Dynamic topographical pattern classification of multichannel prefrontal NIRS signals

• Published in: Journal of neural engineering 2013-08, Vol.10 (4), p.046018-046018
• Main Authors: Schudlo, Larissa C, Power, Sarah D, Chau, Tom
• Format: Article
• Language: English
• Subjects: Adult; Algorithms; Brain; Brain Mapping - methods; brain-computer interface; Classification; Classifiers; Cognition - physiology; Dynamics; Female; Human-computer interface; Humans; Male; Multichannel; near-infrared spectroscopy; near-infrared topography; Oxygen - metabolism; Pattern Recognition, Automated - methods; prefrontal cortex; Prefrontal Cortex - physiology; Reproducibility of Results; Sensitivity and Specificity; Spectroscopy, Near-Infrared - methods; Tasks; Temporal logic
• ISSN: 1741-2560; 1741-2552
• DOI: 10.1088/1741-2560/10/4/046018

Objective. Near-infrared spectroscopy (NIRS) is an optical imaging technique that has recently been considered for brain-computer interface (BCI) applications. To date, NIRS-BCI studies have primarily made use of temporal features of brain activity, derived from the time-course of optical signals measured from discrete locations, to differentiate mental states. However, functional brain imaging studies have indicated that the spatial distribution of haemodynamic activity is also rich in information. Thus, the progression of a response over both time and space may be valuable to brain state classification. In this paper, we investigate the implication of including spatiotemporal features in the single-trial classification of haemodynamic events for a two-class problem by exploiting this information from dynamic NIR topograms. Approach. The value of spatiotemporal information was explored through a comparative analysis of four different classification schemes performed on multichannel NIRS data collected from the prefrontal cortex during a mental arithmetic activation task and rest. Employing a linear discriminant classifier, data were analysed using spatiotemporal features, temporal features, and a collective pool of spatiotemporal and temporal features. We also considered a majority vote combination of three classifiers; each established using one of the above feature sets. Lastly, two separate task durations (20 and 10 s) were considered for feature extraction. Main results. With features from the longer task interval, the highest overall classification accuracy was achieved using the majority voting classifier (76.1 ± 8.4%), which was greater than the accuracy obtained using temporal features alone (73.5 ± 8.5%) (F3,144 = 7.04, p = 0.0002). While results from the shorter task duration were lower overall, the classifier employing only spatiotemporal features (with an average accuracy of 67.9 ± 9.3%) achieved a higher average accuracy than the rate obtained using only temporal features (64.4 ± 8.4%) (F3,144 = 18.58, p < 10−4). Significance. Collectively, these results suggest that spatiotemporal information can be of value in the analysis of functional NIRS data, and improved classification rates may be obtained in future NIRS-BCI applications with the inclusion of this information.