Papers by Byoung-Kyong Min
Journal of neuroengineering and rehabilitation, May 30, 2024
Background Transcranial alternating current stimulation (tACS) is a prominent non-invasive brain ... more Background Transcranial alternating current stimulation (tACS) is a prominent non-invasive brain stimulation method for modulating neural oscillations and enhancing human cognitive function. This study aimed to investigate the effects of individualized theta tACS delivered in-phase and out-of-phase between the dorsal anterior cingulate cortex (dACC) and left dorsolateral prefrontal cortex (lDLPFC) during inhibitory control performance. Methods The participants engaged in a Stroop task with phase-lagged theta tACS over individually optimized highdensity electrode montages targeting the dACC and lDLPFC. We analyzed task performance, event-related potentials, and prestimulus electroencephalographic theta and alpha power. Results We observed significantly reduced reaction times following out-of-phase tACS, accompanied by reduced frontocentral N1 and N2 amplitudes, enhanced parieto-occipital P1 amplitudes, and pronounced frontocentral late sustained potentials. Out-of-phase stimulation also resulted in significantly higher prestimulus frontocentral theta and alpha activity. Conclusions These findings suggest that out-of-phase theta tACS potently modulates top-down inhibitory control, supporting the feasibility of phase-lagged tACS to enhance inhibitory control performance.
Anesthesiology, Jan 8, 2024
Mental workload is defined as the proportion of the information processing capability used to per... more 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 workload estimation using electroencephalogram (EEG) signals. Raw EEG signals are converted to 3D EEG images and then multilevel features are extracted in each layer of the 3D convolution operation and each multilevel feature is multiplied by a weighting factor, which determines the importance of the feature. The accuracy of our network is 90.3%, which is better than conventional algorithms.
NeuroImage, Oct 1, 2020
Whether thalamocortical interactions play a decisive role in conscious perception remains an open... more Whether thalamocortical interactions play a decisive role in conscious perception remains an open question. We presented rapid red/green color flickering stimuli, which induced the mental perception of either an illusory orange color or non-fused red and green colors. Using magnetoencephalography, we observed 6-Hz thalamic activity associated with thalamocortical inhibitory coupling only during the conscious perception of the illusory orange color. This sustained thalamic disinhibition was temporally coupled with higher visual cortical activation during the conscious perception of the orange color, providing neurophysiological evidence of the role of thalamocortical synchronization in conscious awareness of mental representation. Bayesian model comparison consistently supported the thalamocortical model in conscious perception. Taken together, experimental and theoretical evidence established the thalamocortical inhibitory network as a gateway to conscious mental representations.
Trends in Biotechnology, Sep 1, 2020
Most of the studies employing neuroimaging have focused on cortical and subcortical signals indiv... more Most of the studies employing neuroimaging have focused on cortical and subcortical signals individually to obtain neurophysiological signatures of cognitive functions. However, understanding the dynamic communication between the cortex and subcortical structures is essential for unraveling the neural correlates of cognition. In this quest, magnetoencephalography (MEG) and electroencephalography (EEG) are the methods of choice because they are non-invasive electrophysiological recording techniques with high temporal resolution. Sophisticated MEG/EEG source estimation techniques and network analysis methods developed recently can provide a more comprehensive understanding of the neurophysiological mechanisms of fundamental cognitive processes. Used together with noninvasive modulation of cortical-subcortical communication, these approaches may open up new possibilities for expanding the repertoire of non-invasive cognitive neurotechnology.
IEEE Access, 2020
Mental workload is defined as the proportion of the information processing capability used to per... more 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) employing a multilevel feature fusion algorithm for mental workload estimation using electroencephalogram (EEG) signals. The 1D EEG signals are converted to 3D EEG images to enable the 3D CNN to learn the spectral and spatial information over the scalp. The multilevel feature fusion framework integrates local and global neuronal activities by workload tasks in the 3D CNN algorithm. Multilevel features are extracted in each layer of the 3D convolution operation and each multilevel feature is multiplied by a weighting factor, which determines the importance of the feature. The weighting factor is adaptively estimated for each EEG image by a backpropagation process. Furthermore, we generate subframes from each EEG image and propose a temporal attention technique based on the long short-term memory model (LSTM) to extract a significant subframe at each multilevel feature that is strongly correlated with task difficulty. To verify the performance of our network, we performed the Sternberg task to measure the mental workload of the participant, which was classified according to its difficulty as low or high workload condition. We showed that the difficulty of the workload was well designed, which was reflected in the behavior of the participant. Our network is trained on this dataset and the accuracy of our network is 90.8 %, which is better than that of conventional algorithms. We also evaluated our method using the public EEG dataset and achieved 93.9 % accuracy. INDEX TERMS Convolutional neural network, electroencephalogram (EEG), feature fusion, mental workload, working memory.
