Mixture of autoregressive modeling orders and its implication on single trial EEG classification
作者：
Highlights：
• Two methods for mixing AR features for EEG signal classification are proposed.
• Evolutionary and ensemble learning methods are considered.
• The results are assessed against a set of conventional order estimation methods.
• The feasibilities are investigated using several BCI competition datasets.
• Adequacy of Ensemble-based mixture and EA-based fusion methods are shown.
摘要
•Two methods for mixing AR features for EEG signal classification are proposed.•Evolutionary and ensemble learning methods are considered.•The results are assessed against a set of conventional order estimation methods.•The feasibilities are investigated using several BCI competition datasets.•Adequacy of Ensemble-based mixture and EA-based fusion methods are shown.
论文关键词：Autoregressive analysis,Genetic algorithm,Particle Swarm Optimization,Electroencephalogram
论文评审过程：Received 23 December 2015, Revised 10 August 2016, Accepted 11 August 2016, Available online 11 August 2016, Version of Record 20 August 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.08.044
