Charitable crowdfunding donation-intention estimation depending on emotional project images using fNIRS-based functional connectivity.

Charitable fundraising increasingly relies on online crowdfunding platforms. Project images of charitable crowdfunding use emotional appeals to promote helping behavior. Negative emotions are commonly used to motivate helping behavior because the image of a happy child may not motivate donors to donate as willingly. However, some research has found that happy images can be more beneficial. These contradictory results suggest that the emotional valence of project imagery and how fundraisers frame project images effectively remain debatable. Thus, we compared and analyzed brain activation differences in the prefrontal cortex governing human emotions depending on donation decisions using functional near-infrared spectroscopy, a neuroimaging device. We advance existing theory on charitable behavior by demonstrating that little correlation exists in donation intentions and brain activity between negative and positive project images, which is consistent with survey results on donation intentions by victim image. We also discovered quantitative brain hemodynamic signal variations between donors and nondonors, which can predict and detect donor mental brain functioning using functional connectivity, that is, the statistical dependence between the time series of electrophysiological activity and oxygenated hemodynamic levels in the prefrontal cortex. These findings are critical in developing future marketing strategies for online charitable crowdfunding platforms, especially project images.

Inter-Brain Synchrony Pattern Investigation on Triadic Board Game Play-Based Social Interaction: An fNIRS Study.

Recent advances in functional neuroimaging techniques, including methodologies such as fNIRS, have enabled the evaluation of inter-brain synchrony (IBS) induced by interpersonal interactions. However, the social interactions assumed in existing dyadic hyperscanning studies do not sufficiently emulate polyadic social interactions in the real world. Therefore, we devised an experimental paradigm that incorporates the Korean folk board game "Yut-nori" to reproduce social interactions that emulate social activities in the real world. We recruited 72 participants aged 25.2 ± 3.9 years (mean ± standard deviation) and divided them into 24 triads to play Yut-nori, following the standard or modified rules. The participants either competed against an opponent (standard rule) or cooperated with an opponent (modified rule) to achieve a goal efficiently. Three different fNIRS devices were employed to record cortical hemodynamic activations in the prefrontal cortex both individually and simultaneously. Wavelet transform coherence (WTC) analyses were performed to assess prefrontal IBS within a frequency range of 0.05-0.2 Hz. Consequently, we observed that cooperative interactions increased prefrontal IBS across overall frequency bands of interest. In addition, we also found that different purposes for cooperation generated different spectral characteristics of IBS depending on the frequency bands. Moreover, IBS in the frontopolar cortex (FPC) reflected the influence of verbal interactions. The findings of our study suggest that future hyperscanning studies should consider polyadic social interactions to reveal the properties of IBS in real-world interactions.

Forecasting Unplanned Purchase Behavior under Buy-One Get-One-Free Promotions Using Functional Near-Infrared Spectroscopy.

It is very important for consumers to recognize their wrong shopping habits such as unplanned purchase behavior (UPB). The traditional methods used for measuring the UPB in qualitative and quantitative studies have some drawbacks because of human perception and memory. We proposed a UPB identification methodology applied with the brain-computer interface technique using a support vector machine (SVM) along with a functional near-infrared spectroscopy (fNIRS). Hemodynamic signals and behavioral data were collected from 33 subjects by performing Task 1 which included the Buy-One-Get-One-Free (BOGOF) and Task 2 which excluded the BOGOF condition. The acquired data were calculated with 6 time-domain features and then classified them using SVM with 10-cross validations. Thereafter, we evaluated whether the results were reliable using the area under the receiver operating characteristic curve (AUC). As a result, we achieved average accuracy greater than 94%, which is reliable because of the AUC values above 0.97. We found that the UPB brain activity was more relevant to Task 1 with the BOGOF condition than with Task 2 in the prefrontal cortex. UPBs were sufficiently derived from self-reported measurement, indicating that the subjects perceived increased impulsivity in the BOGOF condition. Therefore, this study improves the detection and understanding of UPB as a path for a computer-aided detection perspective for rating the severity of UPBs.

Brain-computer interface to predict impulse buying behavior using functional near-infrared spectroscopy.

As the rate of vaccination against COVID-19 is increasing, demand for overseas travel is also increasing. Despite people's preference for duty-free shopping, previous studies reported that duty-free shopping increases impulse buying behavior. There are also self-reported tools to measure their impulse buying behavior, but it has the disadvantage of relying on the human memory and perception. Therefore, we propose a Brain-Computer Interface (BCI)-based brain signal processing methodology to supplement these limitations and to reduce ambiguity and conjecture of data. To achieve this goal, we focused on the brain's prefrontal cortex (PFC) activity, which supervises human decision-making and is closely related to impulse buying behavior. The PFC activation is observed by recording signals using a functional near-infrared spectroscopy (fNIRS) while inducing impulse buying behavior in virtual computing environments. We found that impulse buying behaviors were not only higher in online duty-free shops than in online regular stores, but the fNIRS signals were also different on the two sites. We also achieved an average accuracy of 93.78% in detecting impulse buying patterns using a support vector machine. These results were identical to the people's self-reported responses. This study provides evidence as a potential biomarker for detecting impulse buying behavior with fNIRS.

