An EEG signal smoothing algorithm using upscale and downscale representation.

Effective smoothing of electroencephalogram (EEG) signals while maintaining the original signal's features is important in EEG signal analysis and brain-computer interface. This paper proposes a novel EEG signal-smoothing algorithm and its potential application in cognitive conflict (CC) processing.Instead of being processed in the time domain, the input signal is visualized in increasing line width, the representation frame of which is converted into a binary image.

A Novel 3D Paradigm for Target Expansion of Augmented Reality SSVEP.

Steady-State Visual Evoked Potentials (SSVEP) have proven to be practical in Brain-Computer Interfaces (BCI), particularly when integrated with augmented reality (AR) for real-world application. However, unlike conventional computer screen-based SSVEP (CS-SSVEP), which benefits from stable experimental environments, AR-based SSVEP (AR-SSVEP) systems are susceptible to the interference of real-world environment and device instability. Particularly, the performance of AR-SSVEP significantly declines as the target frequency increases.

The effect of an EEG neurofeedback intervention for corneal neuropathic pain: A single-case experimental design with multiple baselines.

Corneal neuropathic pain is a complex condition, rarely responsive to current treatments. This trial investigated the potential effect of a novel home-based self-directed EEG neurofeedback intervention on corneal neuropathic pain using a multiple-baseline single-case experimental design. Four Participants completed a predetermined baseline of 7, 10, 14, and 17 days, randomly assigned to each participant, followed by 20 intervention sessions over four weeks.

Autonomous Clustering by Fast Find of Mass and Distance Peaks.

Clustering is an essential analytical tool across a wide range of scientific fields, including biology, chemistry, astronomy, and pattern recognition. This paper introduces a novel clustering algorithm, called Torque Clustering, as a competitive alternative to existing methods, based on the intuitive principle that a cluster should merge with its nearest neighbor with a higher mass, unless both clusters have relatively large masses and the distance between them is also substantial. By identifying peaks in mass and distance, the algorithm effectively detects and removes incorrect mergers.

Cascaded Thinning in Upscale and Downscale Representation for EEG Signal Processing.

Smoothing filters are widely used in EEG signal processing for noise removal while preserving signals' features. Inspired by our recent work on Upscale and Downscale Representation (UDR), this paper proposes a cascade arrangement of some effective image-processing techniques for signal filtering in the image domain. The UDR concept is to visualize EEG signals at an appropriate line width and convert it to a binary image.

A home-based self-directed EEG neurofeedback intervention for people with chronic neuropathic pain following spinal cord injury (the StoPain Trial): description of the intervention.

Study Design: Randomised controlled trial.

Objectives: The objective is to describe an electroencephalography (EEG) neurofeedback intervention that will be provided in a randomised controlled trial for people with neuropathic pain following spinal cord injury (SCI): the StoPain Trial. In this trial, participants in the treatment group will implement an EEG neurofeedback system as an analgesic intervention at home, while participants in the control group will continue with the treatments available to them in the community.

Multimodal fusion for anticipating human decision performance.

Anticipating human decisions while performing complex tasks remains a formidable challenge. This study proposes a multimodal machine-learning approach that leverages image features and electroencephalography (EEG) data to predict human response correctness in a demanding visual searching task. Notably, we extract a novel set of image features pertaining to object relationships using the Segment Anything Model (SAM), which enhances prediction accuracy compared to traditional features.

Understanding the effects of stress on the P300 response during naturalistic simulation of heights exposure.

Stress is a prevalent bodily response universally experienced and significantly affects a person's mental and cognitive state. The P300 response is a commonly observed brain behaviour that provides insight into a person's cognitive state. Previous works have documented the effects of stress on the P300 behaviour; however, only a few have explored the performance in a mobile and naturalistic experimental setup.

EEG Information Transfer Changes in Different Daily Fatigue Levels During Drowsy Driving.

A significant issue for traffic safety has been drowsy driving for decades. A number of studies have investigated the effects of acute fatigue on spectral power; and recent research has revealed that drowsy driving is associated with a variety of brain connections in a specific cortico-cortical pathway. In spite of this, it is still unclear how different brain regions are connected in drowsy driving at different levels of daily fatigue.

Distinction of the object recognition and object identification in the brain-computer interfaces applications.

Object recognition is a complex cognitive process in which information is integrated and processed by various brain regions. Previous studies have shown that both the visual and temporal cortices are active during object recognition and identification. However, although object recognition and object identification are similar, these processes are considered distinct functions in the brain.

Classification of inattentional blindness using brain dynamics of ERPs.

