Speeded Near Infrared Spectroscopy (NIRS) Response Detection

Xu Cui,Signe Bray,Allan L Reiss

doi:10.1371/journal.pone.0015474

Xu Cui, Signe Bray + Show 1 more

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Journal: PLoS ONE	Publication Date: Nov 11, 2010
Citations: 122	License type: CC BY 4.0

Affiliation: Stanford University

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The hemodynamic response measured by Near Infrared Spectroscopy (NIRS) is temporally delayed from the onset of the underlying neural activity. As a consequence, NIRS based brain-computer-interfaces (BCIs) and neurofeedback learning systems, may have a latency of several seconds in responding to a change in participants' behavioral or mental states, severely limiting the practical use of such systems. To explore the possibility of reducing this delay, we used a multivariate pattern classification technique (linear support vector machine, SVM) to decode the true behavioral state from the measured neural signal and systematically evaluated the performance of different feature spaces (signal history, history gradient, oxygenated or deoxygenated hemoglobin signal and spatial pattern). We found that the latency to decode a change in behavioral state can be reduced by 50% (from 4.8 s to 2.4 s), which will enhance the feasibility of NIRS for real-time applications.

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Brain computer interface (BCI) technologies aim to interpret neural commands and translate them into communicative signals or actions, through a computer interface
Several technologies have been studied for recording neural activity in BCI systems, including direct electrophysiological recordings [1,2,3,4], electroencephalography (EEG) [5,6], functional magnetic resonance imaging [7,8] and near infrared spectroscopy (NIRS) [9,10,11,12]
We systematically evaluated the addition of features to improve classification delay, and present the results in the following order: 1) we show the result of a baseline classifier on a single participant to demonstrate that there is a long delay between the predicted and actual onset of finger tapping; 2) we test the effect of including amplitude history, and demonstrate that the classification delay can be significantly reduced; 3) we show that including the history gradient or second order gradient does not improve the classification onset or offset delay; 4) we show that including both oxy- and deoxy-Hb improves accuracy; and 5) we find that including signals from additional channels improves accuracy and reduces delay

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Brain computer interface (BCI) technologies aim to interpret neural commands and translate them into communicative signals or actions, through a computer interface. Several technologies have been studied for recording neural activity in BCI systems, including direct electrophysiological recordings [1,2,3,4], electroencephalography (EEG) [5,6], functional magnetic resonance imaging (fMRI) [7,8] and near infrared spectroscopy (NIRS) [9,10,11,12]. These technologies each present their own advantages and disadvantages, and can be roughly divided according to whether they measure electrical signals, or hemodynamic signals which are an indirect measure of neural activity. EEG measures electrical potentials from the scalp, and has been successfully applied in several BCI applications [5,6], but the spatial resolution of this technology is relatively poor

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