A5033623992- EEG and Brain-Computer Interfaces
- Neuroscience and Neural Engineering
- Neural dynamics and brain function
- Advanced Memory and Neural Computing
- Blind Source Separation Techniques
- Image Processing Techniques and Applications
- Functional Brain Connectivity Studies
- Neural Networks and Applications
Iran University of Science and Technology
2022-2024
Speller brain-computer interface (BCI) systems can help neuromuscular disorders patients write their thoughts by using the electroencephalogram (EEG) signals just focusing on speller tasks. For practical speller-based BCI systems, P300 event-related brain potential is measured EEG signal. In this paper, we design a robust machine-learning algorithm for target detection. The novel spatial-temporal linear feature learning (STLFL) proposed to extract high-level features. STLFL method modified...
A P300-based speller BCI with large target symbols was designed to improve the detection accuracy and information transfer rate (ITR) as well overcome adjacency, crowding, fatigue problems. The paradigm consists of a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times 7$ </tex-math></inline-formula> matrix which uses only 8 flashing characters for selection. Another critical issue in design is feature...
A new approach is introduced to address the subject dependency problem in P300-based brain-computer interfaces (BCI) by using transfer learning. The occurrence of P300, an event-related potential, primarily associated with changes natural neuron activity and elicited response infrequent stimuli, which can be monitored non-invasively through electroencephalogram. However, implementing BCI real-time requires many training samples time-consuming calibration, making it challenging use practical...
Introduction Event-related potentials (ERPs), such as P300, are widely utilized for non-invasive monitoring of brain activity in brain-computer interfaces (BCIs) via electroencephalogram (EEG). However, the non-stationary nature EEG signals and different data distributions among subjects create significant challenges implementing real-time P300-based BCIs. This requires time-consuming calibration a large number training samples. Methods To address these challenges, this study proposes...
Electroencephalogram (EEG) based brain-computer interface speller is a communication rehabilitation tool to help patients suffering from motor disorders. A hybrid EEG signal on steady-state visual evoke potential (SSVEP) and P300 signals is, although more efficient have robust application, however, there are some challenges including low signal-to-noise ratio, information transfer rate, classification accuracy in smaller number of trials. To overcome these issues, this study proposes two...
Brain-computer interface (BCI) systems have been developed to assist individuals with neuromuscular disorders communicate their surroundings using brain signals. One attractive branch of BCI is steady-state visual evoked potential (SSVEP), which has acceptable speed and accuracy non-invasive. However, SSVEP-based EEG signals suffer from eye-fatigue problems, resulting in artifacts that affect the system. Thus, researchers are still working improve systems. This paper proposes robust...
P300 is an event-related potential determined by the changes in natural neurons activity, which occurs mainly as a response to infrequent stimuli. Considering that positive can be monitored non-invasive methods such electroencephalogram, and 'oddball' paradigm elicits deliberately this response, used brain-computer interfaces (BCI). P300-based BCI applications suffer from subject dependency problem, one crucial issue real-time implementation, requiring time-consuming calibration large number...