Classification of prefrontal activity due to mental arithmetic and music imagery using hidden Markov models and frequency domain near-infrared spectroscopy

Abstract

Near-infrared spectroscopy (NIRS) has recently been investigated as a non-invasive brain-computer interface (BCI). In particular, previous research has shown that NIRS signals recorded from the motor cortex during left- and right-hand imagery can be distinguished, providing a basis for a two-choice NIRS-BCI. In this study, we investigated the feasibility of an alternative two-choice NIRS-BCI paradigm based on the classification of prefrontal activity due to two cognitive tasks, specifically mental arithmetic and music imagery. Deploying a dual-wavelength frequency domain near-infrared spectrometer, we interrogated nine sites around the frontopolar locations (International 10-20 System) while ten able-bodied adults performed mental arithmetic and music imagery within a synchronous shape-matching paradigm. With the 18 filtered AC signals, we created task- and subject-specific maximum likelihood classifiers using hidden Markov models. Mental arithmetic and music imagery were classified with an average accuracy of 77.2% +/- 7.0 across participants, with all participants significantly exceeding chance accuracies. The results suggest the potential of a two-choice NIRS-BCI based on cognitive rather than motor tasks.

Functional Brain Imaging Using Non-Invasive Non-Ionizing Methods: Towards Multimodal and Multiscale Imaging

“…After early studies employing single-site near-infrared spectroscopy, first near-infrared imaging devices are being applied successfully for lowresolution functional brain imaging (Strangman et al, 2002;Villringer and Chance, 1997). NIRS is a very portable technique (Atsumori et al, 2009) and has been proposed as a useful technology for non-invasive brain-computer interfaces (Power et al, 2010;Coyle et al, 2004Coyle et al, , 2007Sitaram et al, 2007;Utsugi et al, 2008). NIRS has been used to study brain activity in both event-related tasks and resting states (Boecker et al, 2007;Herrmann et al, 2005;White et al, 2009;Honda et al, 2010;Zhang et al, 2010;Lu et al, 2010).…”

Classification of Hemodynamic Responses Associated With Force and Speed Imagery for a Brain-Computer Interface

“…Power et al [22] used hidden markov model (HMM) to perform the classification of mental arithmetic and music imagery with an average accuracy of 77.2 % across all participants, providing a two-choice NIRS-BCI system. In the work of Luu et at.…”

Comparison of Optical and Concentration Feature Used for fNIRS-Based BCI System Using HMM

“…fNIRS technology employs near-infrared light at two or more wavelengths to dectect the Deoxy-hemoglobin (Hb) and Oxy-hemoglobin (HbO) concentration changes in the brain cortex using the modified Beer-Lambert Law [15]. Both the optical signal [17,18] and concentration signal [19,20] can be used for BCI control. However, no paper has compared the performance difference between optical signal and concentration signal when they are used for BCI classification.…”