Near-infrared spectroscopy (NIRS)-based eyes-closed brain-computer interface (BCI) using prefrontal cortex activation due to mental arithmetic

www.nature.com/scientificreports OPEN received: 15 June 2016 accepted: 12 October 2016 Published: 08 November 2016 Near-infrared spectroscopy (NIRS)based eyes-closed brain-computer interface (BCI) using prefrontal cortex activation due to mental arithmetic Jaeyoung Shin1, Klaus-R Müller1,2 & Han-Jeong Hwang3 We propose a near-infrared spectroscopy (NIRS)-based brain-computer interface (BCI) that can be operated in eyes-closed (EC) state. To evaluate the feasibility of NIRS-based EC BCIs, we compared the performance of an eye-open (EO) BCI paradigm and an EC BCI paradigm with respect to hemodynamic response and classification accuracy. To this end, subjects performed either mental arithmetic or imagined vocalization of the English alphabet as a baseline task with very low cognitive loading. The performances of two linear classifiers were compared; resulting in an advantage of shrinkage linear discriminant analysis (LDA). The classification accuracy of EC paradigm (75.6 ± 7.3%) was observed to be lower than that of EO paradigm (77.0 ± 9.2%), which was statistically insignificant (p = 0.5698). Subjects reported they felt it more comfortable (p = 0.057) and easier (p < 0.05) to perform the EC BCI tasks. The different task difficulty may become a cause of the slightly lower classification accuracy of EC data. From the analysis results, we could confirm the feasibility of NIRS-based EC BCIs, which can be a BCI option that may ultimately be of use for patients who cannot keep their eyes open consistently. Near-infrared spectroscopy (NIRS) is an emerging neuroimaging technology which can monitor cortical activation using near-infrared light in the range of 600–900 nm. NIRS has many advantages: it is non-invasive, easy to use, has relatively low cost and is portable1. Many researchers have used NIRS to monitor cortical activity since JJ Jobsis first, in 1977, used NIRS to measure cerebral state changes when hyperventilating voluntarily2. Recently, NIRS has been also utilized for brain-computer interface (BCI) researches3,4 that aim at establishing a new communication modality for severely paralyzed patients using only their brain signals. Conventional NIRS-based BCIs have generally used left- and right-hand motor imagery tasks to modulate discriminable hemodynamic responses on the motor cortex5–15. However, since there are typically many hairs on the scalp above the motor cortex which interfere with near-infrared light, it is necessary to brush aside the hairs from the measuring location before the experiment. To avoid this time-consuming preparation, measuring prefrontal cortex (PFC) activations may provide a better option because the forehead over PFC is a non-hair bearing area. More importantly, it has been well established that PFC plays an important role in processing cognitive tasks16. For example, Pfurtscheller et al.17 showed a concentration of oxy-hemoglobin (HbO) increase accompanied by a concentration of deoxy-hemoglobin (HbR) decrease over anterior PFC (APFC). So far, many NIRS-based BCI studies have showed promising results by using PFC activity18–20. Mental arithmetic (MA; e.g., successive subtraction of a small number from a large number) is known as one of the most robust tasks that reliably activate PFC areas18–26. Traditionally, the eyes-closed (EC) state has been considered idle or resting state. Under this condition, the alpha rhythm (8–12 Hz) of the electroencephalogram (EEG) is strongly pronounced, especially around the 1 Machine Learning Group, Berlin Institute of Technology (TU Berlin), Marchstr. 23, 10587 Berlin, Germany. Department of Brain and Cognitive Engineering, Korea University, 136-713 Seoul, Korea. 3Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, 730-701 Gumi, Korea. Correspondence and requests for materials should be addressed to K.R.M. (email: ) or H.-J.H. (email: ) 2 Scientific Reports | 6:36203 | DOI: 10.1038/srep36203 1 www.nature.com/scientificreports/ occipital, parietal and posterior temporal regions27. It has been found that EEG and blood oxygenation level dependent (BOLD) signals are highly correlated under EC resting state27. Increased alpha rhythm power was associated with decreased BOLD signal in occipital, superior temporal, inferior frontal, cingulate cortices and with increased BOLD signal in the thalamus and insula27. This result indicates that the EC state does not significantly influence PFC activation. Hence we assume that a NIRS-based BCI paradigm mainly employing the PFC regions could be also applicable in EC state. The development of EC BCI systems is fundamentally required for severely locked-in patients who are considered main users of BCI technology because they gradually lose their oculomotor functions in progressed states of the disease. Thus, they would have difficulties in using conventional BCI paradigms requiring normal or moderate visual functions for command selection and/or feedback. Two recent EEG-based BCI studies showed the feasibility of EC BCI paradigms28,29. One study used the steady-state visual evoked potential (SSVEP) paradigm28 and the other used the visual P300 paradigm29. Both studies showed acceptable classification results. Also, Gallegos-Ayala et al.30 recently reported the possibility of a NIRS-based EC BCI with a completely locked-in state (CLIS) patient but PFC was not used as the region of interest. In this study, we investigated the possibility of developing an EC BCI based on self-modulated NIRS signals by performing MA. The performance of a traditional NIRS-based BCI with eyes-open (EO) is also evaluated to verify the feasibility of the EC BCI. In the experiment, two different conditions were designed which are MA that is one of the most robust mental tasks for the control task and the imagination of the English alphabet for a baseline task (BL), respectively. We measured hemodynamic responses using a multi-channel NIRS system while subjects performed two mental tasks with EO and EC conditions, respectively, and observed the temporal characteristics of hemodynamic responses and spatial separability of the two mental tasks (MA vs BL). The performance of classifying the two mental tasks was estimated for each of the EO and EC paradigm using two conventional linear classifiers. A post-experiment questionnaire was performed to elucidate differences between the EO and EC paradigms with respect to comprehension, difficulty, discomfort, concentration and sleepiness. Materials and Methods Subjects. Eleven right-handed healthy subjects participated in this study (five males and seven females, average age: 26.3 ±  2.7 (mean ± standard deviation)). None of them reported neurological, psychiatric or other related diseases that might affect the outcomes of the current study. This study was approved by the Ethics Committee of the Institute of Psychology and Ergonomics, Berlin Institute of Technology (approval number: SH_01_20150330) and all experime (...truncated)