An Evidence-Based Combining Classifier for Brain Signal Analysis

• Published in: PloS one Vol. 9; no. 1; p. e84341
• Main Authors: Kheradpisheh, Saeed Reza, Nowzari-Dalini, Abbas, Ebrahimpour, Reza, Ganjtabesh, Mohammad
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 02.01.2014; Public Library of Science (PLoS)
• Subjects: Algorithms; Artificial Intelligence; Biology; Biomedical engineering; Brain; Brain - physiology; Brain research; Classification; Classifiers; Cognition & reasoning; Cognitive ability; Computer engineering; Computer Science; Data processing; Decision making; Decision theory; Electroencephalography; Engineering; Human-computer interface; Humans; Interfaces; Labels; Mathematics; Medical science; Medicine; Models, Theoretical; Nervous system; Neurosciences; Pattern recognition; Probability; Reproducibility of Results; Researchers; Signal analysis; Signal processing; Signal Processing, Computer-Assisted; Theory; Uncertainty; Iran
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0084341

Nowadays, brain signals are employed in various scientific and practical fields such as Medical Science, Cognitive Science, Neuroscience, and Brain Computer Interfaces. Hence, the need for robust signal analysis methods with adequate accuracy and generalizability is inevitable. The brain signal analysis is faced with complex challenges including small sample size, high dimensionality and noisy signals. Moreover, because of the non-stationarity of brain signals and the impacts of mental states on brain function, the brain signals are associated with an inherent uncertainty. In this paper, an evidence-based combining classifiers method is proposed for brain signal analysis. This method exploits the power of combining classifiers for solving complex problems and the ability of evidence theory to model as well as to reduce the existing uncertainty. The proposed method models the uncertainty in the labels of training samples in each feature space by assigning soft and crisp labels to them. Then, some classifiers are employed to approximate the belief function corresponding to each feature space. By combining the evidence raised from each classifier through the evidence theory, more confident decisions about testing samples can be made. The obtained results by the proposed method compared to some other evidence-based and fixed rule combining methods on artificial and real datasets exhibit the ability of the proposed method in dealing with complex and uncertain classification problems.