Open AccessSliding HDCA: Single-Trial EEG Classification to Overcome and Quantify Temporal Variability
Author(s) -
Amar R. Marathe,
Anthony J. Ries,
Kaleb McDowell
Publication year - 2014
Publication title -
ieee transactions on neural systems and rehabilitation engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.093
H-Index - 140
eISSN - 1558-0210
pISSN - 1534-4320
DOI - 10.1109/tnsre.2014.2304884
Subject(s) - bioengineering , computing and processing , robotics and control systems , signal processing and analysis , communication, networking and broadcast technologies
Patterns of neural data obtained from electroencephalography (EEG) can be classified by machine learning techniques to increase human-system performance. In controlled laboratory settings this classification approach works well; however, transitioning these approaches into more dynamic, unconstrained environments will present several significant challenges. One such challenge is an increase in temporal variability in measured behavioral and neural responses, which often results in suboptimal classification performance. Previously, we reported a novel classification method designed to account for temporal variability in the neural response in order to improve classification performance by using sliding windows in hierarchical discriminant component analysis (HDCA), and demonstrated a decrease in classification error by over 50% when compared to the standard HDCA method (Marathe et al., 2013). Here, we expand upon this approach and show that embedded within this new method is a novel signal transformation that, when applied to EEG signals, significantly improves the signal-to-noise ratio and thereby enables more accurate single-trial analysis. The results presented here have significant implications for both brain-computer interaction technologies and basic science research into neural processes.