Subdividing Stress Groups into Eustress and Distress Groups Using Laterality Index Calculated from Brain Hemodynamic Response.

A stress group should be subdivided into eustress (low-stress) and distress (high-stress) groups to better evaluate personal cognitive abilities and mental/physical health. However, it is challenging because of the inconsistent pattern in brain activation. We aimed to ascertain the necessity of subdividing the stress groups. The stress group was screened by salivary alpha-amylase (sAA) and then, the brain's hemodynamic reactions were measured by functional near-infrared spectroscopy (fNIRS) based on the near-infrared biosensor. We compared the two stress subgroups categorized by sAA using a newly designed emotional stimulus-response paradigm with an international affective picture system (IAPS) to enhance hemodynamic signals induced by the target effect. We calculated the laterality index for stress (LIS) from the measured signals to identify the dominantly activated cortex in both the subgroups. Both the stress groups exhibited brain activity in the right frontal cortex. Specifically, the eustress group exhibited the largest brain activity, whereas the distress group exhibited recessive brain activity, regardless of positive or negative stimuli. LIS values were larger in the order of the eustress, control, and distress groups; this indicates that the stress group can be divided into eustress and distress groups. We built a foundation for subdividing stress groups into eustress and distress groups using fNIRS.

Inter-Brain Synchrony Levels According to Task Execution Modes and Difficulty Levels: An fNIRS/GSR Study.

Hyperscanning is a brain imaging technique that measures brain synchrony caused by social interactions. Recent research on hyperscanning has revealed substantial inter-brain synchrony (IBS), but little is known about the link between IBS and mental workload. To study this link, we conducted an experiment consisting of button-pressing tasks of three different difficulty levels for the cooperation and competition modes with 56 participants aged 23.7± 3.8 years (mean±standard deviation). We attempted to observe IBS using functional near-infrared spectroscopy (fNIRS) and galvanic skin response (GSR) to assess the activities of the human autonomic nervous system. We found that the IBS levels increased in a frequency band of 0.075-0.15 Hz, which was unrelated to the task repetition frequency in the cooperation mode according to the task difficulty level. Significant relative inter-brain synchrony (RIBS) increases were observed in three and 10 channels out of 15 for the hard tasks compared to the normal and easy tasks, respectively. We observed that the average GSR values increased with increasing task difficulty levels for the competition mode only. Thus, our results suggest that the IBS revealed by fNIRS and GSR is not related to the hemodynamic changes induced by mental workload, simple behavioral synchrony such as button-pressing timing, or autonomic nervous system activity. IBS is thus explicitly caused by social interactions such as cooperation.

Effect of Oxygen Vacancy on the Conduction Modulation Linearity and Classification Accuracy of Pr0.7Ca0.3MnO3 Memristor.

An amorphous Pr0.7Ca0.3MnO3 (PCMO) film was grown on a TiN/SiO2/Si (TiN-Si) substrate at 300 °C and at an oxygen pressure (OP) of 100 mTorr. This PCMO memristor showed typical bipolar switching characteristics, which were attributed to the generation and disruption of oxygen vacancy (OV) filaments. Fabrication of the PCMO memristor at a high OP resulted in nonlinear conduction modulation with the application of equivalent pulses. However, the memristor fabricated at a low OP of 100 mTorr exhibited linear conduction modulation. The linearity of this memristor improved because the growth and disruption of the OV filaments were mostly determined by the redox reaction of OV owing to the presence of numerous OVs in this PCMO film. Furthermore, simulation using a convolutional neural network revealed that this PCMO memristor has enhanced classification performance owing to its linear conduction modulation. This memristor also exhibited several biological synaptic characteristics, indicating that an amorphous PCMO thin film fabricated at a low OP would be a suitable candidate for artificial synapses.

Effects of Transcranial Direct Current Stimulation Over the Dorsolateral Prefrontal Cortex (PFC) on Cognitive-Motor Dual Control Skills.

This randomized crossover study investigated whether anodal transcranial direct current stimulation (tDCS) over the dorsolateral prefontal cortex (dlPFC) modulates memory-guided finger isometric maintenance during single motor and dual cognitive-motor tasks, based on electroencephalogram (EEG) signals. Twenty-three healthy participants (14 female; M age = 29.130 years, SD = 10.918) underwent both sham and 2-mA stimulation sessions over the dlPFC for 20 minutes, with a minimum washout period of seven days. We analyzed finger-force isometric maintenance and event-related spectral perturbation (ERSP) of the EEG during early and later phases of both tasks. We observed a significant motor accuracy improvement (p = .014) and significant variation of force output (p = .027) with significant decrease in ERSP on the dorsomedial prefrontal cortex (dmPFC) (early phase, p = .027; later phase, p = .023) only after 2 mA stimulation. Thus, anodal tDCS over the dlPFC may improve memory-guided force control during cognitive-motor dual tasks.

Improvement in Recovery of Hemodynamic Responses by Extended Kalman Filter With Non-Linear State-Space Model and Short Separation Measurement.

