Brain Computer Interfaces, a Review
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TLDR
The state-of-the-art of BCIs are reviewed, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface.Abstract:
A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or 'locked in' by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.read more
Citations
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Journal ArticleDOI
EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces
Vernon J. Lawhern,Amelia J. Solon,Nicholas R. Waytowich,Nicholas R. Waytowich,Stephen M. Gordon,Chou P. Hung,Chou P. Hung,Brent J. Lance +7 more
TL;DR: This work introduces EEGNet, a compact convolutional neural network for EEG-based BCIs, and introduces the use of depthwise and separable convolutions to construct an EEG-specific model which encapsulates well-known EEG feature extraction concepts for BCI.
Journal ArticleDOI
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
Vernon J. Lawhern,Amelia J. Solon,Nicholas R. Waytowich,Stephen M. Gordon,Chou P. Hung,Brent J. Lance +5 more
TL;DR: In this paper, a compact convolutional network for EEG-based brain computer interfaces (BCI) is proposed, which can learn a wide variety of interpretable features over a range of BCI tasks.
Journal ArticleDOI
The grand challenges of Science Robotics
Guang-Zhong Yang,James G. Bellingham,Pierre E. Dupont,Peer Fischer,Peer Fischer,Luciano Floridi,Robert J. Full,Neil Jacobstein,Neil Jacobstein,Vijay Kumar,Marcia McNutt,Robert Merrifield,Bradley J. Nelson,Brian Scassellati,Mariarosaria Taddeo,Mariarosaria Taddeo,Russell H. Taylor,Manuela Veloso,Zhong Lin Wang,Robert J. Wood,Robert J. Wood +20 more
TL;DR: These 10 grand challenges may have major breakthroughs, research, and/or socioeconomic impacts in the next 5 to 10 years and represent underpinning technologies that have a wider impact on all application areas of robotics.
Journal ArticleDOI
fNIRS-based brain-computer interfaces: a review
Noman Naseer,Keum-Shik Hong +1 more
TL;DR: In this paper, the most common brain areas for fNIRS-based BCI are the primary motor cortex and prefrontal cortex, and the motor imagery tasks were preferred to motor execution tasks since possible proprioceptive feedback could be avoided.
Journal ArticleDOI
A novel deep learning approach for classification of EEG motor imagery signals
Yousef Rezaei Tabar,Ugur Halici +1 more
TL;DR: The results show that deep learning methods provide better classification performance compared to other state of art approaches and can be applied successfully to BCI systems where the amount of data is large due to daily recording.
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