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ORICA: Online Recursive Independent Component Analysis

This package implements the online recursive ICA algorithm [1,2] for real-time adaptive blind source separation of high-density EEG data [1,3]. ORICA was also implemented as functions in BCILAB and EEGLAB and was integrated in an open-source Real-time EEG Source-mapping Toolbox (REST), supporting applications in ICA-based online artifact rejection, feature extraction for real-time biosignal monitoring in clinical environments, and adaptable classifications in brain-computer interfaces.

Motivation

Independent Component Analysis (ICA) has been widely applied to electroencephalographic (EEG) biosignal processing and brain-computer interfaces. The practical use of ICA, however, is limited by its computational complexity, data requirements for convergence, and assumption of data stationarity, especially for high-density data. Hence we develop an optimized online recursive ICA algorithm (ORICA) with online recursive least squares (RLS) whitening for blind source separation of high-density EEG data, which offers instantaneous incremental convergence upon presentation of new data.

To use ORICA

  • See testScript.m for example codes of running ORICA.
  • Use EEGLAB for performance evaluation. EEGLAB can be downloaded at Bitbucket

Files description

  • testScript.m - Example script that shows how to run ORICA.
  • SIM_STAT_16ch_3min.set - A simulated EEG data with 16-channel and 3-min with sampling rate 128 Hz, generated by random-coefficients autoregressive model.

References

[1] S.-H. Hsu, T. Mullen, T.-P Jung, and G. Cauwenberghs, "Real-time adaptive EEG source separation using online recursive independent component analysis," IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2016.

[2] S.-H. Hsu, T. Mullen, T.-P Jung, and G. Cauwenberghs, "Online recursive independent component analysis for real-time source separation of high-density EEG," in IEEE EMBS, 2014.

[3] S.-H. Hsu, L. Pion-Tanachini, T.-P Jung, and G. Cauwenberghs, "Tracking non-stationary EEG sources using adaptive online recursive independent component analysis," in IEEE EMBS, 2015.

Author

Sheng-Hsiou (Shawn) Hsu, SCCN, UCSD

For any question or potential collaboration, contact me at shh078 at ucsd dot edu.