Random ensemble learning for EEG classification

作者：

Highlights：

• Developing a multi-tier distributed computing seizure detection implemented in the cloud.

• Developing a feature-selection technique using I-ICA to elect independent features and to infer the number of features automatically from the input data.

• Proposing a random subspace ensemble method with SVMs as the base classifiers which fits big data problems by parallel processing.

• Proposing a random subspace ensemble method using a combination of different methods as the base classifiers.

摘要

•Developing a multi-tier distributed computing seizure detection implemented in the cloud.•Developing a feature-selection technique using I-ICA to elect independent features and to infer the number of features automatically from the input data.•Proposing a random subspace ensemble method with SVMs as the base classifiers which fits big data problems by parallel processing.•Proposing a random subspace ensemble method using a combination of different methods as the base classifiers.

论文关键词：Brain–computer interface,Distributed computing system,Electroencephalogram,Ensemble learning,Epileptic seizure detection,Computational neuroscience

论文评审过程：Received 15 June 2017, Revised 19 December 2017, Accepted 21 December 2017, Available online 3 January 2018, Version of Record 5 February 2018.

论文官网地址：https://doi.org/10.1016/j.artmed.2017.12.004