Open AccessJournal Article
EEG Classification in Brain Computer Interface (BCI): A Pragmatic Appraisal
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TLDR
This work provides a concise but all encompassing review of methods that have been adopted in the recent time for development of an EEG classification in BCI.Abstract:
Brain computer interface (BCI) is one of the technologies growing at an exponential rate with its applications extended to medical and non-medical fields. EEG is widely used in BCI for detection and analysis of abnormalities of the brain. EEG is characterized by inherently high temporal resolution and precision, low spatial resolution and specificity plus contains artifacts and redundant or noise information both from the subject and equipment interferences. Thus, feature extraction is a critical issue in translation algorithm development for BCI. Above all, BCI still faces a lot challenges that results in performance variation across and even within subjects. Thus, this work provides a concise but all encompassing review of methods that have been adopted in the recent time for development of an EEG classification in BCI.read more
Citations
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Electroencephalogram channel selection based on pearson correlation coefficient for motor imagery-brain-computer interface
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High Performance Multi-class Motor Imagery EEG Classification.
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Proceedings ArticleDOI
Comparison of Classification Algorithms for Motor Imagery Brain-Computer Interface
TL;DR: It was shown that neural networks in combination with Common Spatial Pattern (CSP) filtering could be considered as the most perspective.
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TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.
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Independent component analysis: algorithms and applications
Aapo Hyvärinen,Erkki Oja +1 more
TL;DR: The basic theory and applications of ICA are presented, and the goal is to find a linear representation of non-Gaussian data so that the components are statistically independent, or as independent as possible.
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High confidence visual recognition of persons by a test of statistical independence
TL;DR: A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence, which implies a theoretical "cross-over" error rate of one in 131000 when a decision criterion is adopted that would equalize the false accept and false reject error rates.
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Electroencephalography: Basic Principles, Clinical Applications, and Related Fields
TL;DR: The main thrust of Electroencephalography is to preserve the sound basis of classic EEG recording and reading and, on the other hand, to present the newest developments for future EEG/neurophysiology research, especially in view of the highest brain functions as mentioned in this paper.