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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)...

Advances in deep learning have shown great promise towards the application of performing high-accuracy Electroencephalography (EEG) signal classification in a variety of tasks. However, many EEG-based datasets are often plagued by the issue of ...

• The framework optimally learns parameters that generalizes towards target subjects.
• We show how the framework is applied across different EEG classification paradigms.
• State-of-the-art accuracy is achieved while utilizing fewer ...

Insufficient data is a long-standing challenge for Brain–Computer Interface (BCI) to build a high-performance deep learning model. Though numerous research groups and institutes collect a multitude of EEG datasets for the same BCI task, sharing ...

• This work provided new insights on federated learning for the BCI.
• Enabled knowledge sharing across heterogeneous EEG datasets.
• Proposed a new paradigm to build a deep learning model for BCI.
• Designed a personalized FL EEG ...

Metacognitive monitoring, defined as the self-awareness and management of cognitive processes, influences creative design. Yet, there are few tools to enhance metacognitive monitoring through biofeedback. To address the gap, we present “Multi-...

• BCI-VR design tool developed to augment users’ awareness of affect levels.
• EEG classification results showed respectable validation accuracy for an ordinary design task.
• The study identifies a wide range of individual differences ...

Recently, brain–computer interfaces (BCIs) have attracted worldwide attention for their great potential in clinical and real-life applications. To implement a complete BCI system, one must set up several links to translate the brain ...

• This study developed a one-stop open-source BCI software, namely MetaBCI.
• MetaBCI is written in Python, and covers all links of the BCI chain.
• MetaBCI lowers the technical threshold for BCI beginners.
• MetaBCI saves time and ...

• Fractal dimension as a novel feature for subject-independent event classification.
• Assessing complexity in brain signals during motor and imagination tasks.
• High classification accuracy by using Fractal Dimension Features.
• ...

The brain-computer interface (BCI) technology acquires human brain electrical signals, which can be effectively and successfully used to control external devices, potentially supporting subjects suffering from motor ...

Brain–Computer Interface is tested as a successful method in improving human cognitive functions such as attention and memory. Attention plays a significant role in areas ranging from a person’s day-to-day life to educational domain and ...

Fatigue driving poses a significant threat to driver safety, leading to an increased risk of traffic accidents and potential harm to both life and property. Traditional fatigue detection methods, such as machine vision, are low effective due to factors ...

Transfer learning for motor imagery-based brain–computer interfaces (MI-BCIs) struggles with inter-subject variability, hindering its generalization to new users. This paper proposes an advanced implicit transfer learning framework, META-EEG, ...

• We propose a meta-learning-based zero-calibration EEG feature learning framework.
• We construct meta-tasks robust to unseen subjects in meta-training.
• We design intermittent freezing to learn class-relevant EEG features efficiently.

In recent years, brain-based technologies that capitalise on human abilities to facilitate human–system/robot interactions have been actively explored, especially in brain robotics. Brain–computer interfaces, as applications of this ...

Recently, more and more studies have begun to use deep learning to decode and classify EEG signals. The use of deep learning has led to an increase in the classification accuracy of motor imagery (MI), but the problem of taking a long time to ...

• A novel network structure based on the combination of CNN and Riemannian geometry.
• Domain adaptation is used to improve the performance of cross-session classification.
• A novel domain loss function based on Riemannian geometry.

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Although recent studies have proposed seizure detection algorithms with good sensitivity performance, there is a remained challenge that they were hard to achieve significantly short detection latency in real-time scenarios. In this manuscript, ...

• The first to introduce crossing period and probabilistic prediction.
• Multiscale STFT-based 3D-CNN model to capture accurately probabilities.
• Rectified weighting strategy and accumulative decision-making rule are proposed.
• Short ...

Upper limb amputation is a widespread problem worldwide, leading to massive loss of functionality for the victims. While a few solutions exist, these are often very expensive and involve expensive and dangerous surgical procedures. In this paper, ...

Quantification of event-related (de) synchronization (ERD/ERS) patterns is a challenging task in the field of motor imagery (MI)-based brain–computer interface (BCI). Accurately determining the optimal time and frequency band for localizing the ...

Security authentication involves the process of verifying a person's identity. Authentication technology has played a crucial role in data security for many years. However, existing typical biometric authentication technologies exhibit ...

In this paper we propose a new version of penalty-based aggregation functions, the Multi Cost Aggregation choosing functions (MCAs), in which the function to minimize is constructed using a convex combination of two relaxed versions of restricted ...

• We develop relaxed versions of Restricted Equivalence Functions.
• We aggregate information from different classifiers trained on different wave bands.
• The new aggregations learn from the input data to discriminate between classes.

Brain-computer interface (BCI) technology is a system that uses brain signals to assist in controlling devices in the outside world. Among the many methods of implementing BCI, one of the most representative method is event-related potential (ERP)-...

Motor imagery (MI)-based brain–computer interfaces are widely employed for improving the rehabilitation of paralyzed people and their quality of life. It has been well documented that brain activity patterns in the primary motor cortex and ...

• Depending on the location of paralysis, EEG patterns exerted during MI were compared.
• The FC structure-based graph analysis is investigated.
• The FC structure was different during MI with a paralyzed upper limb.
• FC along with ...

Time-consuming data labeling in brain-computer interfaces (BCIs) raises many problems such as mental fatigue and is one key factor that hinders the real-world adoption of motor imagery (MI)-based BCIs. An alternative approach is to ...

• An online semi-supervised learning method is proposed to deal with the problem of limited EEG data during calibration.
• The proposed method does not rely on stored data to retrain the model online, which decreases memory usage burden.

The performance of electroencephalography (EEG) classifiers in a brain–computer interface (BCI) depends heavily on the quality and consistency of training data. Therefore, facilitating collaboration between two independent systems, namely humans ...