TY  - JOUR
A1  - Lemm, Steven
A1  - Curio, Gabriel
A1  - Hlushchuk, Yevhen
A1  - Müller, Klaus-Robert
T1  - Enhancing the signal-to-noise ratio of ICA-based extracted ERPs
N2  - When decomposing single trial electroencephalography it is a challenge to incorporate prior physiological knowledge. Here, we develop a method that uses prior information about the phase-locking property of event-related potentials in a regularization framework to bias a blind source separation algorithm toward an improved separation of single-trial phase-locked responses in terms of an increased signal-to-noise ratio. In particular, we suggest a transformation of the data, using weighted average of the single trial and trial-averaged response, that redirects the focus of source separation methods onto the subspace of event-related potentials. The practical benefit with respect to an improved separation of such components from ongoing background activity and extraneous noise is first illustrated on artificial data and finally verified in a real-world application of extracting single-trial somatosensory evoked potentials from multichannel EEG-recordings
Y1  - 2006
UR  - http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=10
U6  - https://doi.org/10.1109/Tbme.2006.870258
SN  - 0018-9294
ER  - 
TY  - JOUR
A1  - Lemm, Steven
A1  - Blankertz, Benjamin
A1  - Curio, Gabriel
A1  - Müller, Klaus-Robert
T1  - Spatio-spectral filters for improving the classification of single trial EEG
N2  - Data recorded in electroencephalogram (EEG)-based brain-computer interface experiments is generally very noisy, non-stationary, and contaminated with artifacts that can deteriorate discrimination/classification methods. In this paper, we extend the common spatial pattern (CSP) algorithm with the aim to alleviate these adverse effects. In particular, we suggest an extension of CSP to the state space, which utilizes the method of time delay embedding. As we will show, this allows for individually tuned frequency filters at each electrode position and, thus, yields an improved and more robust machine learning procedure. The advantages of the proposed method over the original CSP method are verified in terms of an improved information transfer rate (bits per trial) on a set of EEG-recordings from experiments of imagined limb movements
Y1  - 2005
SN  - 0018-9294
ER  -