AIMC Topic: Brain-Computer Interfaces
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A Channel-Projection Mixed-Scale Convolutional Neural Network for Motor Imagery EEG Decoding.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
May 8, 2019
Motor imagery electroencephalography (EEG) decoding is an essential part of brain-computer interfaces (BCIs) which help motor-disabled patients to communicate with the outside world by external devices. Recently, deep learning algorithms using decomp...
A Convolutional Neural Network for the Detection of Asynchronous Steady State Motion Visual Evoked Potential.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
May 7, 2019
A key issue in brain-computer interface (BCI) is the detection of intentional control (IC) states and non-intentional control (NC) states in an asynchronous manner. Furthermore, for steady-state visual evoked potential (SSVEP) BCI systems, multiple s...
Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach.
IEEE transactions on bio-medical engineering
Apr 29, 2019
OBJECTIVE: This paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain-computer interfaces (BCIs): how to cope with individual differences so that better learning performance can be obtained for a new subject, ...
Multiclass Support Matrix Machines by Maximizing the Inter-Class Margin for Single Trial EEG Classification.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Apr 25, 2019
Accurate classification of Electroencephalogram (EEG) signals plays an important role in diagnoses of different type of mental activities. One of the most important challenges, associated with classification of EEG signals is how to design an efficie...
Making Sense of Spatio-Temporal Preserving Representations for EEG-Based Human Intention Recognition.
IEEE transactions on cybernetics
Apr 24, 2019
Brain-computer interface (BCI) is a system empowering humans to communicate with or control the outside world with exclusively brain intentions. Electroencephalography (EEG)-based BCI is one of the promising solutions due to its convenient and portab...
EIQ: EEG based IQ test using wavelet packet transform and hierarchical extreme learning machine.
Journal of neuroscience methods
Apr 22, 2019
BACKGROUND: The use of electroencephalography has been perpetually incrementing and has numerous applications such as clinical and psychiatric studies, social interactions, brain computer interface etc. Intelligence has baffled us for centuries, and ...
Visual P300 Mind-Speller Brain-Computer Interfaces: A Walk Through the Recent Developments With Special Focus on Classification Algorithms.
Clinical EEG and neuroscience
Apr 18, 2019
Brain-computer interfaces are sophisticated signal processing systems, which directly operate on neuronal signals to identify specific human intents. These systems can be applied to overcome certain disabilities or to enhance the natural capabilities...
Decoding Movements from Cortical Ensemble Activity Using a Long Short-Term Memory Recurrent Network.
Neural computation
Apr 12, 2019
Although many real-time neural decoding algorithms have been proposed for brain-machine interface (BMI) applications over the years, an optimal, consensual approach remains elusive. Recent advances in deep learning algorithms provide new opportunitie...
On the Vulnerability of CNN Classifiers in EEG-Based BCIs.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Apr 2, 2019
Deep learning has been successfully used in numerous applications because of its outstanding performance and the ability to avoid manual feature engineering. One such application is electroencephalogram (EEG)-based brain-computer interface (BCI), whe...
Common spatial pattern and wavelet decomposition for motor imagery EEG- fTCD brain-computer interface.
Journal of neuroscience methods
Apr 1, 2019
BACKGROUND: Recently, hybrid brain-computer interfaces (BCIs) combining more than one modality have been investigated with the aim of boosting the performance of the existing single-modal BCIs in terms of accuracy and information transfer rate (ITR)....
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