AIMC Topic: Brain-Computer Interfaces
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'Write' but not 'spell' Chinese characters with a BCI-controlled robot.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2020
Visual brain-computer interface (BCI) systems have made tremendous process in recent years. It has been demonstrated to perform well in spelling words. However, different from spelling English words in one-dimension sequences, Chinese characters are ...
Four-Way Classification of EEG Responses To Virtual Robot Navigation.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2020
Studies have shown the possibility of using brain signals that are automatically generated while observing a navigation task as feedback for semi-autonomous control of a robot. This allows the robot to learn quasi-optimal routes to intended targets. ...
Decoding movement direction from cortical microelectrode recordings using an LSTM-based neural network.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2020
Brain-machine interfaces (BMIs) allow individuals to communicate with computers using neural signals, and Kalman Filter (KF) are prevailingly used to decode movement directions from these neural signals. In this paper, we implemented a multi-layer lo...
Decoding spectro-temporal representation for motor imagery recognition using ECoG-based brain-computer interfaces.
Journal of integrative neuroscience
Jun 30, 2020
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
Jun 1, 2020
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
Mar 1, 2020
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
Mar 1, 2020
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
Jan 1, 2020
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
Jan 1, 2020
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
Aug 25, 2019
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...
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