuce) to test the viability of applying existing archetypes in the dynamic plant uptake model dynamiCROP to modeling pesticide residue in other crops. Using field data and modeling, we showed that tomato was an unsuitable match for cucumber (R of 0.5325-0.