OBJECTIVE: The purpose of this study is to describe the noise reduction in the hemodynamic responses, obtained by functional near-infrared spectroscopy (fNIRS), using the proposed extended Kalman filter (EKF) with a non-linear state-space model, aided by the short separation (SS) measurement. METHODS: The authors used the simulated data by adding the synthetic hemodynamic response function (HRF) to the multi-distance four-channel fNIRS signals obtained during the resting state. EKF was used to estimate the non-linear state-space model designed based on the Balloon model. The SS channel was used as a regressor that is sensitive only to superficial noises. The whole segments were grouped by the existence of motion artifacts (MAs) to investigate the improvement by EKF compared to the linear Kalman filter (LKF) and adaptive filter (AF) in extracting neural-evoked hemodynamic. RESULTS: Kalman-based approaches were better than AF in reducing noises. Using EKF, the averages of the decreased errors and increased correlation between the recovered and true HRF were 34% in oxy-hemoglobin and 62% in deoxy-hemoglobin concentrations in segments having MAs, compared with LKF. In the MA-free condition, EKF is more robust to the poor quality of signals in noise reduction than LKF. CONCLUSION: The proposed non-linear Kalman approach is better in noise reduction than AF and LKF especially in noisy deoxy-hemoglobin concentrations, and less affected by the conditions of measurements and contaminations by MAs. SIGNIFICANCE: The proposed method can be used for reducing superficial noises and MAs from fNIRS signals as an upgraded alternative to existing AFs.

Process-specific analysis in episodic memory retrieval using fast optical signals and hemodynamic signals in the right prefrontal cortex.

OBJECTIVE: Memory is formed by the interaction of various brain functions at the item and task level. Revealing individual and combined effects of item- and task-related processes on retrieving episodic memory is an unsolved problem because of limitations in existing neuroimaging techniques. To investigate these issues, we analyze fast and slow optical signals measured from a custom-built continuous wave functional near-infrared spectroscopy (CW-fNIRS) system. APPROACH: In our work, we visually encode the words to the subjects and let them recall the words after a short rest. The hemodynamic responses evoked by the episodic memory are compared with those evoked by the semantic memory in retrieval blocks. In the fast optical signal, we compare the effects of old and new items (previously seen and not seen) to investigate the item-related process in episodic memory. The Kalman filter is simultaneously applied to slow and fast optical signals in different time windows. MAIN RESULTS: A significant task-related HbR decrease was observed in the episodic memory retrieval blocks. Mean amplitude and peak latency of a fast optical signal are dependent upon item types and reaction time, respectively. Moreover, task-related hemodynamic and item-related fast optical responses are correlated in the right prefrontal cortex. SIGNIFICANCE: We demonstrate that episodic memory is retrieved from the right frontal area by a functional connectivity between the maintained mental state through retrieval and item-related transient activity. To the best of our knowledge, this demonstration of functional NIRS research is the first to examine the relationship between item- and task-related memory processes in the prefrontal area using single modality.

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 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.

Improvement of the chromatic dispersion tolerance in coherent optical OFDM systems using shifted DFT windows for ultra-long-haul optical transmission systems.

In a high-capacity ultra-long-haul optical coherent orthogonal frequency-division multiplexing (CO-OFDM) system, the dispersion tolerance is determined by the length of cyclic extension (CE). In this paper, we propose a novel scheme to substantially improve the dispersion tolerance of CO-OFDM systems without increasing the CE length. Multiple time-shifted discrete Fourier transform (DFT) windows are exploited at the receiver, each demodulating only a part of the subcarriers. Effectively, the proposed scheme reduces the bandwidth of the OFDM signals under demodulation. Numerical simulations are performed to show the improved dispersion tolerance of the proposed scheme in comparison with the conventional CO-OFDM system. We show that the dispersion tolerance improves by a factor equal to the number of DFT windows. The tradeoff between the improved dispersion tolerance and increased receiver complexity is also presented.

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

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.

10 Gbps WDM transmission performance limits using in-line SOAs and an optical phase conjugator based on four-wave mixing in SOAs as a mid-span spectral inversion technique.

We have theoretically investigated the transmission performance limits of all semiconductor optical amplifiers (SOA)-based 10 Gb/s wavelength division multiplexing (WDM) systems using in-line SOAs and an optical phase conjugator (OPC) based on four-wave mixing in SOAs as a mid-span spectral inversion technique. With a verified numerical model of SOAs, we have found that the crosstalk from SOAs in OPC is a dominant factor to limit the number of channels in WDM systems. In order to increase the available number of channels, we optimize the input optical power and the injection current to SOAs in OPC with using a reservoir channel in in-line SOAs. All SOA-based 10 Gb/s WDM systems using the OPC can transmit 16 channel signals up to 240 km distance with a 3 dB power penalty.

Improvement of dispersion tolerance for electrical-binary-signal-based duobinary transmitters.

We present a way to improve the dispersion tolerance of an electrical-binary-signal-based duobinary transmitter, implemented by using a dual-arm Mach-Zehnder modulator driven with two complementary binary signals. Successful transmission over 200 km of single- mode fiber is achieved by optimizing the relative time delay between the binary signals and the driving voltage.