Situational awareness (SA) is vital for understanding our surroundings. Multiple variables, including inattentive blindness (IB), contribute to the deterioration of SA, which may have detrimental effects on individuals' cognitive performance. IB occurs due to attentional limitations, ignoring critical information and resulting in a loss of SA and a decline in general performance, particularly in complicated situations requiring substantial cognitive resources.

Early Detection of Human Decision-Making in Concealed Object Visual Searching Tasks: An EEG-BiLSTM Study.

Detecting concealed objects presents a significant challenge for human and artificial intelligent systems. Detecting concealed objects task necessitates a high level of human attention and cognitive effort to complete the task successfully. Thus, in this study, we use concealed objects as stimuli for our decision-making experimental paradigms to quantify participants' decision-making performance.

Ventral and Dorsal Stream EEG Channels: Key Features for EEG-Based Object Recognition and Identification.

Object recognition and object identification are multifaceted cognitive operations that require various brain regions to synthesize and process information. Prior research has evidenced the activity of both visual and temporal cortices during these tasks. Notwithstanding their similarities, object recognition and identification are recognized as separate brain functions.

A Connectivity-Aware Graph Neural Network for Real-Time Drowsiness Classification.

Drowsy driving is one of the primary causes of driving fatalities. Electroencephalography (EEG), a method for detecting drowsiness directly from brain activity, has been widely used for detecting driver drowsiness in real-time. Recent studies have revealed the great potential of using brain connectivity graphs constructed based on EEG data for drowsy state predictions.

MGRW-Transformer: Multigranularity Random Walk Transformer Model for Interpretable Learning.

Deep-learning models have been widely used in image recognition tasks due to their strong feature-learning ability. However, most of the current deep-learning models are "black box" systems that lack a semantic explanation of how they reached their conclusions. This makes it difficult to apply these methods to complex medical image recognition tasks.

An investigation into the effectiveness of using acoustic touch to assist people who are blind.

Wearable smart glasses are an emerging technology gaining popularity in the assistive technologies industry. Smart glasses aids typically leverage computer vision and other sensory information to translate the wearer's surrounding into computer-synthesized speech. In this work, we explored the potential of a new technique known as "acoustic touch" to provide a wearable spatial audio solution for assisting people who are blind in finding objects.

Higher Order Fractal Belief Rényi Divergence With Its Applications in Pattern Classification.

Information can be quantified and expressed by uncertainty, and improving the decision level of uncertain information is vital in modeling and processing uncertain information. Dempster-Shafer evidence theory can model and process uncertain information effectively. However, the Dempster combination rule may provide counter-intuitive results when dealing with highly conflicting information, leading to a decline in decision level.

Design and verification of a wearable wireless 64-channel high-resolution EEG acquisition system with wi-fi transmission.

Brain-computer interfaces (BCIs) allow communication between the brain and the external world. This type of technology has been extensively studied. However, BCI instruments with high signal quality are typically heavy and large.

FedTP: Federated Learning by Transformer Personalization.

Federated learning is an emerging learning paradigm where multiple clients collaboratively train a machine learning model in a privacy-preserving manner. Personalized federated learning extends this paradigm to overcome heterogeneity across clients by learning personalized models. Recently, there have been some initial attempts to apply transformers to federated learning.

EEG-Based Brain-Computer Interfaces are Vulnerable to Backdoor Attacks.

Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to deeper understanding of the brain and wide adoption of sophisticated machine learning approaches for decoding the EEG signals. However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks. This paper proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which makes adversarial attacks much easier to implement.

Speech2EEG: Leveraging Pretrained Speech Model for EEG Signal Recognition.

Identifying meaningful brain activities is critical in brain-computer interface (BCI) applications. Recently, an increasing number of neural network approaches have been proposed to recognize EEG signals. However, these approaches depend heavily on using complex network structures to improve the performance of EEG recognition and suffer from the deficit of training data.

Robust Fuzzy Neural Network With an Adaptive Inference Engine.

Fuzzy neural networks (FNNs) have been very successful at handling uncertainty in data using fuzzy mappings and if-then rules. However, they suffer from generalization and dimensionality issues. Although deep neural networks (DNNs) represent a step toward processing high-dimensional data, their capacity to address data uncertainty is limited.

Error-related potential-based shared autonomy via deep recurrent reinforcement learning.

Error-related potential (ErrP)-based brain-computer interfaces (BCIs) have received a considerable amount of attention in the human-robot interaction community. In contrast to traditional BCI, which requires continuous and explicit commands from an operator, ErrP-based BCI leverages the ErrP, which is evoked when an operator observes unexpected behaviours from the robot counterpart. This paper proposes a novel shared autonomy model for ErrP-based human-robot interaction.