Electroencephalogram (EEG) signal analysis, which is widely used for human-computer
interaction and neurological disease diagnosis, requires a large amount of labeled data for …

Brain–computer interfaces (BCIs) development is closely related to physics. In this paper, we
review the physical principles of BCIs, and underlying novel approaches for registration …

Objective: This paper presents a Transfer Learning approach for dealing with the statistical
variability of electroencephalographic (EEG) signals recorded on different sessions and/or …

Ear-electroencephalography (EEG) is a promising tool for practical brain-computer interface
(BCI) applications because it is more unobtrusive, comfortable, and mobile than a typical …

Objective. Extracting reliable information from electroencephalogram (EEG) is difficult
because the low signal-to-noise ratio and significant intersubject variability seriously hinder …

Objective. Covariance matrices of noisy multichannel electroencephalogram (EEG) time
series data provide essential information for the decoding of brain signals using machine …

With advances in technologies, robots can be employed in collaboration with human for
completing the shared objective (s). This paper proposes a novel time-variant human …

A brain-computer interface (BCI) establishes a direct communication pathway between the
brain and an external device. Electroencephalogram (EEG) is the most popular input signal …

Brain-computer interfaces (BCIs) are systems that translate brain activity patterns into
commands that can be executed by an artificial device. This enables the possibility of …

Many brain-computer interfaces make use of brain signals that are elicited in response to a
visual, auditory or tactile stimulus, so-called event-related potentials (ERPs). In visual ERP …