A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals

By Ali Bashashati, Mehrdad Fatourechi, Rabab Kreidieh Ward, Gary E. Birch. Journal of Neural Engineering, 2007

948 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/b2ec92cbf4a04637181c8598adfffbbbeb648ddf

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Principles of neural ensemble physiology underlying the operation of brain–machine interfaces (2009, background): “Many recent review articles have covered BMI method”
Face Spoof Detection With Image Distortion Analysis (2015, methodology): “Following the success of SVM [36] in signal processing [37], pattern recognition and classification applications [38], [39], we choose to use SVM via the LibSVM Library [40].”
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, background): “To extract the type of information needed to control such a vast list of actuators, a multitude of mathematical and computational approaches have been proposed as potential real-time decoders of voluntary motor activity generated by the brains of animals and human subjects (49, 449, 490).”
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications (2010, background): “Signal processing [126] and classification algorithms [127] for EEG have been developed in the context of building Brain Computer Interfaces (BCIs), and researchers are constantly seeking ways for developing new approaches to recognizing affective states from EEG and other physiological signals.”
Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review (2020, background): “Amyotrophic lateral sclerosis (ALS), cerebral palsy, brain stem stroke, multiple sclerosis, cerebral palsy, and spinal cord injury are the main causes of locked-in syndrome (Holz et al., 2013).”
Prior Knowledge Improves Decoding of Finger Flexion from Electrocorticographic Signals (2011, methodology): “Other efforts have formulated the decoding problem as that of feature translation rather than classiﬁcation.”
A Survey of Stimulation Methods Used in SSVEP-Based BCIs (2010, positive): “Existing review papers focus on general VEPbased BCIs [7] and signal processing algorithms applied to BCIs [2].”
A comprehensive review of EEG-based brain–computer interface paradigms (2019, positive): “Many different decoding methods, signal processing algorithms [231], and classification algorithms [30] have been recently investigated.”
A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update (2018, background): “[16, 136, 155]), the two most common types of features used to represent EEG signals are frequency band power features and time point features.”
EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces (2016, methodology): “For example, the [16, 4] × [2, 16] model configuration, while performing the best on P300, was slightly worse in SMR and among the worst models for MRCP.”
Progress in Brain Computer Interface: Challenges and Opportunities (2019, background): “The design of classifiers for BCI comprises two issues [81], [82], [83].”
Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation (2016, methodology): “PCA is known as a linear transformation that is generally used to reduce the dimension of a dataset, keeping characteristics that contribute the most to the variance as well as maintaining lower-order principal components, while leaving out the higher-order components [137].”