NeuroImage, Nov 1, 2010
People often fail to notice a large change in the visual scene when the change occurs during a br... more People often fail to notice a large change in the visual scene when the change occurs during a brief interruption of the viewing. Since the change is well above perceptual threshold in continuous viewing, the failure (termed change blindness) has been attributed to abnormal visual short-term memory (VSTM). However, it is still unclear where the abnormality lies among the phases in VSTM, namely, encoding, maintenance, and retrieval-comparison. EEG oscillations, especially the gamma activity, have been suggested as neural signatures of VSTM, but have not been examined in the context of change blindness. Thus, we asked in the present study whether change detection or failure is correlated with EEG oscillatory activities and, if so, whether the timing and the spatial distribution of the oscillations could pin-point the abnormal phase of VSTM in change blindness. While on EEG recording, subjects watched morphed pictures of human faces in trials which consisted of a 200-ms initial image display, a 500-ms blank period, and a 200-ms comparison image display. The two images were either the same or clearly different above threshold. Trials with different images were classified as hit or missed, based on subjects' responses, and EEG data were compared between the two types of trials. Enhanced gamma activity was observed in the right temporal-parietal region during all periods in the hit trials compared to the missed ones. Frontal theta activity was increased during initial image encoding, whereas beta activity was decreased during maintenance and retrieval-comparison in the hit trials. These results point to weak encoding of initial images as the culprit for a later failure in change detection, while abnormal processing in subsequent phases of VSTM may result from the weak encoding and also contribute to change blindness.
Ultrasound in Medicine and Biology, 2015
Transcranial magnetic resonance imaging-guided high-intensity focused ultrasound (MRgHIFU) is gai... more Transcranial magnetic resonance imaging-guided high-intensity focused ultrasound (MRgHIFU) is gaining attention as a potent substitute for surgical intervention in the treatment of neurologic disorders. To discern the neurophysiologic correlates of its therapeutic effects, we applied MRgHIFU to an intractable neurologic disorder, essential tremor, while measuring magnetoencephalogram mu rhythms from the motor cortex. Focused ultrasound sonication destroyed tissues by focusing a high-energy beam on the ventralis intermedius nucleus of the thalamus. The post-treatment effectiveness was also evaluated using the clinical rating scale for tremors. Thalamic MRgHIFU had substantial therapeutic effects on patients, based on MRgHIFU-mediated improvements in movement control and significant changes in brain mu rhythms. Ultrasonic thalamotomy may reduce hyper-excitable activity in the motor cortex, resulting in normalized behavioral activity after sonication treatment. Thus, non-invasive and spatially accurate MRgHIFU technology can serve as a potent therapeutic tool with broad clinical applications.
Brain machine interfaces (BMIs) enable us to control external devices using our brain signals. Us... more Brain machine interfaces (BMIs) enable us to control external devices using our brain signals. Using a grid-shaped flickering line-array and a shrink-rLDA classifier, top-down information could recently be decoded in a steady-state visual evoked potential (SSVEP)-based BMI paradigm. The present study tested its feasibility in online implementation. We found that within reasonable computing time (0.114 s on average) its online system was successfully accomplished with a decoding accuracy of 53.7% on average. The accuracy was 3.2 times significantly higher than the accuracy by random-shuffled data (16.7%). Therefore, using the grid-shaped SSVEP-based BMI, one's multiclass (at least 6 classes) intention can be online decoded and subsequently control external devices.
