TY - JOUR PY - 2013// TI - Comparison of EEG-features and classification methods for motor imagery in patients with disorders of consciousness JO - PLoS one A1 - Höller, Yvonne A1 - Bergmann, Jürgen A1 - Thomschewski, Aljoscha A1 - Kronbichler, Martin A1 - Höller, Peter A1 - Crone, Julia S. A1 - Schmid, Elisabeth V. A1 - Butz, Kevin A1 - Nardone, Raffaele A1 - Trinka, Eugen SP - e80479 EP - e80479 VL - 8 IS - 11 N2 - Current research aims at identifying voluntary brain activation in patients who are behaviorally diagnosed as being unconscious, but are able to perform commands by modulating their brain activity patterns. This involves machine learning techniques and feature extraction methods such as applied in brain computer interfaces. In this study, we try to answer the question if features/classification methods which show advantages in healthy participants are also accurate when applied to data of patients with disorders of consciousness. A sample of healthy participants (N = 22), patients in a minimally conscious state (MCS; N = 5), and with unresponsive wakefulness syndrome (UWS; N = 9) was examined with a motor imagery task which involved imagery of moving both hands and an instruction to hold both hands firm. We extracted a set of 20 features from the electroencephalogram and used linear discriminant analysis, k-nearest neighbor classification, and support vector machines (SVM) as classification methods. In healthy participants, the best classification accuracies were seen with coherences (mean = .79; range = .53-.94) and power spectra (mean = .69; range = .40-.85). The coherence patterns in healthy participants did not match the expectation of central modulated [Formula: see text]-rhythm. Instead, coherence involved mainly frontal regions. In healthy participants, the best classification tool was SVM. Five patients had at least one feature-classifier outcome with p[Formula: see text]0.05 (none of which were coherence or power spectra), though none remained significant after false-discovery rate correction for multiple comparisons. The present work suggests the use of coherences in patients with disorders of consciousness because they show high reliability among healthy subjects and patient groups. However, feature extraction and classification is a challenging task in unresponsive patients because there is no ground truth to validate the results.

Language: en

LA - en SN - 1932-6203 UR - http://dx.doi.org/10.1371/journal.pone.0080479 ID - ref1 ER -