Decoding of finger activation from ECoG data: a comparative study

Motor Brain-Computer Interfaces (BCIs) are systems that allow severely motor-impaired patients to use their brain activity to interact with their environment. Electrocorticography (ECoG) arrays may be profitably used to develop safe and chronic motor BCI systems. BCI signal processing pipelines generally include neuronal signal pre-processing, feature extraction and classification/regression. The article presents a comparative study addressing the problem of neural feature classification in asynchronous multi-limb ECoG-driven BCIs. Several conventional classifiers often reported in the BCI literature were coupled with two preprocessing techniques and with a conventional feature extraction approach. They were compared to artificial neural network (ANN) end-to-end classifiers which mimic conventional BCI signal processing pipelines. Different initializations of ANNs were particularly studied. The comparison study was carried out using publicly available datasets (BCI competition IV).

[1]  Ali Farhadi,et al.  AJILE Movement Prediction: Multimodal Deep Learning for Natural Human Neural Recordings and Video , 2017, AAAI.

[2]  Thomas G. Dietterich,et al.  Solving Multiclass Learning Problems via Error-Correcting Output Codes , 1994, J. Artif. Intell. Res..

[3]  R. Tibshirani,et al.  Regression shrinkage and selection via the lasso: a retrospective , 2011 .

[4]  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.

[5]  Elaine B. Martin,et al.  Model selection for partial least squares regression , 2002 .

[6]  Andrey Eliseyev,et al.  Stable and artifact-resistant decoding of 3D hand trajectories from ECoG signals using the generalized additive model , 2014, Journal of neural engineering.

[7]  Andrey Eliseyev,et al.  Iterative N-way partial least squares for a binary self-paced brain–computer interface in freely moving animals , 2011, Journal of neural engineering.

[8]  Nicholas P. Szrama,et al.  Decoding three-dimensional reaching movements using electrocorticographic signals in humans , 2016, Journal of neural engineering.

[9]  Andrew McCallum,et al.  An Introduction to Conditional Random Fields , 2010, Found. Trends Mach. Learn..

[10]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update , 2018, Journal of neural engineering.

[11]  David B. Grayden,et al.  Consistency of Long-Term Subdural Electrocorticography in Humans , 2017, IEEE Transactions on Biomedical Engineering.

[12]  G. Schalk,et al.  Brain-Computer Interfaces Using Electrocorticographic Signals , 2011, IEEE Reviews in Biomedical Engineering.

[13]  Trevor Hastie,et al.  Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.

[14]  Naotaka Fujii,et al.  A new method for quantifying the performance of EEG blind source separation algorithms by referencing a simultaneously recorded ECoG signal , 2017, Neural Networks.

[15]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[16]  Francesco Visin,et al.  A guide to convolution arithmetic for deep learning , 2016, ArXiv.

[17]  Naotaka Fujii,et al.  Decoding continuous three-dimensional hand trajectories from epidural electrocorticographic signals in Japanese macaques , 2012, Journal of neural engineering.

[18]  Amit Konar,et al.  Performance analysis of left/right hand movement classification from EEG signal by intelligent algorithms , 2011, 2011 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB).

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

[20]  Rabab K. Ward,et al.  Comparing Different Classifiers in Sensory Motor Brain Computer Interfaces , 2015, PloS one.

[21]  C. Braun,et al.  A review on directional information in neural signals for brain-machine interfaces , 2009, Journal of Physiology-Paris.

[22]  S. Czepiel,et al.  Maximum Likelihood Estimation of Logistic Regression Models : Theory and Implementation , 2022 .

[23]  J. Wolpaw,et al.  Decoding two-dimensional movement trajectories using electrocorticographic signals in humans , 2007, Journal of neural engineering.

[24]  Rajesh P. N. Rao,et al.  Generalized Features for Electrocorticographic BCIs , 2008, IEEE Transactions on Biomedical Engineering.

[25]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[26]  Guillaume Charvet,et al.  WIMAGINE: Wireless 64-Channel ECoG Recording Implant for Long Term Clinical Applications , 2015, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[27]  Odelia Schwartz,et al.  Decoding of finger trajectory from ECoG using deep learning , 2018, Journal of neural engineering.

[28]  Cuntai Guan,et al.  Multi-class filter bank common spatial pattern for four-class motor imagery BCI , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[29]  Y Liu,et al.  The effects of spatial filtering and artifacts on electrocorticographic signals. , 2015, Journal of neural engineering.

[30]  T. Aksenova,et al.  Data-Driven Transducer Design and Identification for Internally-Paced Motor Brain Computer Interfaces: A Review , 2018, Front. Neurosci..

[31]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[32]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[33]  Alain Rakotomamonjy,et al.  Decoding Finger Movements from ECoG Signals Using Switching Linear Models , 2011, Front. Neurosci..

[34]  Andrey Eliseyev,et al.  L1-Penalized N-way PLS for subset of electrodes selection in BCI experiments , 2012, Journal of neural engineering.