Scientific Reports, Nov 3, 2016
We present a fast and accurate non-invasive brain-machine interface (BMI) based on demodulating s... more We present a fast and accurate non-invasive brain-machine interface (BMI) based on demodulating steady-state visual evoked potentials (SSVEPs) in electroencephalography (EEG). Our study reports an SSVEP-BMI that, for the first time, decodes primarily based on top-down and not bottom-up visual information processing. The experimental setup presents a grid-shaped flickering line array that the participants observe while intentionally attending to a subset of flickering lines representing the shape of a letter. While the flickering pixels stimulate the participant's visual cortex uniformly with equal probability, the participant's intention groups the strokes and thus perceives a 'letter Gestalt'. We observed decoding accuracy of 35.81% (up to 65.83%) with a regularized linear discriminant analysis; on average 2.05-fold, and up to 3.77-fold greater than chance levels in multi-class classification. Compared to the EEG signals, an electrooculogram (EOG) did not significantly contribute to decoding accuracies. Further analysis reveals that the top-down SSVEP paradigm shows the most focalised activation pattern around occipital visual areas; Granger causality analysis consistently revealed prefrontal topdown control over early visual processing. Taken together, the present paradigm provides the first neurophysiological evidence for the top-down SSVEP BMI paradigm, which potentially enables multiclass intentional control of EEG-BMIs without using gaze-shifting.
Brain Research, Nov 1, 2013
The influence of the illumination condition on our cognitive-performance seems to be more critica... more The influence of the illumination condition on our cognitive-performance seems to be more critical in the modern life, wherein, most people work in an office under a specific illumination condition. However, neurophysiological changes in a specific illumination state and their cognitive interpretation still remain unclear. Thereby, in the present study, the effect of different illumination conditions on the same cognitive performance was evaluated particularly by EEG wavelet analyses. During a sustained attention task, we observed that the higher illumination condition yielded significantly lower parietal tonic electroencephalogram (EEG) alpha activity before the presentation of the probe digit and longer reaction times, than that of the other illumination conditions. Although previous studies suggest that lower prestimulus EEG alpha activity is related to higher performance in an upcoming task, the reduced prestimulus alpha activity under higher illumination was associated with delayed reaction times in the present study. Presumably, the higher background illumination condition seems to be too bright for normal attentional processing and distracted participants' attention during a sustained attention task. Such a bottom-up effect by stimulus salience seemed to overwhelm a prestimulus top-down effect reflected in prestimulus alpha power during the bright background condition. This finding might imply a dynamic competition between prestimulus top-down and poststimulus bottom-up processes. Our findings provide compelling evidence that the illumination condition substantially modulates our attentional processing. Further refinement of the illumination parameters and subsequent exploration of cognitive-modulation are necessary to facilitate our cognitive performance.
Electroencephalography (EEG) has become a popular tool in brain-computer interface (BCI) research... more Electroencephalography (EEG) has become a popular tool in brain-computer interface (BCI) research. Some of the drawbacks pertain to the offline analyses of the neural signal that prevent the subjects from engaging in real-time error correction during learning. Other limitations include the complex nature of the visual stimuli, often inducing fatigue and introducing considerable delays, possibly interfering with spontaneous performance. By replacing the complex external visual input with internally driven motor imagery we can overcome some delay problems, at the expense of losing the ability to precisely parameterize features of the input stimulus. To address these issues we here introduce a direction-imagery task to BCI. We observed that all participants showed almost perfect performance in the fourth session. Participants reported that as they mastered the mental control with direct thinking of direction. These observations provide corroborative evidence for practicability of prefrontal signals to be used as promising cognitive BCI commands.
Trends in Biotechnology, Jul 1, 2014
Frontiers in Human Neuroscience, Apr 26, 2023
K (2023) Deep learning-based electroencephalic diagnosis of tinnitus symptom.
Brain-machine interfaces (BMIs) enable humans to control devices by modulating their brain signal... more Brain-machine interfaces (BMIs) enable humans to control devices by modulating their brain signals. As the current BMI technology has several obstacles to overcome, additional sources of brain activity need to be explored. It seems plausible that the brain activity associated with top-down cognitive functions could open a new prospect in the field of BMIs. As top-down cognitive BMIs could exploit neural signals from more diverse networks, a deep-learning approach with complex hidden layers may provide a more optimal decoding performance. In this study, using our top-down steady-state visual evoked potential (SSVEP) paradigm (N = 20), we observed that the decoding accuracy (48.42%) of a deep-learning algorithm with a sigmoid activation function was significantly higher than that of regularized linear discriminant analysis (rLDA) with shrinkage (42.52%; t(19) = −3.183, p < 0.01), used in our previous study. Therefore, a deep-learning approach seems to be more optimized for classification in the top-down cognitive BMI paradigm.
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Papers by Byoung-Kyong Min