Features Extraction from NIRS Data using Extreme Decomposition

the main aim of BCI builds a communicating bridge between brain and peripheral devices. NIRS is dependent on changes of blood flow, as it measures oxygenated and deoxygenated hemoglobin’s in the super-facial layers of the human cortex. We are able to detect HbO and HbR of imaged movement and movement on the surface of the brain with NIRS. If we want to achieve the control of external devices with HbO and HbR, the change of these data must be analyzed, and be extracted. In this paper, we present a new method to achieve in the analysis of the data of HbO and HbR, realize the removal of high frequency and achieve preliminary extraction the characteristics of the data. Secondary analysis of the extracted feature points could reduce the number of feature points. Designing a new compensated interpolation algorithm achieve completely new feature points to replace the original feature points to represent the data .The interpolated data curves response the change of original data, and realize  the removal of high frequency to smooth the output curve.

[1]  J. Wolpaw,et al.  Brain–computer interfaces in neurological rehabilitation , 2008, The Lancet Neurology.

[2]  Paulo Gonçalves,et al.  Empirical Mode Decompositions as Data-Driven Wavelet-like Expansions , 2004, Int. J. Wavelets Multiresolution Inf. Process..

[3]  Miguel A. L. Nicolelis,et al.  Brain–machine interfaces: past, present and future , 2006, Trends in Neurosciences.

[4]  N. Logothetis.  What we can do and what we cannot do with fMRI , 2008, Nature.

[5]  Jinglei Lv,et al.  FMRI Signal Analysis Using Empirical Mean Curve Decomposition , 2013, IEEE Transactions on Biomedical Engineering.

[6]  G. Buzsáki,et al.  Neuronal Oscillations in Cortical Networks , 2004, Science.

[7]  T. Muroga,et al.  Application to robot control using brain function measurement by near-infrared spectroscopy , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  C. Braun,et al.  Hand Movement Direction Decoded from MEG and EEG , 2008, The Journal of Neuroscience.

[9]  Klaus-Robert Müller,et al.  Neurophysiological predictor of SMR-based BCI performance , 2010, NeuroImage.

[10]  Klaus-Robert Müller,et al.  Enhanced Performance by a Hybrid Nirs–eeg Brain Computer Interface , 2022 .

[11]  Stephen M. Smith,et al.  fMRI resting state networks define distinct modes of long-distance interactions in the human brain , 2006, NeuroImage.

[12]  Moritz Grosse-Wentrup,et al.  Multitask Learning for Brain-Computer Interfaces , 2010, AISTATS.

[13]  D. Heeger,et al.  In this issue , 2002, Nature Reviews Drug Discovery.