곽영철 교수

Non-invasive brain-computer interfaces (BCIs) have been widely used for neural decoding, linking neural signals to control devices. Hybrid BCI systems using electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have received significant attention for overcoming the limitations of EEG- and fNIRS-standalone BCI systems. However, most hybrid EEG-fNIRS BCI studies have focused on late fusion because of discrepancies in their temporal resolutions and recording locations. Desp

Speckle noise is inherent to synthetic aperture radar (SAR) images and degrades the target recognition performance. Deep learning based on convolutional neural networks (CNNs) has been widely applied for SAR target recognition, but the extracted features are still sensitive to speckle noise. In addition, speckle noise has been seldom considered in such CNN-based approaches. In this letter, we propose a speckle-noise-invariant CNN that employs regularization for minimizing feature variations caus

Mental workload is defined as the proportion of the information processing capability used to perform a task. High cognitive load requires additional resources to process information; this demand for additional resources may reduce the processing efficiency and performance. Therefore, the technique of workload estimation can ensure a proper working environment to promote the working efficiency of each person. In this paper, we propose a three-dimensional convolutional neural network (3D CNN) emp

Electroencephalography (EEG)-based brain-computer interface (BCI) systems have been extensively used in various applications, such as communication, control, and rehabilitation. However, individual anatomical and physiological differences cause subject-specific variability of EEG signals for the same task, and BCI systems thus require a calibration procedure that adjusts system parameters to each subject. To overcome this problem, we propose a subject-invariant deep neural network (DNN) using ba

The brain-computer interface (BCI) system provides information exchanges between neural signals containing the user's intention and device control signals. In this paper, we propose a graph neural network (GNN) with a multilevel feature fusion structure for high-performance BCI systems. Since the proposed structure can exploit both local and global neural information, the decoding accuracy greatly increases. Experimental results show that the original GNN outperforms conventional algorithms. Fur

Individuals have different working memory performance and some studies investigated a relationship between working memory performance and electroencephalography (EEG) band power. In this paper, we study EEG features to classify low performance group and high performance group and find that the power ratio feature of alpha and beta is more separable than their absolute powers. We test a deep artificial neural network (ANN) using the power ratio feature to classify the low performance group and hi

Mental workload is defined as the proportion of the information processing capability used to perform a task. High cognitive load requires additional resources that may reduce the processing efficiency and performance. Therefore, the technique of workload estimation can ensure a proper working environment to promote the working efficiency of each person. In this paper, we propose a three-dimensional convolutional neural network (3D CNN) based a multilevel feature fusion algorithm for mental work

The brain-computer interface (BCI) system provides information exchanges between neural signals containing the user's intention and device control signals. Electroencephalogram (EEG) is a widely used signal for obtaining neural signals. In EEG decoding, EEG variability across different subjects critically degrades deep learning performance. In this paper, we propose a feature normalization method for reducing EEG variability with rest state EEG signals. The decoding structure is trained with a n