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Tensor decomposition of EEG signals for transfer learning applications

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  • Additional Information
    • Contributors:
      Lund University, Faculty of Medicine, Department of Clinical Sciences, Malmö, Clinical Memory Research, Lunds universitet, Medicinska fakulteten, Institutionen för kliniska vetenskaper, Malmö, Klinisk minnesforskning, Originator; Lund University, Faculty of Engineering, LTH, Departments at LTH, Department of Automatic Control, Lunds universitet, Lunds Tekniska Högskola, Institutioner vid LTH, Institutionen för reglerteknik, Originator; Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), MultiPark: Multidisciplinary research focused on Parkinson´s disease, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), MultiPark: Multidisciplinary research focused on Parkinson´s disease, Originator; Lund University, Profile areas and other strong research environments, Lund University Profile areas, LU Profile Area: Natural and Artificial Cognition, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Lunds universitets profilområden, LU profilområde: Naturlig och artificiell kognition, Originator
    • Abstract:
      We address the recognized person-to-person Brain–Computer Interface (BCI) calibration problem and tackle session-dependency through the use of unsupervised canonical polyadic (CP) tensor decomposition. For a motor imagery task, the approach reveals universal structures within EEG data, common between subjects and prominent for a certain task. Further, we develop a novel similarity measure that includes weighting of the decomposition’s factor matrices, and argue that it is more representative than what has previously been presented in literature. The proposed similarity measure shows potential in a BCI classification task, i.e. drowsiness during simulated driving (average Pearson correlation of 0.6).