Regularized Common Spatial Pattern With Aggregation for EEG Classification in Small-Sample Setting

By Haiping Lu, How Lung Eng, Cuntai Guan, K. Plataniotis, Anastasios N. Venetsanopoulos. IEEE Transactions on Biomedical Engineering, 2010

316 citations recorded by the source corpus.

Common spatial pattern (CSP) is a popular algorithm for classifying electroencephalogram (EEG) signals in the context of brain-computer interfaces (BCIs). This paper presents a regularization and aggregation technique for CSP in a small-sample setting (SSS). Conventional CSP is based on a sample-based covariance-matrix estimation. Hence, its performance in EEG classification deteriorates if the number of training samples is small. To address this concern, a regularized CSP (R-CSP) algorithm is proposed, where the covariance-matrix estimation is regularized by two parameters to lower the estimation variance while reducing the estimation bias. To tackle the problem of regularization parameter determination, R-CSP with aggregation (R-CSP-A) is further proposed, where a number of R-CSPs are aggregated to give an ensemble-based solution. The proposed algorithm is evaluated on data set IVa of BCI Competition III against four other competing algorithms. Experiments show that R-CSP-A significantly outperforms the other methods in average classification performance in three sets of experiments across various testing scenarios, with particular superiority in SSS.

Subfields: Cognitive Neuroscience, Human-Computer Interaction, Signal Processing

View Paper: https://www.semanticscholar.org/paper/725f9f9a211beec5b4f78878880302c53b0f4573

How researchers see this paper

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In Cognitive Neuroscience

  1. Fabien Lotte
  2. Dongrui Wu
  3. Cuntai Guan

In Human-Computer Interaction

  1. Fabien Lotte

In Signal Processing

  1. Fabien Lotte
  2. Dongrui Wu

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