Chang-Hwan Im

Development of an SSVEP-based BCI spelling system adopting a QWERTY-style LED keyboard.

In this study, we introduce a new mental spelling system based on steady-state visual evoked potential (SSVEP), adopting a QWERTY style layout keyboard with 30 LEDs flickering with different frequencies. The proposed electroencephalography (EEG)-based mental spelling system allows the users to spell one target character per each target selection, without the need for multiple step selections adopted by conventional SSVEP-based mental spelling systems. Through preliminary offline experiments and online experiments, we confirmed that human SSVEPs elicited by visual flickering stimuli with a frequency resolution of 0.1 Hz could be classified with classification accuracy high enough to be used for a practical brain-computer interface (BCI) system. During the preliminary offline experiments performed with five participants, we optimized various factors influencing the performance of the mental spelling system, such as distances between adjacent keys, light source arrangements, stimulating frequencies, recording electrodes, and visual angles. Additional online experiments were conducted with six participants to verify the feasibility of the optimized mental spelling system. The results of the online experiments were an average typing speed of 9.39 letters per minute (LPM) with an average success rate of 87.58%, corresponding to an average information transfer rate of 40.72 bits per minute, demonstrating the high performance of the developed mental spelling system. Indeed, the average typing speed of 9.39 LPM attained in this study was one of the best LPM results among those reported in previous BCI literatures.

Multimodal function optimization based on particle swarm optimization

In this paper, a new algorithm for the multimodal function optimization is proposed, based on the particle swarm optimization (PSO). A new method, named the multigrouped particle swarm optimization (MGPSO), keeps basic concepts of the PSO, and, thus, shows a more straightforward convergence compared to conventional hybrid type approaches. Moreover, the MGPSO has a unique advantage in that one can search N superior peaks of a multimodal function when the number of groups is N. The usefulness of the proposed algorithm was verified by the application to various case studies, including a practical electromagnetic optimization problem

Hybrid genetic algorithm for electromagnetic topology optimization

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 studies, and the results are presented herein.

Magnetic field analysis of 2-D permanent magnet array for planar motor

A new permanent magnet array for planar motor is proposed. The flux density distribution for the array is solved analytically by using the scalar magnetic potential equation. It is verified that the performance of the new magnet array is superior to the existing magnet arrays presented in patents.

Classification of selective attention to auditory stimuli: Toward vision-free brain-computer interfacing

Brain-computer interface (BCI) is a developing, novel mode of communication for individuals with severe motor impairments or those who have no other options for communication aside from their brain signals. However, the majority of current BCI systems are based on visual stimuli or visual feedback, which may not be applicable for severe locked-in patients that have lost their eyesight or the ability to control their eye movements. In the present study, we investigated the feasibility of using auditory steady-state responses (ASSRs), elicited by selective attention to a specific sound source, as an electroencephalography (EEG)-based BCI paradigm. In our experiment, two pure tone burst trains with different beat frequencies (37 and 43 Hz) were generated simultaneously from two speakers located at different positions (left and right). Six participants were instructed to close their eyes and concentrate their attention on either auditory stimulus according to the instructions provided randomly through the speakers during the inter-stimulus interval. EEG signals were recorded at multiple electrodes mounted over the temporal, occipital, and parietal cortices. We then extracted feature vectors by combining spectral power densities evaluated at the two beat frequencies. Our experimental results showed high classification accuracies (64.67%, 30 commands/min, information transfer rate (ITR) = 1.89 bits/min; 74.00%, 12 commands/min, ITR = 2.08 bits/min; 82.00%, 6 commands/min, ITR = 1.92 bits/min; 84.33%, 3 commands/min, ITR = 1.12 bits/min; without any artifact rejection, inter-trial interval = 6s), enough to be used for a binary decision. Based on the suggested paradigm, we implemented a first online ASSR-based BCI system that demonstrated the possibility of materializing a totally vision-free BCI system.

Determination of optimal electrode positions for transcranial direct current stimulation (tDCS)

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 simulation studies applied to a standard three-layer head model demonstrated that the proposed approach is promising in enhancing the performance of tDCS.

Inconsistent outcomes of transcranial direct current stimulation may originate from anatomical differences among individuals: Electric field simulation using individual MRI data

Transcranial direct current stimulation (tDCS) is a neuromodulation protocol that can facilitate or inhibit cortical excitability in particular areas of the brain. Although recent studies have reported that tDCS can successfully modulate the excitability of various brain sites, outcomes of tDCS were not consistent between subjects even when identical stimulation protocols were applied. Thus far, however, no studies have clearly verified the main cause of this individual variability. In this study, the main hypothesis was that individual variability in tDCS effects might be partly explained by anatomical differences among subjects. To verify our hypothesis, we investigated the relationship between the behavioral outcomes of a verbal working memory (WM) task and current density values at the dorsolateral prefrontal cortex (DLPFC) simulated using the finite element method (FEM). A 3-back verbal working memory task experiment was conducted in 17 healthy subjects before and after tDCS with cathode and anode electrodes located at the right supraorbital and F3 locations, respectively. The results showed that participants who showed evidence of enhanced WM task performance after tDCS had a significantly larger current density at the DLPFC than other participants, suggesting that inconsistent behavioral outcomes of tDCS might be partly due to individual anatomical differences.

Evaluation of local electric fields generated by transcranial direct current stimulation with an extracephalic reference electrode based on realistic 3D body modeling

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 placed at six different locations: over the right temporal lobe, on the right supraorbital region, on the right deltoid, on the left deltoid, under the chin, and on the right buccinator muscle. The maximum current density and electric field intensity values in the brainstem generated by the extracephalic reference electrodes were comparable to, or even less than, those generated by the cephalic reference electrodes. These results suggest that extracephalic reference electrodes do not lead to unwanted modulation of the brainstem cardio-respiratory and autonomic centers, as indicated by recent experimental studies. The volume energy density was concentrated at the neck area by the use of deltoid reference electrodes, but was still smaller than that around the active electrode locations. In addition, the distributions of elicited cortical electric fields demonstrated that the use of extracephalic reference electrodes might allow for the robust prediction of cortical modulations with little dependence on the reference electrode locations.

A novel algorithm for multimodal function optimization based on evolution strategy

In this paper, a novel algorithm for multimodal function optimization is proposed, based on the concept of evolution strategy. A new concept, named restricted evolution, shows more improved characteristics than conventional approaches that have been used for multimodal function optimization. The efficiency and usefulness of the proposed method will be verified by the application to various cases including practical optimization problems.

An Improved Particle Swarm Optimization Algorithm Mimicking Territorial Dispute Between Groups for Multimodal Function Optimization Problems

In the present paper, an improved particle swarm optimization (PSO) algorithm for multimodal function optimization is proposed. The new algorithm, named auto-tuning multigrouped PSO (AT-MGPSO) algorithm mimics natural phenomena in ecosystem such as territorial dispute between different group members and immigration of weak groups, resulting in automatic determination of the size of each groups territory and robust convergence. The usefulness of the proposed algorithm is verified by the application to a specially designed test function and a practical electromagnetic optimization problem.