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• Development of a one-source domain transfer learning method, termed one-source domain adversarial neural networks (ODANN), which utilizes domain adversarial neural networks to assimilate common features from source domains, enhancing the ...

A Brain-computer interface (BCI) system establishes a novel communication channel between the human brain and a computer. Most event related potential-based BCI applications make use of decoding models, which requires training. This training ...

• complex task-related component analysis(cTRCA).
• steady-state somatosensory evoked potential.
• phase information.
• complex signal.
• false triggering.

Motor Imagery (MI) recognition is one of the most critical decoding problems in brain- computer interface field. Combined with the steady-state somatosensory evoked potential (MI-SSSEP), this new paradigm can achieve ...

Decoding brain activity through electroencephalogram (EEG) signals is still challenging, due to the low signal-to-noise ratio and spatial resolution of EEG signals. To address these limitations, we propose an advanced end-to-end network, called the ...

One of the major obstacles to the widespread adoption of Virtual Reality (VR) is Cybersickness. It is a sense of physical discomfort akin to motion sickness experienced by the users either during or subsequent to VR utilization. Typically, it is ...

Event-related potentials (ERPs) represent the electroencephalographic responses to specific stimuli and are crucial for analyzing and understanding the processing of conscious activities within the human brain. Their classification is of ...

Feature adaptation plays crucial roles in the calibration process of motor imagery brain computer interfaces (MI-BCIs). Due to the temporal varying and spatial coupling characteristics in MI-electroencephalograph (EEG), recently proposed cross-...

Approximately 65 million individuals experience epilepsy globally. Surgery or medication cannot cure more than 30% of epilepsy patients.However, through therapeutic intervention, anticipating a seizure can help us avoid it. According to previous ...

Even though multiple studies have examined the decoding of speech from brain activity through non-invasive technologies in recent years, the task still presents a challenge as decoding quality is still insufficient for practical applications. An ...

Electroencephalography (EEG) motor imagery (MI) signals have recently attracted much attention because of their potential to communicate with the surrounding environment in a specific way without the need for muscular and physical movement. ...

In recent decades, Brain-Computer Interface (BCI) technology has made significant advancements, making it increasingly popular across various fields. This has sparked the need for developing prototype systems that integrate BCI with innovative ...

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 ...

With the development of brain-computer interface (BCI), electroencephalogram (EEG) is considered to be one of the best physiological signals to detect the fatigue state of drivers due to its advantages of extremely high time resolution and low ...

A major challenge in electroencephalogram-based brain-computer interfaces (EEG-based BCIs) is to manage individual differences in EEG. The paper presents FasterEA, which reduces time by randomly selecting EEG trials to compute the reference matrix ...

This review systematically explores the application of transformer-based models in EEG signal processing and brain-computer interface (BCI) development, with a distinct focus on ensuring methodological rigour and adhering to empirical validations ...

• Transformer-based EEG signal encoders show promise for improving BCI performance.
• BCI research lags behind in adopting transformer models despite the NLP boom.
• Optimizing transformer architecture could lead to efficient end-to-end ...

A brain–computer interface (BCI) enables direct communication between the human brain and external devices. Electroencephalography (EEG) based BCIs are currently the most popular for able-bodied users. To increase user-friendliness, usually a ...

Here, we present a novel dual-frequency steady-state visual evoked potential (SSVEP) brain-computer interface (BCI) system with a unique human–computer interaction (HCI) feature that utilizes distinct visual resource disparities between the two ...

Accurate recognition of human intent is crucial for effective human–computer speech interaction. Numerous intent understanding studies were based on speech-to-text transcription, which often overlook the influence of paralinguistic cues (such as ...

Electroencephalography (EEG)-based brain-computer interfaces (BCIs) have a wide range of applications in affect recognition. The usage of irrelevant information channels when decoding brain activity from different regions can negatively impact ...

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In today's world, a large number of people suffer from motor impairment-related challenges. Rehabilitation is the main method used to overcome these difficulties. The goal of the paper is to develop a deep learning-based electroencephalogram (EEG)...

Because of significant individual differences of brain signals, ensuring accuracy and information transfer rate (ITR) remains a challenge for SSVEP recognition. To address this issue, this study combines innovatively sliding time window method ...