Application of Phase Space Reconstruction in a Few-Channel EEG-NIRS Bimodal Brain-Computer Interface System

We developed a highly accurate, few-channel, bimodal electroencephalograph (EEG) and near-infrared spectroscopy (NIRS) brain-computer interface (BCI) system by developing new methods for signal processing and feature extraction. For data processing, we performed source analysis of EEG and NIRS signals to select the best channels from which to build a few-channel system. For EEG feature extraction, we used phase space reconstruction to convert EEG few-channel signals into multichannel signals, facilitating the extraction of EEG features by common spatial pattern. The Hurst exponent of the selected 10 channels constituted the extracted NIRS data feature. For pattern classification, we fused EEG and NIRS features together and used the support vector machine classification method. The average accuracy of bimodal EEG-NIRS was significantly higher than that of either EEG or NIRS as unimodal techniques.

[1]  Klaus-Robert Müller,et al.  Relationship between neural and hemodynamic signals during spontaneous activity studied with temporal kernel CCA. , 2010, Magnetic resonance imaging.

[2]  Noriaki Hattori,et al.  [Clinical application of functional near-infrared spectroscopy in rehabilitation medicine]. , 2010, Brain and nerve = Shinkei kenkyu no shinpo.

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

[4]  Rajesh P. N. Rao,et al.  Cortical activity during motor execution, motor imagery, and imagery-based online feedback , 2010, Proceedings of the National Academy of Sciences.

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

[6]  Theodore J. Huppert,et al.  Real-time imaging of human brain function by near-infrared spectroscopy using an adaptive general linear model , 2009, NeuroImage.

[7]  P E Lange,et al.  Cerebral oxygenation measured by near-infrared spectroscopy during circulatory arrest and cardiopulmonary resuscitation. , 2003, British journal of anaesthesia.

[8]  Andrew C. Papanicolaou,et al.  Brain activation profiles during kinesthetic and visual imagery: An fMRI study , 2016, Brain Research.

[9]  R D Pascual-Marqui,et al.  Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. , 2002, Methods and findings in experimental and clinical pharmacology.

[10]  R. Turner,et al.  Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Akihiro Ishikawa,et al.  Development of a new rehabilitation system based on a brain-computer interface using near-infrared spectroscopy. , 2010, Advances in experimental medicine and biology.

[12]  H. Obrig,et al.  Shedding light on words and sentences: Near-infrared spectroscopy in language research , 2012, Brain and Language.

[13]  T. Chau,et al.  Towards a system-paced near-infrared spectroscopy brain–computer interface: differentiating prefrontal activity due to mental arithmetic and mental singing from the no-control state , 2011, Journal of neural engineering.

[14]  F. Jolesz,et al.  Brain–machine interface via real-time fMRI: Preliminary study on thought-controlled robotic arm , 2009, Neuroscience Letters.

[15]  T. Ono,et al.  Brain Cortical Mapping by Simultaneous Recording of Functional Near Infrared Spectroscopy and Electroencephalograms from the Whole Brain During Right Median Nerve Stimulation , 2009, Brain Topography.

[16]  G. R. Müller-Putz,et al.  Cooperation in mind: Motor imagery of joint and single actions is represented in different brain areas , 2016, Brain and Cognition.

[17]  K. Zentgraf,et al.  Cognitive motor processes: The role of motor imagery in the study of motor representations , 2009, Brain Research Reviews.

[18]  Bettina Sorger,et al.  Another kind of 'BOLD Response': answering multiple-choice questions via online decoded single-trial brain signals. , 2009, Progress in brain research.

[19]  Andrzej Cichocki,et al.  Blind noise reduction for multisensory signals using ICA and subspace filtering, with application to EEG analysis , 2002, Biological Cybernetics.

[20]  E. John,et al.  Electroencephalography: Basic Principles and Applications , 2001 .

[21]  N. Birbaumer.  Breaking the silence: brain-computer interfaces (BCI) for communication and motor control. , 2006, Psychophysiology.

[22]  Gert Pfurtscheller,et al.  Motor imagery and direct brain-computer communication , 2001, Proc. IEEE.

[23]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[24]  Barak A. Pearlmutter,et al.  Linear Spatial Integration for Single-Trial Detection in Encephalography , 2002, NeuroImage.

[25]  A. Berthoz,et al.  Mental representations of movements. Brain potentials associated with imagination of hand movements. , 1995, Electroencephalography and clinical neurophysiology.

[26]  A. Villringer,et al.  Near infrared spectroscopy (NIRS): A new tool to study hemodynamic changes during activation of brain function in human adults , 1993, Neuroscience Letters.

[27]  Karl J. Friston.  Modalities, Modes, and Models in Functional Neuroimaging , 2009, Science.

[28]  A. Fallgatter,et al.  Functional near-infrared spectroscopy for the assessment of speech related tasks , 2012, Brain and Language.

[29]  Andrzej Cichocki,et al.  Adaptive Blind Signal and Image Processing - Learning Algorithms and Applications , 2002 .

[30]  Binbin Xu,et al.  Phase space reconstruction of an experimental model of cardiac field potential in normal and arrhythmic conditions , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[31]  G. Pfurtscheller,et al.  Motor imagery activates primary sensorimotor area in humans , 1997, Neuroscience Letters.