BCI

EEG-Transformer: Self-attention from Transformer Architecture for Decoding EEG of Imagined Speech

Transformers are groundbreaking architectures that have changed a flow of deep learning, and many high-performance models are developing based on transformer architectures. Transformers...

Deep Transfer Learning for Error Decoding from Non-Invasive EEG

We recorded high-density EEG in a flanker task experiment (31 subjects) and an online BCI control paradigm (4 subjects). On these datasets, we evaluated the use of transfer learning for error...

A generic framework for adaptive EEG-based BCI training and operation

There are numerous possibilities and motivations for an adaptive BCI, which may not be easy to clarify and organize for a newcomer to the field. To our knowledge, there has not been any work done...

The signature of robot action success in EEG signals of a human observer: Decoding and visualization using deep convolutional neural networks

The importance of robotic assistive devices grows in our work and everyday life. Cooperative scenarios involving both robots and humans require safe human-robot interaction. One important aspect...

T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs

Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the human brain and a computer. Due to individual differences and...

Hierarchical internal representation of spectral features in deep convolutional networks trained for EEG decoding

Recently, there is increasing interest and research on the interpretability of machine learning models, for example how they transform and internally represent EEG signals in Brain-Computer...

Would Motor-Imagery based BCI user training benefit from more women experimenters?

Mental Imagery based Brain-Computer Interfaces (MI-BCI) are a mean to control digital technologies by performing MI tasks alone. Throughout MI-BCI use, human supervision (e.g., experimenter or...

Performance Comparison of Contention- and Schedule-based MAC Protocols in Urban Parking Sensor Networks

Network traffic model is a critical problem for urban applications, mainly because of its diversity and node density. As wireless sensor network is highly concerned with the development of smart...

Decoding High-level Imagined Speech using Attention-based Deep Neural Networks

Brain-computer interface (BCI) is the technology that enables the communication between humans and devices by reflecting status and intentions of humans. When conducting imagined speech, the users...

Interpretable Convolutional Neural Networks for Subject-Independent Motor Imagery Classification

Deep learning frameworks have become increasingly popular in brain computer interface (BCI) study thanks to their outstanding performance. However, in terms of the classification model alone, they...

Recurrent Neural Networks for P300-based BCI

P300-based spellers are one of the main methods for EEG-based brain-computer interface, and the detection of the P300 target event with high accuracy is an important prerequisite. The rapid serial...

Target-centered Subject Transfer Framework for EEG Data Augmentation

Data augmentation approaches are widely explored for the enhancement of decoding electroencephalogram signals. In subject-independent brain-computer interface system, domain adaption and...

Decoding Visual Imagery from EEG Signals using Visual Perception Guided Network Training Method

An electroencephalogram is an effective approach that provides a bidirectional pathway between user and computer in a non-invasive way. In this study, we adopted the visual perception data for...

Evaluation of a congruent auditory feedback for Motor Imagery BCI

Designing a feedback that helps participants to achieve higher performances is an important concern in brain-computer interface (BCI) research. In a pilot study, we demonstrate how a congruent...

Imagined Speech and Visual Imagery as Intuitive Paradigms for Brain-Computer Interfaces

Brain-computer interfaces (BCIs) have shown promise in enabling communication for individuals with motor impairments. Recent advancements like brain-to-speech technology aim to reconstruct speech...

Decoding Continual Muscle Movements Related to Complex Hand Grasping from EEG Signals

Brain-computer interface (BCI) is a practical pathway to interpret users' intentions by decoding motor execution (ME) or motor imagery (MI) from electroencephalogram (EEG) signals. However,...

Decoding Multi-class Motor-related Intentions with User-optimized and Robust BCI System Based on Multimodal Dataset

A brain-computer interface (BCI) based on electroencephalography (EEG) can be useful for rehabilitation and the control of external devices. Five grasping tasks were decoded for motor execution...

A Factorization Approach for Motor Imagery Classification

Brain-computer interface uses brain signals to communicate with external devices without actual control. Many studies have been conducted to classify motor imagery based on machine learning....