Evolving Training Sets for Improved Transfer Learning in Brain Computer Interfaces

被引：5

作者：

Adair, Jason ^{[1
]}

Brownlee, Alexander ^{[1
]}

Daolio, Fabio ^{[1
]}

Ochoa, Gabriela ^{[1
]}

机构：

[1] Univ Stirling, Comp Sci & Math, Stirling, Scotland

来源：

MACHINE LEARNING, OPTIMIZATION, AND BIG DATA, MOD 2017 | 2018年 / 10710卷

基金：

英国工程与自然科学研究理事会;

关键词：

Optimisation; Machine learning; Ensemble Brain-computer interface; P300; Evolutionary computation Transfer learning;

D O I：

10.1007/978-3-319-72926-8_16

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A new proof-of-concept method for optimising the performance of Brain Computer Interfaces (BCI) while minimising the quantity of required training data is introduced. This is achieved by using an evolutionary approach to rearrange the distribution of training instances, prior to the construction of an Ensemble Learning Generic Information (ELGI) model. The training data from a population was optimised to emphasise generality of the models derived from it, prior to a recombination with participant-specific data via the ELGI approach, and training of classifiers. Evidence is given to support the adoption of this approach in the more difficult BCI conditions: smaller training sets, and those suffering from temporal drift. This paper serves as a case study to lay the groundwork for further exploration of this approach.

引用

页码：186 / 197

页数：12

共 16 条

[1] Optimizing spatial filters for robust EEG single-trial analysis [J].