Ruisen Huang's research while affiliated with Pusan National University and other places

Publications (14)

Article
Full-text available
Functional near-infrared spectroscopy(fNIRS) is an emerging technique for the non-invasive brain-computer interface (BCD). Quickly obtaining precise brain signals is very crucial for successful BCIs. This paper investigates a real-time filtering technique to remove motion artifact (MA) and low-frequency drift in the fNIRS signals. Optical intensiti...
Article
Full-text available
Functional near-infrared spectroscopy (fNIRS) in brain imaging needs to be robust to subject-wise variability. The use of a fixed differential pathlength factor (DPF) per wavelength for the entire brain will degrade the accuracy of hemodynamic responses. Since the tissue composition varies within the brain, correct DPF values should be used for var...
Article
Full-text available
This study decodes consumers' preference levels using a convolutional neural network (CNN) in neuromarketing. The classification accuracy in neuromarketing is a critical factor in evaluating the intentions of the consumers. Functional near-infrared spectroscopy (fNIRS) is utilized as a neuroimaging modality to measure the cerebral hemodynamic respo...
Conference Paper
Full-text available
Mild cognitive impairment (MCI) is an intermediate stage leading to Alzheimer's disease (AD). Diagnosis for MCI patients at an early stage can reduce the chances of developing into a severe condition for cognition. This study aims to identify the healthy control (HC) and MCI through the neural images in the specific time points during the mental ta...
Conference Paper
Full-text available
One of the advantages of fNIRS systems is their portability for real-world applications. Therefore, the online rejection of the motion artifacts in functional near-infrared spectroscopy (fNIRS) signals is one of the essential research topics. We proposed a square-root cubature Kalman filter using a weighted moving average model to tract the dynamic...
Conference Paper
Full-text available
Objective: This study investigates a new hand exoskeleton control framework for stroke patients using a brain-computer interface (BCI) technique. Methods: Hand rehabilitation exoskeletons are required to improve critical features, including portability, low cost, safe human-robotic interaction, and intelligent control. The new hand exoskeleton work...

Citations

... 43 1.2. Classification of X-ray Images using CNN 44 A CNN is an effective tool for image classification, which has been used in various fields 45 such as health, economics, and agriculture [4][5][6][7][8]. Last year, various types of CNNs were 46 extensively used in COVID-19 detection in medical images. ...
... Using the subjects' self-assessments as ground-truth, a classification accuracy of 77% was reported. Another study collecting fNIRS signals from the prefrontal cortex, Qing et al. 87 used a CNN to determine the preference levels of subjects toward various Pepsi and Coca-Cola ads, achieving an average three-class classification accuracy of 87.9% for 30 s videos. 15 and 60 s videos showed similar accuracies of 84.3% and 86.4%, respectively. ...
... Functional near-infrared spectroscopy (fNIRS) is a new brain signal measurement device based on hemodynamic signals, such as functional MRI (fMRI). The fNIRS can be used in a variety of brain disorders diagnosis and classification method using deep learning methods [45][46][47][48]. Various techniques are utilized for classifying the tumors with MRI. ...
... Improved results were achieved compared with their previous study [67], but the limitations remained as before; there was a 2.3 s delay in the real-time system, which is inefficient. Recently, Kavichai et al. [84] were able to reduce the time delay in [68] by using the Shared Control Strategy (SCS) method. The SCS method employed environment information by using external sensors. ...