Hoseok Choi

A thin film polyimide mesh microelectrode for chronic epidural electrocorticography recording with enhanced contactability.

OBJECTIVE Epidural electrocorticography (ECoG) activity may be more reliable and stable than single-unit-activity or local field potential. Invasive brain computer interface (BCI) devices are limited by mechanical mismatching and cellular reactive responses due to differences in the elastic modulus and the motion of stiff electrodes. We propose a mesh-shaped electrode to enhance the contactability between surface of dura and electrode. APPROACH We designed a polyimide (PI) electrode with a mesh pattern for more conformal contact with a curved surface. We compared the contact capability of mesh PI electrodes with conventionally used sheet PI electrode. The electrical properties of the mesh PI electrode were evaluated for four weeks. We recorded the epidural ECoG (eECoG) activity on the surface of rhesus monkey brains while they performed a saccadic task for four months. MAIN RESULTS The mesh PI electrode showed good contact with the agarose brain surface, as evaluated by visual inspection and signal measurement. It was about 87% accurate in predicting the direction of saccade eye movement. SIGNIFICANCE Our results indicate that the mesh PI electrode was flexible and good contact on the curved surface and can record eECoG activity maintaining close contact to dura, which was proved by in vivo and in vitro test.

Sequential phosphorylation analysis using dye-tethered peptides and microfluidic isoelectric focusing electrophoresis

We report a simple method for analyzing sequential phosphorylation by protein kinases using fluorescent peptide substrates and microfluidic isoelectric focusing (μIEF) electrophoresis. When a dye-labeled peptide substrate was sequentially phosphorylated by two consecutive protein kinases (mitogen-activated protein kinase (MAPK) and glycogen synthase kinase 3 (GSK3)), its differently phosphorylated forms were easily separated and visualized by fluorescent focusing zones in the μIEF channel based on a change in the isoelectric point (pI) by phosphorylation. As a result, ratiometric and quantitative analysis of the fluorescent focusing regions shifted by phosphorylation enabled the analysis of phosphorylation efficiency and the relevant inhibition of protein kinases (MAPK and GSK3) with high simplicity and selectivity. Furthermore, the GSK3 activity in the cell lysates was elucidated by μIEF electrophoresis in combination with immunoprecipitation. Our results suggest that this method has great potential for analyzing the sequential phosphorylation of multiple protein kinases that are implicated in cellular signaling pathways.

Improved prediction of bimanual movements by a two-staged (effector-then-trajectory) decoder with epidural ECoG in nonhuman primates

OBJECTIVE In arm movement BCIs (brain-computer interfaces), unimanual research has been much more extensively studied than its bimanual counterpart. However, it is well known that the bimanual brain state is different from the unimanual one. Conventional methodology used in unimanual studies does not take the brain stage into consideration, and therefore appears to be insufficient for decoding bimanual movements. In this paper, we propose the use of a two-staged (effector-then-trajectory) decoder, which combines the classification of movement conditions and uses a hand trajectory predicting algorithm for unimanual and bimanual movements, for application in real-world BCIs. APPROACH Two micro-electrode patches (32 channels) were inserted over the dura mater of the left and right hemispheres of two rhesus monkeys, covering the motor related cortex for epidural electrocorticograph (ECoG). Six motion sensors (inertial measurement unit) were used to record the movement signals. The monkeys performed three types of arm movement tasks: left unimanual, right unimanual, bimanual. To decode these movements, we used a two-staged decoder, which combines the effector classifier for four states (left unimanual, right unimanual, bimanual movements, and stationary state) and movement predictor using regression. MAIN RESULTS Using this approach, we successfully decoded both arm positions using the proposed decoder. The results showed that decoding performance for bimanual movements were improved compared to the conventional method, which does not consider the effector, and the decoding performance was significant and stable over a period of four months. In addition, we also demonstrated the feasibility of epidural ECoG signals, which provided an adequate level of decoding accuracy. SIGNIFICANCE These results provide evidence that brain signals are different depending on the movement conditions or effectors. Thus, the two-staged method could be useful if BCIs are used to generalize for both unimanual and bimanual operations in human applications and in various neuro-prosthetics fields.

Decoding Saccadic Directions Using Epidural ECoG in Non-Human Primates

A brain-computer interface (BCI) can be used to restore some communication as an alternative interface for patients suffering from locked-in syndrome. However, most BCI systems are based on SSVEP, P300, or motor imagery, and a diversity of BCI protocols would be needed for various types of patients. In this paper, we trained the choice saccade (CS) task in 2 non-human primate monkeys and recorded the brain signal using an epidural electrocorticogram (eECoG) to predict eye movement direction. We successfully predicted the direction of the upcoming eye movement using a support vector machine (SVM) with the brain signals after the directional cue onset and before the saccade execution. The mean accuracies were 80% for 2 directions and 43% for 4 directions. We also quantified the spatial-spectro-temporal contribution ratio using SVM recursive feature elimination (RFE). The channels over the frontal eye field (FEF), supplementary eye field (SEF), and superior parietal lobule (SPL) area were dominantly used for classification. The α-band in the spectral domain and the time bins just after the directional cue onset and just before the saccadic execution were mainly useful for prediction. A saccade based BCI paradigm can be projected in the 2D space, and will hopefully provide an intuitive and convenient communication platform for users.

