User Adapted Motor-Imaginary Brain-Computer Interface by means of EEG Channel Selection Based on Estimation of Distributed Algorithms

Brain-Computer Interfaces (BCIs) have become a research field with interesting applications, and it can be inferred from published papers that different persons activate different parts of the brain to perform the same action. This paper presents a personalized interface design method, for electroen...

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Vydáno v:Mathematical Problems in Engineering Ročník 2016; číslo 2016; s. 436 - 447
Hlavní autoři: Martin, Jose I., Santana, Roberto, Muguerza, Javier, Arruti, A., Astigarraga, Aitzol, Sierra, B.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cairo, Egypt Hindawi Limiteds 01.01.2016
Hindawi Publishing Corporation
John Wiley & Sons, Inc
Témata:
Accuracy
Algorithms
Brain
Brain research
Channels
Classification
Communication
Competition
Computer science
Design engineering
Discriminant analysis
Electroencephalography
Genetic algorithms
Greedy algorithms
Human-computer interface
Mathematical problems
Methods
Optimization
Performance evaluation
Sensors
Spain
ISSN:1024-123X, 1563-5147
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Shrnutí:Brain-Computer Interfaces (BCIs) have become a research field with interesting applications, and it can be inferred from published papers that different persons activate different parts of the brain to perform the same action. This paper presents a personalized interface design method, for electroencephalogram- (EEG-) based BCIs, based on channel selection. We describe a novel two-step method in which firstly a computationally inexpensive greedy algorithm finds an adequate search range; and, then, an Estimation of Distribution Algorithm (EDA) is applied in the reduced range to obtain the optimal channel subset. The use of the EDA allows us to select the most interacting channels subset, removing the irrelevant and noisy ones, thus selecting the most discriminative subset of channels for each user improving accuracy. The method is tested on the IIIa dataset from the BCI competition III. Experimental results show that the resulting channel subset is consistent with motor-imaginary-related neurophysiological principles and, on the other hand, optimizes performance reducing the number of channels.
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ISSN:1024-123X
1563-5147
DOI:10.1155/2016/1435321