Jaeyoung Shin

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

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.

Channel Modeling for Indoor Broadband Power-Line Communications Networks With Arbitrary Topologies by Taking Adjacent Nodes Into Account

We develop a general channel model in broadband power-line communications (BPLC) network using infinite geometric series and the description matrices. The developed general channel model can handle arbitrary PLC channel links easily; thus, we can calculate accurate channel characteristics without increasing computing complexity. For considering the effect of the various branches of the network, we investigate the characteristics of the channel links with adjacent nodes composed of multiple branches. We conduct the simulations by varying the degree of distantness of the adjacent nodes and the number of branches at the nodes. Our simulation results suggest that we should consider adjacent nodes with a degree of distantness up to one for the arbitrary channel links; these were not considered in previous studies. This enables us to calculate accurate channel responses of the arbitrary channel links of indoor BPLC networks.

Multiclass classification of hemodynamic responses for performance improvement of functional near-infrared spectroscopy-based brain–computer interface

Abstract. We improved the performance of a functional near-infrared spectroscopy (fNIRS)-based brain–computer interface based on relatively short task duration and multiclass classification. A custom-built eight-channel fNIRS system was used over the motor cortex areas in both hemispheres to measure the hemodynamic responses evoked by four different motor tasks (overt execution of arm lifting and knee extension for both sides) instead of finger tapping. The hemodynamic responses were classified using the naive Bayes classifier. Among the mean, max, slope, variance, and median of the signal amplitude and the time lag of the signal, several signal features are chosen to obtain highest classification accuracy. Ten runs of threefold cross-validation were conducted, which yielded classification accuracies of 87.1%±2.4% to 95.5%±2.4%, 77.5%±1.9% to 92.4%±3.2%, and 73.8%±3.5% to 91.5%±1.4% for the binary, ternary, and quaternary classifications, respectively. Eight seconds of task duration for obtaining sufficient quaternary classification accuracy was suggested. The bit transfer rate per minute (BPM) based on the quaternary classification accuracy was investigated. A BPM can be achieved from 2.81 to 5.40  bits/min.

Open Access Dataset for EEG+NIRS Single-Trial Classification

We provide an open access dataset for hybrid brain–computer interfaces (BCIs) using electroencephalography (EEG) and near-infrared spectroscopy (NIRS). For this, we conducted two BCI experiments (left versus right hand motor imagery; mental arithmetic versus resting state). The dataset was validated using baseline signal analysis methods, with which classification performance was evaluated for each modality and a combination of both modalities. As already shown in previous literature, the capability of discriminating different mental states can be enhanced by using a hybrid approach, when comparing to single modality analyses. This makes the provided data highly suitable for hybrid BCI investigations. Since our open access dataset also comprises motion artifacts and physiological data, we expect that it can be used in a wide range of future validation approaches in multimodal BCI research.We provide an open access dataset for hybrid brain-computer interfaces (BCIs) using electroencephalography (EEG) and near-infrared spectroscopy (NIRS). For this, we con-ducted two BCI experiments (left vs. right hand motor imagery; mental arithmetic vs. resting state). The dataset was validated using baseline signal analysis methods, with which classification performance was evaluated for each modality and a combination of both modalities. As already shown in previous literature, the capability of discriminating different mental states can be en-hanced by using a hybrid approach, when comparing to single modality analyses. This makes the provided data highly suitable for hybrid BCI investigations. Since our open access dataset also comprises motion artifacts and physiological data, we expect that it can be used in a wide range of future validation approaches in multimodal BCI research.

Simultaneous Acquisition of EEG and NIRS during Cognitive Tasks for an Open Access Dataset

We provide an open access multimodal brain-imaging dataset of simultaneous electroencephalography (EEG) and near-infrared spectroscopy (NIRS) recordings. Twenty-six healthy participants performed three cognitive tasks: 1) n-back (0-, 2- and 3-back), 2) discrimination/selection response task (DSR) and 3) word generation (WG) tasks. The data provided includes: 1) measured data, 2) demographic data, and 3) basic analysis results. For n-back (dataset A) and DSR tasks (dataset B), event-related potential (ERP) analysis was performed, and spatiotemporal characteristics and classification results for ‘target’ versus ‘non-target’ (dataset A) and symbol ‘O’ versus symbol ‘X’ (dataset B) are provided. Time-frequency analysis was performed to show the EEG spectral power to differentiate the task-relevant activations. Spatiotemporal characteristics of hemodynamic responses are also shown. For the WG task (dataset C), the EEG spectral power and spatiotemporal characteristics of hemodynamic responses are analyzed, and the potential merit of hybrid EEG-NIRS BCIs was validated with respect to classification accuracy. We expect that the dataset provided will facilitate performance evaluation and comparison of many neuroimaging analysis techniques.

