title: Three-way analysis of spectrospatial electromyography data: classification and interpretation.
abstract: Classifying multivariate electromyography (EMG) data is an important problem in prosthesis control as well as in neurophysiological studies and diagnosis. With modern high-density EMG sensor technology, it is possible to capture the rich spectrospatial structure of the myoelectric activity. We hypothesize that multi-way machine learning methods can efficiently utilize this structure in classification as well as reveal interesting patterns in it. To this end, we investigate the suitability of existing three-way classification methods to EMG-based hand movement classification in spectrospatial domain, as well as extend these methods by sparsification and regularization. We propose to use Fourier-domain independent component analysis as preprocessing to improve classification and interpretability of the results. In high-density EMG experiments on hand movements across 10 subjects, three-way classification yielded higher average performance compared with state-of-the art classification based on temporal features, suggesting that the three-way analysis approach can efficiently utilize detailed spectrospatial information of high-density EMG. Phase and amplitude patterns of features selected by the classifier in finger-movement data were found to be consistent with known physiology. Thus, our approach can accurately resolve hand and finger movements on the basis of detailed spectrospatial information, and at the same time allows for physiological interpretation of the results.
authors.affiliation: Dept. of Computer Science/HIIT, University of Helsinki, Helsinki, Finland; Brain Research Unit, O.V. Lounasmaa Laboratory, Aalto University, Espoo, Finland.
authors.forename: Jukka-Pekka
authors.initials: JP
authors.lastname: Kauppi
authors.affiliation: Dept. of Computer Science, Machine Learning Group, Berlin Institute of Technology, Berlin, Germany; Dept. of Neurorehabilitation Engineering, Universitätsmedizin Göttingen, Göttingen, Germany.
authors.forename: Janne
authors.initials: J
authors.lastname: Hahne
authors.affiliation: Dept. of Computer Science, Machine Learning Group, Berlin Institute of Technology, Berlin, Germany; Dept. of Brain and Cognitive Engineering, Korea University, Seoul, Korea.
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller
authors.affiliation: Dept. of Computer Science/HIIT, University of Helsinki, Helsinki, Finland; Dept. of Dynamic Brain Imaging, Advanced Telecommunication Research Institute International (ATR), Kyoto, Japan.
authors.forename: Aapo
authors.initials: A
authors.lastname: Hyvärinen
mesh_terms: D004576:Electromyography / Q000379:methods*; D001330:Electronic Data Processing / Q000379:methods*; D005260:Female; D006225:Hand / Q000502:physiology*; D006801:Humans; D008297:Male; D009068:Movement / Q000502:physiology*