Brain-computer interface (BCI) is a rapidly evolving technology that has the potential to
widely influence research, clinical and recreational use. Non-invasive BCI approaches are …

Automatic deep-learning models used for sleep scoring in children with obstructive sleep
apnea (OSA) are perceived as black boxes, limiting their implementation in clinical settings …

Background: Swift and accurate blood smear analyses are crucial for diagnosing leukemia
and other hematological malignancies. However, manual leukocyte count and …

In this paper, we proposed a novel hybrid decoding model that combines the superiority of
CNNs and multi-head self-attention mechanisms, called HCANN, to finely characterizing …

This short review examines recent advancements in neurotechnologies within the context of
managing unilateral spatial neglect (USN), a common condition following stroke. Despite the …

Abstract Background and Objectives: Code-modulated visual evoked potentials (c-VEPs)
have marked a milestone in the scientific literature due to their ability to achieve reliable …

Wireless electroencephalography (EEG) devices allow for recordings in contexts outside the
laboratory. However, many details must be considered for their use. In this research, using a …

Individuals afflicted by neurodegenerative conditions such as amyotrophic lateral sclerosis
may eventually reach a point where they lose the ability to communicate with the outside …

Code-modulated visual evoked potentials (c-VEPs) are an innovative control signal utilized
in brain-computer interfaces (BCIs) with promising performance. Prior studies on steady …

Background and objective: Neurofeedback (NF) is a paradigm that allows users to self-
modulate patterns of brain activity. It is implemented with a closed-loop brain–computer …