Transfer Learning Based on Regularized Common Spatial Patterns Using Cosine Similarities of Spatial Filters for Motor-Imagery BCI

In motor-imagery brain–computer interface (BCI), transfer learning based on the framework of regularized common spatial patterns (RCSP) can make full use of the training data derived from other sub...

[1]  T. Jung,et al.  Improving EEG-Based Emotion Classification Using Conditional Transfer Learning , 2017, Front. Hum. Neurosci..

[2]  Jie Li,et al.  Phase Interval Value Analysis for the Motor Imagery Task in BCI , 2009, J. Circuits Syst. Comput..

[3]  Seungjin Choi,et al.  Bayesian common spatial patterns for multi-subject EEG classification , 2014, Neural Networks.

[4]  Jing Hua,et al.  Direct Arrhythmia Classification from Compressive ECG Signals in Wearable Health Monitoring System , 2017, J. Circuits Syst. Comput..

[5]  Klaus-Robert Müller,et al.  Towards Zero Training for Brain-Computer Interfacing , 2008, PloS one.

[6]  David M. W. Powers,et al.  PSO-based dimension reduction of EEG recordings: Implications for subject transfer in BCI , 2013, Neurocomputing.

[7]  J. Friedman.  Regularized Discriminant Analysis , 1989 .

[8]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[9]  Elizabeth P. Chou,et al.  Cosine similarity as a sample size-free measure to quantify phase clustering within a single neurophysiological signal , 2018, Journal of Neuroscience Methods.

[10]  Clemens Brunner,et al.  Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks , 2006, NeuroImage.

[11]  G. Pfurtscheller,et al.  The BCI competition III: validating alternative approaches to actual BCI problems , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[12]  Shun-ichi Amari.  Information Geometry of Positive Measures and Positive-Definite Matrices: Decomposable Dually Flat Structure , 2014, Entropy.

[13]  Seungjin Choi,et al.  Composite Common Spatial Pattern for Subject-to-Subject Transfer , 2009, IEEE Signal Processing Letters.

[14]  Cuntai Guan,et al.  Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms , 2011, IEEE Transactions on Biomedical Engineering.

[15]  Ali Motie Nasrabadi,et al.  Subject transfer BCI based on Composite Local Temporal Correlation Common Spatial Pattern , 2015, Comput. Biol. Medicine.

[16]  Haixian Wang,et al.  Regularized Filters for L1-Norm-Based Common Spatial Patterns , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[17]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.

[18]  Klaus-Robert Müller,et al.  The non-invasive Berlin Brain–Computer Interface: Fast acquisition of effective performance in untrained subjects , 2007, NeuroImage.

[19]  Fabien Lotte,et al.  Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain–Computer Interfaces , 2015, Proceedings of the IEEE.

[20]  Moritz Grosse-Wentrup,et al.  Beamforming in Noninvasive Brain–Computer Interfaces , 2009, IEEE Transactions on Biomedical Engineering.

[21]  Dong Xu,et al.  Comprehensive Common Spatial Patterns With Temporal Structure Information of EEG Data: Minimizing Nontask Related EEG Component , 2012, IEEE Transactions on Biomedical Engineering.

[22]  Motoaki Kawanabe,et al.  Stationary common spatial patterns for brain–computer interfacing , 2012, Journal of neural engineering.

[23]  Daniel A. Braun,et al.  Motor Task Variation Induces Structural Learning , 2009, Current Biology.

[24]  G. Pfurtscheller,et al.  Optimal spatial filtering of single trial EEG during imagined hand movement. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[25]  Motoaki Kawanabe,et al.  Learning a common dictionary for subject-transfer decoding with resting calibration , 2015, NeuroImage.

[26]  Haiping Lu,et al.  Regularized Common Spatial Pattern With Aggregation for EEG Classification in Small-Sample Setting , 2010, IEEE Transactions on Biomedical Engineering.

[27]  Alexandre Barachant,et al.  Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review , 2017 .

[28]  G. Pfurtscheller,et al.  Designing optimal spatial filters for single-trial EEG classification in a movement task , 1999, Clinical Neurophysiology.

[29]  Pengjiang Qian,et al.  Seizure Classification From EEG Signals Using Transfer Learning, Semi-Supervised Learning and TSK Fuzzy System , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[30]  Philip S. Yu,et al.  Adaptation Regularization: A General Framework for Transfer Learning , 2014, IEEE Transactions on Knowledge and Data Engineering.

[31]  Xiaomu Song,et al.  Improving brain-computer interface classification using adaptive common spatial patterns , 2015, Comput. Biol. Medicine.

[32]  Bernhard Schölkopf,et al.  Transfer Learning in Brain-Computer Interfaces , 2015, IEEE Computational Intelligence Magazine.

[33]  Jianjun Meng,et al.  Improved Semisupervised Adaptation for a Small Training Dataset in the Brain–Computer Interface , 2014, IEEE Journal of Biomedical and Health Informatics.

[34]  Haixian Wang,et al.  Regularized common spatial patterns with subject-to-subject transfer of EEG signals , 2017, Cognitive Neurodynamics.

[35]  Klaus-Robert Müller,et al.  True Zero-Training Brain-Computer Interfacing – An Online Study , 2014, PloS one.

[36]  Wojciech Samek,et al.  Transferring Subspaces Between Subjects in Brain--Computer Interfacing , 2012, IEEE Transactions on Biomedical Engineering.