Performance enhancement of a brain-computer interface using high-density multi-distance NIRS

This study investigated the effectiveness of using a high-density multi-distance source-detector (SD) separations in near-infrared spectroscopy (NIRS), for enhancing the performance of a functional NIRS (fNIRS)-based brain-computer interface (BCI). The NIRS system that was used for the experiment was capable of measuring signals from four SD separations: 15, 21.2, 30, and 33.5 mm, and this allowed the measurement of hemodynamic response alterations at various depths. Fifteen participants were asked to perform mental arithmetic and word chain tasks, to induce task-related hemodynamic response variations, or they were asked to stay relaxed to acquire a baseline signal. To evaluate the degree of BCI performance enhancement by high-density channel configuration, the classification accuracy obtained using a typical low-density lattice SD arrangement, was compared to that obtained using the high-density SD arrangement, while maintaining the SD separation at 30 mm. The analysis results demonstrated that the use of a high-density channel configuration did not result in a noticeable enhancement of classification accuracy. However, the combination of hemodynamic variations, measured by two multi-distance SD separations, resulted in the significant enhancement of overall classification accuracy. The results of this study indicated that the use of high-density multi-distance SD separations can likely provide a new method for enhancing the performance of an fNIRS-BCI.

A Ternary Hybrid EEG-NIRS Brain-Computer Interface for the Classification of Brain Activation Patterns during Mental Arithmetic, Motor Imagery, and Idle State

The performance of a brain-computer interface (BCI) can be enhanced by simultaneously using two or more modalities to record brain activity, which is generally referred to as a hybrid BCI. To date, many BCI researchers have tried to implement a hybrid BCI system by combining electroencephalography (EEG) and functional near-infrared spectroscopy (NIRS) to improve the overall accuracy of binary classification. However, since hybrid EEG-NIRS BCI, which will be denoted by hBCI in this paper, has not been applied to ternary classification problems, paradigms and classification strategies appropriate for ternary classification using hBCI are not well investigated. Here we propose the use of an hBCI for the classification of three brain activation patterns elicited by mental arithmetic, motor imagery, and idle state, with the aim to elevate the information transfer rate (ITR) of hBCI by increasing the number of classes while minimizing the loss of accuracy. EEG electrodes were placed over the prefrontal cortex and the central cortex, and NIRS optodes were placed only on the forehead. The ternary classification problem was decomposed into three binary classification problems using the “one-versus-one” (OVO) classification strategy to apply the filter-bank common spatial patterns filter to EEG data. A 10 × 10-fold cross validation was performed using shrinkage linear discriminant analysis (sLDA) to evaluate the average classification accuracies for EEG-BCI, NIRS-BCI, and hBCI when the meta-classification method was adopted to enhance classification accuracy. The ternary classification accuracies for EEG-BCI, NIRS-BCI, and hBCI were 76.1 ± 12.8, 64.1 ± 9.7, and 82.2 ± 10.2%, respectively. The classification accuracy of the proposed hBCI was thus significantly higher than those of the other BCIs (p < 0.005). The average ITR for the proposed hBCI was calculated to be 4.70 ± 1.92 bits/minute, which was 34.3% higher than that reported for a previous binary hBCI study.

A study on information transfer rate by brain-computer interface (BCI) using functional near-infrared spectroscopy (fNIRS)

We develop an 8-channel time domain functional near-infrared spectroscopy (fNIRS) system and measure concentration changes of hemoglobin during left/right arm lifting. Correlation-based signal improvement (CBSI) method is used to remove the effect of the head movement. We investigate the performances of the information transfer rate as a function of classification accuracy estimated by support vector machine. We achieve the information transfer rate in the range of 0.28~2.08 bits/min

BIM-enabled Definition of a Path Object and its Properties to Evaluate Building Circulation using Numerical Data

In this paper, we seek to define the path object and its properties as an instance level of a circulation path between two spaces. We further suggest the practical application of path objects in design review issues using numeric data on building circulation (NDBC) as a complete collection of circulation paths from a given Building Information Modeling (BIM) model. As the use of BIM is increasingly being implemented in architecture, engineering, and construction industries, rich data related to building objects and useful digital representations have been developed for specific circulation review tasks. Among the several benefits observed from such applications, this paper focuses on the BIM-enabled formal definition of the path object and its properties. We further demonstrate the use of several analysis applications using the NDBC. Due to the rich spatial information defined in a BIM model, path objects can be instantiated dynamically, and a specific data model for building circulation paths can be defined with a series of numeric data sets. In this paper, the formal definition of a path object and its properties are explored, and one of its NDBC applications is clarified and demonstrated for further circulation analysis tasks using a software tool developed by us.

Rule Checking Method-Centered Approach to Represent Building Permit Requirements

This paper aims to describe rule checking method, classification and its demonstration. As applications of BIM extends, there have been some challenging projects on automated building compliance checking. The current rule-making method is developercentered and thus is difficult to define rules without propound programming knowledge. This paper introduces high level rule making methods with law sentence-centered approach. The proposed methods have intuitive naming convention and are directly mapped with the predicate of the law sentences. Therefore, it is easy to infer function of the methods. According to the type of object and property in instance level, three hierarchies of method classification were set: 1) level 1 divides types of instance, 2) level 2 classifies the type of property, and 3) level 3 specifies the content of checking. From the level 3, representative rule checking method is defined. The representative method is subdivided into extended methods according to the specific object and property to check. The rule checking methods are combined together to form an intermediate pseudocode. The pseudo-code is later to be parsed into computer executable form. This paper mainly focuses on 1) introducing law sentence -centered rule checking method, 2) object and property-based classification of rule checking method, 3) method extensibility and 4) demonstration of rule checking methods with actual requirement sentences from the Korea Building Permit. The high level rule checking method is developed as a part of KBimLogic. KBimLogic is a software that translates the Korea Building Permit requirement into computer executable format. KBimLogic is now under development with government funding.