Cortical Signal Analysis and Advances in Functional Near-Infrared Spectroscopy Signal: A Review

The relatively good transparency of biological materials in the near infrared region of the spectrum permits sufficient photon transmission through organs in situ for the monitoring of cellular events. Observations by infrared transillumination in the exposed heart and in the brain in cephalo without surgical intervention show that oxygen sufficiency for cytochrome a,a3, function, changes in tissue blood volume, and the average hemoglobin-oxyhemoglobin equilibrium can be recorded effectively and in continuous fashion for research and clinical purposes. The copper atom associated with heme a3 did not respond to anoxia and may be reduced under normoxic conditions, whereas the heme-a copper was at least partially reducible.

A brain-computer interface (BCI) is a communication system that allows the use of brain activity to control computers or other external devices. It can, by bypassing the peripheral nervous system, provide a means of communication for people suffering from severe motor disabilities or in a persistent vegetative state. In this paper, brain-signal generation tasks, noise removal methods, feature extraction/selection schemes, and classification techniques for fNIRS-based BCI are reviewed. The most common brain areas for fNIRS BCI are the primary motor cortex and the prefrontal cortex. In relation to the motor cortex, motor imagery tasks were preferred to motor execution tasks since possible proprioceptive feedback could be avoided. In relation to the prefrontal cortex, fNIRS showed a significant advantage due to no hair in detecting the cognitive tasks like mental arithmetic, music imagery, emotion induction, etc. In removing physiological noise in fNIRS data, band-pass filtering was mostly used. However, more advanced techniques like adaptive filtering, independent component analysis (ICA), multi optodes arrangement, etc. are being pursued to overcome the problem that a band-pass filter cannot be used when both brain and physiological signals occur within a close band. In extracting features related to the desired brain signal, the mean, variance, peak value, slope, skewness, and kurtosis of the noised-removed hemodynamic response were used. For classification, the linear discriminant analysis method provided simple but good performance among others: support vector machine (SVM), hidden Markov model (HMM), artificial neural network, etc. fNIRS will be more widely used to monitor the occurrence of neuro-plasticity after neuro-rehabilitation and neuro-stimulation. Technical breakthroughs in the future are expected via bundled-type probes, hybrid EEG-fNIRS BCI, and through the detection of initial dips.

Near infrared spectroscopy (NIRS) is a promising technology for functional brain imaging which measures hemodynamic signals from the cortex, similar to functional magnetic resonance imaging (fMRI), but does not require the participant to lie motionless in a confined space. NIRS can therefore be used for more naturalistic experiments, including face to face communication, or natural body movements, and is well suited for real-time applications that may require lengthy training. However, improving signal quality and reducing noise, especially noise induced by head motion, is challenging, particularly for real time applications. Here we study the properties of head motion induced noise, and find that motion noise causes the measured oxygenated and deoxygenated hemoglobin signals, which are typically strongly negatively correlated, to become more positively correlated. Next, we develop a method to reduce noise based on the principle that the concentration changes of oxygenated and deoxygenated hemoglobin should be negatively correlated. We show that despite its simplicity, this method is effective in reducing noise and improving signal quality, for both online and offline noise reduction. Copyright 2009 Elsevier Inc. All rights reserved.