Article

Discriminative Manifold Learning Based Detection of Movement-Related Cortical Potentials

IEEE Transactions on Neural Systems and Rehabilitation Engineering, Institute of Electrical and Electronics Engineers (IEEE), ISSN 1558-0210

Volume 24, 9, 2016

DOI:10.1109/tnsre.2016.2531118, Dimensions: pub.1061741082, PMID: 26955040,

Authors

Lin, Chuang (1) (2)
Jiang, N I N G (1) (2)
Xu, Ren (1) (2)
Farina, Dario * (1) (2)

* Corresponding author

Affiliations

Organisations

  1. (1) University Medical Center Göttingen, grid.411984.1
  2. (2) University of Göttingen, grid.7450.6
  3. (3) Iowa State University, grid.34421.30
  4. (4) Aalborg University, grid.5117.2, AAU

Description

The detection of voluntary motor intention from EEG has been applied to closed-loop brain-computer interfacing (BCI). The movement-related cortical potential (MRCP) is a low frequency component of the EEG signal, which represents movement intention, preparation, and execution. In this study, we aim at detecting MRCPs from single-trial EEG traces. For this purpose, we propose a detector based on a discriminant manifold learning method, called locality sensitive discriminant analysis (LSDA), and we test it in both online and offline experiments with executed and imagined movements. The online and offline experimental results demonstrated that the proposed LSDA approach for MRCP detection outperformed the Locality Preserving Projection (LPP) approach, which was previously shown to be the most accurate algorithm so far tested for MRCP detection. For example, in the online tests, the performance of LSDA was superior than LPP in terms of a significant reduction in false positives (FP) (passive FP: 1.6 ±0.9/min versus 2.9 ±1.0/min, p = 0.002, active FP: 2.2 ±0.8/min versus 2.7 ±0.6/min , p = 0.03 ), for a similar rate of true positives. In conclusion, the proposed LSDA based MRCP detection method is superior to previous approaches and is promising for developing patient-driven BCI systems for motor function rehabilitation as well as for neuroscience research.

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Times Cited: 17

Field Citation Ratio (FCR): 5.12

Relative Citation ratio (RCR): 0.49