Hanyang University · Neuroscience
Professor Chang-Hwan Im's research lab specializes in biomedical engineering and non-invasive neuromodulation, with a primary focus on transcranial direct current stimulation (tDCS) and brain-computer interface (BCI) technologies. The lab develops advanced computational modeling and optimization techniques—such as genetic algorithms, evolution strategies, and finite element methods—to enhance the precision and safety of tDCS by optimizing electrode placement and electric field distribution. A key research direction involves hybrid BCI systems that integrate electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to improve classification accuracy in both binary and ternary tasks. The lab also investigates patient-specific modeling for personalized neuromodulation and aims to design compact, practical hBCI systems for real-world applications.
Figures are computed from collected data and may differ slightly.
This paper proposes a hybrid genetic algorithm (GA) for electromagnetic topology optimization. A two-dimensional (2-D) encoding technique, which considers the geometrical topology, is first applied to electromagnetics. Then, a 2-D geographic crossover is used as the crossover operator. A novel local optimization algorithm, called the on/off sensitivity method, hybridized with the 2-D encoded GA, improves the convergence characteristics. The algorithm was verified by applying it to various case s
In this study, local electric field distributions generated by transcranial direct current stimulation (tDCS) with an extracephalic reference electrode were evaluated to address extracephalic tDCS safety issues. To this aim, we generated a numerical model of an adult male human upper body and applied the 3D finite element method to electric current conduction analysis. In our simulations, the active electrode was placed over the left primary motor cortex (M1) and the reference electrode was plac
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
The present study introduces a new approach to determining optimal electrode positions in transcranial direct current stimulation (tDCS). Electric field and 3D conduction current density were analyzed using 3D finite element method (FEM) formulated for a dc conduction problem. The electrode positions for minimal current injection were optimized by changing the Cartesian coordinate system into the spherical coordinate system and applying the (2+6) evolution strategy (ES) algorithm. Preliminary si
It has been demonstrated that the performance of typical unimodal brain-computer interfaces (BCIs) can be noticeably improved by combining two different BCI modalities. This so-called "hybrid BCI" technology has been studied for decades; however, hybrid BCIs that particularly combine electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) (hereafter referred to as hBCIs) have not been widely used in practical settings. One of the main reasons why hBCI systems are so unpopu
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