Gamers’ involvement detection from EEG data with cGAAM – A method for feature selection for clustering

作者：

Highlights：

• A genetic algorithm controlled by unsupervised classification was introduced.

• A joined-approach for clustering and feature selection was proposed.

• Three EEG features differentiating levels of players’ involvement were identified.

• The predominance of the proposed approach over other methods was shown.

摘要

•A genetic algorithm controlled by unsupervised classification was introduced.•A joined-approach for clustering and feature selection was proposed.•Three EEG features differentiating levels of players’ involvement were identified.•The predominance of the proposed approach over other methods was shown.

论文关键词：Feature selection,Clustering,Genetic algorithm,Game player involvement recognition,Brain activity patterns,EEG signal analysis

论文评审过程：Received 31 March 2017, Revised 26 January 2018, Accepted 27 January 2018, Available online 31 January 2018, Version of Record 22 February 2018.

论文官网地址：https://doi.org/10.1016/j.eswa.2018.01.046