AIMC Topic: Brain-Computer Interfaces

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A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain-machine interface systems.

Journal of neural engineering Apr 1, 2018
OBJECTIVE: Considering the importance and the near-future development of noninvasive brain-machine interface (BMI) systems, this paper presents a comprehensive theoretical-experimental survey on the classification and evolutionary methods for BMI-bas...
Humans Animals Support Vector Machine Electroencephalography Brain-Computer Interfaces Algorithms Databases, Factual Brain
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The mind-reading devices that can free paralysed muscles.

Nature Mar 8, 2018
Electrodes, Implanted Paralysis Animals Neural Prostheses Male Mice Middle Aged Exoskeleton Device Robotics Muscles Stroke Humans Epilepsy, Temporal Lobe Touch Adult Brain-Computer Interfaces Movement Spinal Cord Injuries
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A Hybrid Network for ERP Detection and Analysis Based on Restricted Boltzmann Machine.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Mar 1, 2018
Detecting and Please provide the correct one analyzing the event-related potential (ERP) remains an important problem in neuroscience. Due to the low signal-to-noise ratio and complex spatio-temporal patterns of ERP signals, conventional methods usua...
Signal-To-Noise Ratio Evoked Potentials Event-Related Potentials, P300 Signal Processing, Computer-Assisted Prosthesis Design Brain-Computer Interfaces Algorithms Electroencephalography Humans Neural Networks, Computer
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Robust Support Matrix Machine for Single Trial EEG Classification.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Mar 1, 2018
Electroencephalogram (EEG) signals are of complex structure and can be naturally represented as matrices. Classification is one of the most important steps for EEG signal processing. Newly developed classifiers can handle these matrix-form data by ad...
Humans Electroencephalography Signal Processing, Computer-Assisted Artifacts Brain-Computer Interfaces Software Algorithms Support Vector Machine Signal-To-Noise Ratio
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As above, so below? Towards understanding inverse models in BCI.

Journal of neural engineering Feb 1, 2018
OBJECTIVE: In brain-computer interfaces (BCI), measurements of the user's brain activity are classified into commands for the computer. With EEG-based BCIs, the origins of the classified phenomena are often considered to be spatially localized in the...
Machine Learning Humans Algorithms Brain-Computer Interfaces Electroencephalography Brain Signal Processing, Computer-Assisted
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Inferring imagined speech using EEG signals: a new approach using Riemannian manifold features.

Journal of neural engineering Feb 1, 2018
OBJECTIVE: In this paper, we investigate the suitability of imagined speech for brain-computer interface (BCI) applications.
Electroencephalography Brain-Computer Interfaces Imagination Male Speech Support Vector Machine Female Young Adult Adult Humans
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Training set extension for SVM ensemble in P300-speller with familiar face paradigm.

Technology and health care : official journal of the European Society for Engineering and Medicine Jan 1, 2018
BACKGROUND: P300-spellers are brain-computer interface (BCI)-based character input systems. Support vector machine (SVM) ensembles are trained with large-scale training sets and used as classifiers in these systems. However, the required large-scale ...
Female Young Adult Support Vector Machine Humans Adult Electroencephalography Brain-Computer Interfaces Male Algorithms Time Factors
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Cryptographic decoding of movement.

Nature biomedical engineering Dec 1, 2017
Humans Movement Neural Prostheses Machine Learning Brain Brain-Computer Interfaces
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Augmenting intracortical brain-machine interface with neurally driven error detectors.

Journal of neural engineering Dec 1, 2017
OBJECTIVE: Making mistakes is inevitable, but identifying them allows us to correct or adapt our behavior to improve future performance. Current brain-machine interfaces (BMIs) make errors that need to be explicitly corrected by the user, thereby con...
Motor Cortex Macaca mulatta Electrodes, Implanted Brain-Computer Interfaces Acoustic Stimulation Photic Stimulation Support Vector Machine Action Potentials Principal Component Analysis Animals Male
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Four ethical priorities for neurotechnologies and AI.

Nature Nov 8, 2017
Artificial intelligence and brain-computer interfaces must respect and preserve people’s privacy, identity, agency and equality, say Rafael Yuste, Sara Goering and colleagues.
Codes of Ethics Biomedical Enhancement Individuality Bioengineering Neurosciences Parkinson Disease Female Humans Guidelines as Topic Privacy Electroencephalography Animals Neural Networks, Computer Brain-Computer Interfaces Alzheimer Disease Informed Consent Artificial Intelligence Male
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