A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update
Objective. Most current electroencephalography (EEG)-based brain–computer interfaces
(BCIs) are based on machine learning algorithms. There is a large diversity of classifier …
(BCIs) are based on machine learning algorithms. There is a large diversity of classifier …
Physical principles of brain–computer interfaces and their applications for rehabilitation, robotics and control of human brain states
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 …
review the physical principles of BCIs, and underlying novel approaches for registration …
Efficient deep neural networks for classification of Alzheimer's disease and mild cognitive impairment from scalp EEG recordings
The early diagnosis of subjects with mild cognitive impairment (MCI) is an effective
appliance of prognosis of Alzheimer's disease (AD). Electroencephalogram (EEG) has many …
appliance of prognosis of Alzheimer's disease (AD). Electroencephalogram (EEG) has many …
A review of classification algorithms for EEG-based brain–computer interfaces
In this paper we review classification algorithms used to design brain–computer interface
(BCI) systems based on electroencephalography (EEG). We briefly present the commonly …
(BCI) systems based on electroencephalography (EEG). We briefly present the commonly …
Enhance decoding of pre-movement EEG patterns for brain–computer interfaces
Objective. In recent years, brain–computer interface (BCI) systems based on
electroencephalography (EEG) have developed rapidly. However, the decoding of voluntary …
electroencephalography (EEG) have developed rapidly. However, the decoding of voluntary …
A tutorial on EEG signal-processing techniques for mental-state recognition in brain–computer interfaces
F Lotte - Guide to brain-computer music interfacing, 2014 - Springer
This chapter presents an introductory overview and a tutorial of signal-processing
techniques that can be used to recognize mental states from electroencephalographic (EEG) …
techniques that can be used to recognize mental states from electroencephalographic (EEG) …
Noninvasive brain-computer interfaces based on sensorimotor rhythms
Brain-computer interfaces (BCIs) have been explored in the field of neuroengineering to
investigate how the brain can use these systems to control external devices. We review the …
investigate how the brain can use these systems to control external devices. We review the …
Detection of self-paced reaching movement intention from EEG signals
E Lew, R Chavarriaga, S Silvoni… - Frontiers in …, 2012 - frontiersin.org
Future neuroprosthetic devices, in particular upper limb, will require decoding and executing
not only the user's intended movement type, but also when the user intends to execute the …
not only the user's intended movement type, but also when the user intends to execute the …
Electroencephalography (EEG)-based brain-computer interfaces
Brain-Computer Interfaces (BCI) are systems that can translate the brain activity patterns of a
user into messages or commands for an interactive application. The brain activity which is …
user into messages or commands for an interactive application. The brain activity which is …
The smartphone brain scanner: a portable real-time neuroimaging system
Combining low-cost wireless EEG sensors with smartphones offers novel opportunities for
mobile brain imaging in an everyday context. Here we present the technical details and …
mobile brain imaging in an everyday context. Here we present the technical details and …