Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices
through the utilization of brain waves. It is worth noting that the application of BCI is not …

Since the launch of the first consumer grade EEG measuring sensorsNeuroSky Mindset'in
2007, the market has witnessed an introduction of at least one new product every year by …

Deep neural networks (DNNs) used for brain–computer interface (BCI) classification are
commonly expected to learn general features when trained across a variety of contexts, such …

The detection of seizures in epileptic patients via Electroencephalography (EEG) is an
essential key to medical treatment. With the advances in deep learning, many approaches …

Objective: Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow
control of several applications by decoding neurophysiological phenomena, which are …

Human nonverbal communication tools are very ambiguous and difficult to transfer to
machines or artificial intelligence (AI). If the AI understands the mental state behind a user's …

Electroencephalograph (EEG) has been widely applied for brain-computer interface (BCI)
which enables paralyzed people to directly communicate with and control external devices …

Identifying bio-signals based-sleep stages requires time-consuming and tedious labor of
skilled clinicians. Deep learning approaches have been introduced in order to challenge the …

Deep learning has achieved excellent performance in a wide range of domains, especially
in speech recognition and computer vision. Relatively less work has been done for …

Background: Brain-Computer Interface (BCI) is becoming more reliable, thanks to the
advantages of Artificial Intelligence (AI). Recently, hybrid Deep Learning (hDL), which …