A Brain-Computer Interface for Mental Arithmetic Task from Single-Trial Near-Infrared Spectroscopy Brain Signals

Near-infrared spectroscopy (NIRS) enables non-invasive recording of cortical hemoglobin oxygenation in human subjects through the intact skull using light in the near-infrared range to determine. Recently, NIRS-based brain-computer interfaces are introduced for discriminating left and right-hand motor imagery. A neuroimaging study has also revealed event-related hemodynamic responses associated with the performance of mental arithmetic tasks. This paper proposes a novel BCI for detecting changes resulting from increases in the magnitude of operands used in a mental arithmetic task, using data from single-trial NIRS brain signals. We measured hemoglobin responses from 20 healthy subjects as they solved mental arithmetic problems with three difficulty levels. Accuracy in recognizing one difficulty level from another is then presented using 5´5-fold cross-validations on the data collected. The results yielded an overall average accuracy of 71.2%, thus demonstrating potential in the proposed NIRS-based BCI in recognizing difficulty of problems encountered by mental arithmetic problem solvers.

[1]  Carles Escera,et al.  Problem size effect in additions and subtractions: an event-related potential study , 2004, Neuroscience Letters.

[2]  Cuntai Guan,et al.  Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain–computer interface , 2007, NeuroImage.

[3]  Shirley M Coyle,et al.  Brain–computer interface using a simplified functional near-infrared spectroscopy system , 2007, Journal of neural engineering.

[4]  M. Tanida,et al.  Relation between asymmetry of prefrontal cortex activities and the autonomic nervous system during a mental arithmetic task: near infrared spectroscopy study , 2004, Neuroscience Letters.

[5]  Robert J. K. Jacob,et al.  Combining Electroencephalograph and Functional Near Infrared Spectroscopy to Explore Users' Mental Workload , 2009, HCI.

[6]  Shirley Coyle,et al.  On the suitability of near-infrared (NIR) systems for next-generation brain-computer interfaces. , 2004, Physiological measurement.

[7]  B. Sankur,et al.  Complexity and functional clusters of the brain during mental arithmetic , 2008, 2008 IEEE 16th Signal Processing, Communication and Applications Conference.

[8]  Dennis J. McFarland,et al.  Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.

[9]  E. Parzen On Estimation of a Probability Density Function and Mode , 1962 .

[10]  Ata Akin,et al.  Hemodynamic correlates of mental arithmetic task in migraine , 2009, 2009 14th National Biomedical Engineering Meeting.

[11]  D. Boas,et al.  HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain. , 2009, Applied optics.

[12]  Robert J. K. Jacob,et al.  Brain measurement for usability testing and adaptive interfaces: an example of uncovering syntactic workload with functional near infrared spectroscopy , 2009, CHI.

[13]  A. Villringer,et al.  Non-invasive optical spectroscopy and imaging of human brain function , 1997, Trends in Neurosciences.

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

[15]  Chong-Ho Choi,et al.  Input Feature Selection by Mutual Information Based on Parzen Window , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Sungho Tak,et al.  Wavelet minimum description length detrending for near-infrared spectroscopy. , 2009, Journal of biomedical optics.

[17]  Kai Keng Ang,et al.  Rough set-based neuro-fuzzy system , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.