AI Medical Compendium Topic:
Brain-Computer Interfaces

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Decoding spectro-temporal representation for motor imagery recognition using ECoG-based brain-computer interfaces.

Journal of integrative neuroscience
One of the challenges in brain-computer interface systems is obtaining motor imagery recognition from brain activities. Brain-signal decoding robustness and system performance improvement during the motor imagery process are two of the essential issu...

The future of upper extremity rehabilitation robotics: research and practice.

Muscle & nerve
The loss of upper limb motor function can have a devastating effect on people's lives. To restore upper limb control and functionality, researchers and clinicians have developed interfaces to interact directly with the human body's motor system. In t...

Motor imagery classification using geodesic filtering common spatial pattern and filter-bank feature weighted support vector machine.

The Review of scientific instruments
In recent years, Brain Computer Interface (BCI) based on motor imagery has been widely used in the fields of medicine, active safe systems for automobiles, entertainment, and so on. Motor imagery relevant electroencephalogram (EEG) signals are weak, ...

A TrAdaBoost Method for Detecting Multiple Subjects' N200 and P300 Potentials Based on Cross-Validation and an Adaptive Threshold.

International journal of neural systems
Traditional training methods need to collect a large amount of data for every subject to train a subject-specific classifier, which causes subjects fatigue and training burden. This study proposes a novel training method, TrAdaBoost based on cross-va...

A natural evolution optimization based deep learning algorithm for neurological disorder classification.

Bio-medical materials and engineering
BACKGROUND: A neurological disorder is one of the significant problems of the nervous system that affects the essential functions of the human brain and spinal cord. Monitoring brain activity through electroencephalography (EEG) has become an importa...

General principles of machine learning for brain-computer interfacing.

Handbook of clinical neurology
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 controlling devices such as a prosthetic arm or exoskeleton, a wheelchair,...

[A TrAdaBoost-based method for detecting multiple subjects' P300 potentials].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Individual differences of P300 potentials lead to that a large amount of training data must be collected to construct pattern recognition models in P300-based brain-computer interface system, which may cause subjects' fatigue and degrade the system p...

A Hybrid Physiological Approach of Emotional Reaction Detection Using Combined FCM and SVM Classifier.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Users' emotional reaction capturing is one of the primary issues for brain computer interface applications. Despite the intuitive feedback provided by the qualitative methods, emotional reactions are expected to be detected and classified quantitativ...

A Convolutional Neural Network for Enhancing the Detection of SSVEP in the Presence of Competing Stimuli.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Stimulus proximity has been shown to have an influence on the classification performance of a steady-state visual evoked potential based brain-computer interface (SSVEP-BCI). Multiple visual stimuli placed close to each other compete for neural repre...