Error correction output codding coupled with the CSP for motor imagery BCI systems

Error correction output codding coupled with the CSP for motor imagery BCI systems. Shahtalebi, S. & Mohammadi, A. In 2017 25th European Signal Processing Conference (EUSIPCO), pages 2071-2075, Aug, 2017.
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Motivated by the fact that modeling and representation of multi-class signal patterns plays a critical role in Electroencephalogram (EEG)-based brain computer interface (BCI) systems, the paper proposes the coupling of error correction output coding (ECOC) with the common spatial pattern (CSP) analysis. Referred to as the ECO-CSP framework, the ECOC approach is applied to EEG motor imagery classification problem. A BCI system designed to operate in real world conditions, must be able to discriminate multiple tasks and activities. This fact, expresses the urge to develop/implement classifiers intrinsically designed for multi-class problems. One of such techniques which is well regarded in other fields but has not yet been applied to EEG-based classification is the ECOC. The paper addresses this gap. The BCI Competition IV-2a dataset is used to evaluate the performance of the proposed ECO-CSP framework. Our results show that ECO-CSP achieve similar performance in comparison to the state-of-the-art algorithms but is extensively simpler with significantly less computational overhead making it a practical alternative for real-time EEG motor imagery classification tasks.

@InProceedings{8081574,
  author = {S. Shahtalebi and A. Mohammadi},
  booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
  title = {Error correction output codding coupled with the CSP for motor imagery BCI systems},
  year = {2017},
  pages = {2071-2075},
  abstract = {Motivated by the fact that modeling and representation of multi-class signal patterns plays a critical role in Electroencephalogram (EEG)-based brain computer interface (BCI) systems, the paper proposes the coupling of error correction output coding (ECOC) with the common spatial pattern (CSP) analysis. Referred to as the ECO-CSP framework, the ECOC approach is applied to EEG motor imagery classification problem. A BCI system designed to operate in real world conditions, must be able to discriminate multiple tasks and activities. This fact, expresses the urge to develop/implement classifiers intrinsically designed for multi-class problems. One of such techniques which is well regarded in other fields but has not yet been applied to EEG-based classification is the ECOC. The paper addresses this gap. The BCI Competition IV-2a dataset is used to evaluate the performance of the proposed ECO-CSP framework. Our results show that ECO-CSP achieve similar performance in comparison to the state-of-the-art algorithms but is extensively simpler with significantly less computational overhead making it a practical alternative for real-time EEG motor imagery classification tasks.},
  keywords = {brain-computer interfaces;electroencephalography;error correction codes;medical signal processing;signal classification;brain computer interface systems;error correction output coding;common spatial pattern analysis;ECO-CSP framework;ECOC approach;EEG motor imagery classification problem;multiclass problems;BCI Competition IV-2a dataset;EEG motor imagery classification tasks;motor imagery BCI systems;multiclass signal patterns;electroencephalogram;Electroencephalography;Feature extraction;Covariance matrices;Brain;Encoding;Signal processing;Brain-computer interface (BCI);Common spatial patterns;Electroencephalogram (EEG);Error correction output coding;Motor Imagery},
  doi = {10.23919/EUSIPCO.2017.8081574},
  issn = {2076-1465},
  month = {Aug},
}

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