Feature Selection Applying Statistical and Neurofuzzy Methods to EEG-Based BCI.

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Kernel-Based Relevance Analysis with Enhanced Interpretability for Detection of Brain Activity Patterns.On Feature Selection and Rule Extraction for High Dimensional Data: A Case of Diffuse Large B-Cell Lymphomas Microarrays Classification

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Feature Selection Applying Statistical and Neurofuzzy Methods to EEG-Based BCI.

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Feature Selection Applying Statistical and Neurofuzzy Methods to EEG-Based BCI.

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Feature Selection Applying Statistical and Neurofuzzy Methods to EEG-Based BCI.

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Feature Selection Applying Statistical and Neurofuzzy Methods to EEG-Based BCI.

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P1476

Feature Selection Applying Statistical and Neurofuzzy Methods to EEG-Based BCI

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P2093

Juan-Antonio Martinez-Leon

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Julio Ibarrola

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Comparison of linear, nonlinear, and feature selection methods for EEG signal classification.

High-performance neuroprosthetic control by an individual with tetraplegia.

Brain-machine interfaces: past, present and future.

Brain-computer interfaces for communication and control.

A review of classification algorithms for EEG-based brain-computer interfaces.

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

An asynchronous P300 BCI with SSVEP-based control state detection.

Improving motor imagery classification with a new BCI design using neuro-fuzzy S-dFasArt.

Subject-independent mental state classification in single trials.

Parallel man-machine training in development of EEG-based cursor control.

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781207

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scholarly article

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10.1155/2015/781207

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2015

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Jose-Manuel Cano-Izquierdo

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2015-04-21T00:00:00Z

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276170546

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25977685

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Amar

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4419264

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Feature Selection Applying Statistical and Neurofuzzy Methods to EEG-Based BCI.

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P2860

P2860

Feature Selection Applying Statistical and Neurofuzzy Methods to EEG-Based BCI.

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type

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P1433

P1476

P2093

P2860

P304

P31

P356

P478

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P577

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P698

P921

P932

P356

P1433

P1476

P2093

P2860

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P356

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P50

P577

P5875

P698

P921

P932