Incremental Training of Neural Network for Motor Tasks Recognition Based on Brain-Computer Interface

A. D. Orjuela-Cañón, Andrés Jutinico, Nayid Triana

Research output: Chapter in Book/ReportChapter (peer-reviewed)peer-review

3 Scopus citations


Brain-computer interfaces (BCI) based on motor imagery tasks (MI) have been established as a promising solution for restoring communication and control of people with motor disabilities. Physically impaired people may perform different motor imagery tasks which could be recorded in a non-invasive way using electroencephalography (EEG). However, the success of the MI-BCI systems depends on the reliable processing of the EEG signals and the adequate selection of the features used to characterize the brain activity signals for effective classification of MI activity and translation into corresponding actions. The multilayer perceptron (MLP) has been the neural network most widely used for classification in BCI technologies. The fact that MLP is a universal approximator makes this classifier sensitive to overtraining, especially with such noisy, non-linear, and non-stationary data as EEG. Traditional training techniques, as well as more recent ones, have mainly focused on the machine-learning aspects of BCI training. As a novel alternative for BCI training, this work proposes an incremental training process. Preliminary results with a non-disabled individual demonstrate that the proposed method has been able to improve the BCI training performance in comparison with the cross-validation technique. Best results showed that the incremental training proposal allowed an increase of the performance by at least 10% in terms of classification compared to a conventional cross-validation technique, which indicates the potential application for classification models of BCI’s systems.
Original languageEnglish (US)
Title of host publicationIncremental Training of Neural Network for Motor Tasks Recognition Based on Brain-Computer Interface
EditorsIngela Nyström , Yanio Hernández Heredia, Vladimir Milián Núñez
Number of pages10
ISBN (Electronic)9783030339043
ISBN (Print)9783030339036
StatePublished - Oct 31 2019
Event24th Iberoamerican Congress, CIARP 2019 - Havana, Cuba
Duration: Oct 28 2019Oct 31 2019

Publication series

NameLecture Notes In Computer Sciencies


Conference24th Iberoamerican Congress, CIARP 2019
Internet address


Dive into the research topics of 'Incremental Training of Neural Network for Motor Tasks Recognition Based on Brain-Computer Interface'. Together they form a unique fingerprint.

Cite this