Brain–computer interfaces: a review

By Shirley Coyle, Tomás Ward, Charles Markham. 2003

1,175 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/85ab60140cab72e6655e7b012dc92ff8fc4fd906

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Transfer Learning for Brain–Computer Interfaces: A Euclidean Space Data Alignment Approach (2018, background): “Recently, the application scope of BCIs has been extended to able-bodied people [23], [34], and EEG becomes the most popular input signal because it is easy and safe to acquire, and has high temporal resolution.”
Brain computer interfacing: Applications and challenges (2015, background): “Table 1 [123] gives a summary for brain acquisition meth-ods along with their advantages and disadvantages.”
Subject-Independent Brain–Computer Interfaces Based on Deep Convolutional Neural Networks (2019, background): “Over the course of numerous BCI studies [16]–[21], growing attention has been dedicated to the analysis of electroencephalography (EEG) signals, especially by movement imagination, called motor imagery (MI) [1], [2], [8], [12], [14], [16], [22].”
A comprehensive review of EEG-based brain–computer interface paradigms (2019, methodology): “Since scalp EEG is categorized as low-cost and affordable technology among brain monitoring technologies, it has the potential to be commercialized for general public [3].”
A novel deep learning approach for classification of EEG motor imagery signals (2017, methodology): “Among different brain activity monitoring modalities, electroencephalography (EEG) technique provides an easy and inexpensive solution for BCI systems and is used in many non-invasive BCI studies [4].”
Machine Learning for Neural Decoding (2017, background): “However, for engineering applications such as BMIs [48, 49], the goal is to decode information (e.”
fNIRS-based brain-computer interfaces: a review (2015, methodology): “For more details on genetic algorithms, please see Pernkopf and O’Leary (2001) and Nicolas-Alonso and Gomez-Gil (2012).”
Review of Multimodal Data Acquisition Approaches for Brain–Computer Interfaces (2024, unknown): “One of the primary advantages of this setup is its capacity for real-time monitoring and data processing, which is essential for applications such as BCIs and neurological research [8].”