Rapid Detection of Glycogen Synthase Kinase-3 Activity in Mouse Sperm Using Fluorescent Gel Shift Electrophoresis

Assaying the glycogen synthase kinase-3 (GSK3) activity in sperm is of great importance because it is closely implicated in sperm motility and male infertility. While a number of studies on GSK3 activity have relied on labor-intensive immunoblotting to identify phosphorylated GSK3, here we report the simple and rapid detection of GSK3 activity in mouse sperm using conventional agarose gel electrophoresis and a fluorescent peptide substrate. When a dye-tethered and prephosphorylated (primed) peptide substrate for GSK3 was employed, a distinct mobility shift in the fluorescent bands on the agarose was observed by GSK3-induced phosphorylation of the primed peptides. The GSK3 activity in mouse testes and sperm were quantifiable by gel shift assay with low sample consumption and were significantly correlated with the expression levels of GSK3 and p-GSK3. We suggest that our assay can be used for reliable and rapid detection of GSK3 activity in cells and tissue extracts.

Meaning based covert speech classification for brain-computer interface based on electroencephalography

In this study, we investigated that whether covertly spoken words with different meaning are discriminable during electroencephalography (EEG) recording. Neural activities were recorded from 30 channel 10-20 system electrodes. By employing a paired T-test, we briefly identify the difference in spatio-spectro-temporal characteristics between two categories of meaning (number and face). EEG features were then classified by support vector machine. On average, 71.69% of the trials were correctly classified. After extract optimized features using support vector machine based recursive feature elimination, the accuracy was improved up to 92.46%. Our preliminary results shed light on the construction of meaning based speech brain-computer interface.

Long-term evaluation and feasibility study of the insulated screw electrode for ECoG recording

BACKGROUND A screw-shaped electrode can offer a compromise between signal quality and invasiveness. However, the standard screw electrode can be vulnerable to electrical noise while directly contact with the skull or skin, and the feasibility and stability for chronic implantation in primate have not been fully evaluated. NEW METHOD We designed a novel screw electrocorticogram (ECoG) electrode composed of three parts: recording electrode, insulator, and nut. The recording electrode was made of titanium with high biocompatibility and high electrical conductivity. Zirconia is used for insulator and nut to prevent electrical noise. RESULT In computer simulations, the screw ECoG with insulator showed a significantly higher performance in signal acquisition compared to the condition without insulator. In a non-human primate, using screw ECoG, clear visual-evoked potential (VEP) waveforms were obtained, VEP components were reliably maintained, and the electrodes impedance was stable during the whole evaluation period. Moreover, it showed higher SNR and wider frequency band compared to the electroencephalogram (EEG). We also observed the screw ECoG has a higher sensitivity that captures different responses on various stimuli than the EEG. COMPARISON The screw ECoG showed reliable electrical characteristic and biocompatibility for three months, that shows great promise for chronic implants. These results contrasted with previous reports that general screw electrode was only applicable for acute applications. CONCLUSION The suggested electrode can offer whole-brain monitoring with high signal quality and minimal invasiveness. The screw ECoG can be used to provide more in-depth understanding, not only relationship between functional networks and cognitive behavior, but also pathomechanisms in brain diseases.

Right Hemisphere Lateralization in Neural Connectivity Within Fronto-Parietal Networks in Non-human Primates During a Visual Reaching Task

A fronto-parietal network, comprised of the posterior parietal cortex (PPC) and the dorsal premotor cortex (PMd) has been proposed to be involved in planning and guiding movement. However, the issue of how the network is expressed across the bilateral cortical area according to the effectors side remains unclear. In this study, we tested these questions using electrocorticographic (ECoG) recordings in non-human primates and using a simple visual guided reaching task that induced a left or right hand response based on relevant cues provided for the task. The findings indicate that right hemisphere lateralized network patterns in which the right PMd was strongly coordinated with bilateral PPC immediately after presentation of the movement cue occurred, while the coherence with the left PMd was not enhanced. No difference was found in the coherence pattern between the effectors side (left hand or right hand), but the strength of coherence was different, in that animals showed a higher coherence in the right hand response compared to the left. Our data support that right lateralization in long-range phase synchrony in the 10–20 Hz low beta band is involved in motor preparation stage, irrespective of the upcoming effectors side.

Bimanual Arm Movements Decoding using Hybrid Method

In arm movement BCI (brain-computer interface), the unimanual research has been well. However, the bimanual brain state is known to be different from the unimanual one, so the conventional arm movement decoding method seems to be insufficient to decode bimanual movement. In this research, we suggested the hybrid method to improve the decoding accuracy for bimanual movement estimation. The method consists of two step; 1st step: the movement conditions classification, and 2nd step: the hand trajectory prediction algorithm. As a result, the hybrid method showed improved arm movement decoding performance and significant and stable decoding rate over several months for bimanual tasks. This technique could be applied to arm movement BCI in real world and the various neuro-prosthetics fields.

Analysis of antifreeze protein activity using colorimetric gold nanosensors

High activity and long stability of antifreeze proteins (AFPs), also known as ice-binding proteins (IBPs), are necessary for exerting their physiological functions in biotechnology and cryomedicine. Here we report a simple analysis of antifreeze protein activity and stability based on self-assembly of gold nanoparticles (AuNPs) via freezing and thawing cycles. While the mercaptosuccinic acid-capped AuNP (MSA-AuNP) was easily self-assembled after a freezing/thawing cycle, due to the mechanical attack of ice crystal on the MSA-AuNP surface, the presence of AFP impeded the self-assembly of MSA-AuNP via the interaction of AFP with ice crystals via freezing and thawing cycles, which led to a strong color in the MSA-AuNP solution. As a result, the aggregation parameter (E520/E650) of MSA-AuNP showed the rapid detection of both activity and stability of AFPs. We suggest that our newly developed method is very suitable for measuring antifreeze activity and stability in a simple and rapid manner with reliable quantification.