Biology subjects

Kim, S.-P.

Publications and source records attributed to Kim, S.-P..

16 recordsLinked to original sources

bioRxiv2026

Spatiotemporal transformation of neural data reveals representations of erroneous behaviors

Abnormal states such as erroneous behaviors are generally difficult to represent from neural data. However, such states are also known to have specific spatiotemporal features, indicating a feasibility of developing a method to focus on them. If a method can highlight these spatiotemporal features, it may effectively represent such abnormal states, helping evaluate abnormal brain functions. In the present study, we proposed the hierarchy of supported modules (HSM) to highlight spatiotemporal features that can represent abnormal states. HSM spatiotemporally transforms multidimensional neural time-series based on their spatiotemporal context. We evaluated HSM through decoding and similarity analyses using multiple publicly available datasets. In the HSM results, decoding accuracies were higher for erroneous behaviors than for normal behaviors, and similarities were lower between erroneous behaviors and normal behaviors than between normal behaviors, demonstrating the ability of HSM to capture the spatiotemporal features of erroneous behaviors. Surprisingly, many parts of these results were also present even before HSM learning, showing the virtue of HSM as a simple-to-use method. The proposed HSM method may help elucidate the mechanisms underlying erroneous behaviors.

neuroscience↗

bioRxiv2026

Effects of Cognitive Demand Reduction on Choice Overload

Choice overload occurs when an ever-growing number of options impairs decision quality, because evaluating options taxes cognitive resources. We investigated whether reducing cognitive demand could mitigate overload by encouraging greater cognitive effort to achieve optimal choice. We conducted two experiments manipulating cognitive demand in complementary ways: Experiment 1 reduced demand by presenting high-attractiveness sets, and Experiment 2 did so by providing a shortlist tool. In both experiments, participants chose from sets of 6-24 options while their eye-gaze and electroencephalographic (EEG) data were recorded. We found that reducing demand made decisions faster, but did not improve choice performance as set-size increased. Under low-demand conditions, eye-gaze measures revealed narrower search and EEG measures showed reduced working memory engagement per option, together indicating less searching and processing efforts. These results suggest that even with reduced cognitive demand, people coast through easier decisions, conserving effort and leaving the choice overload effect largely intact.

neuroscience↗

bioRxiv2025

A Plug-and-Play P300-Based BCI with Calibration-Free Application

The practical deployment of P300-based brain-computer interfaces (BCIs) have long been hindered by the need for user-specific calibration and multiple stimulus repetitions. In this study, we build and validate a plug-and-play, calibration-free P300 BCI system that operates in a single-trial setting using a pre-trained xDAWN spatial filter and a deep convolutional neural network. Without any subject-specific adaptation, participants could control an IoT device via the BCI system in real time with decoding accuracy reaching 85.2% comparable to the offline benchmark of 87.8%, demonstrating the feasibility of realizing a plug-and-play BCI. Offline analyses revealed that a small set of parietal and occipital electrodes contributed most to decoding performance, supporting the viability of low-density, high-accuracy BCI configurations. A data sufficiency simulation provided quantitative guidelines for pre-training dataset size, and an error trial analysis showed that both stimulus timing and preparatory attentional state influenced real-time decoding performance. Together, these results demonstrate the first real-time validation of a calibration-free, single-trial P300 BCI and offer practical insights for developing scalable, robust, and user-friendly BCI systems.

bioengineering↗

bioRxiv2024

Neural geometry efficiently representing abstract form of value and modality in the primate basal ganglia

Basal ganglia process diverse values from various modalities using limited resources, necessitating efficient processing. This involves converging tactile and visual information within bimodal value-coding neurons, ensuring efficient processing with limited number of neurons. However, convergence at the single neuron level may compromise modality-specific information, raising the question: Does efficient processing inevitably degrade information quality? We investigated the representational geometry in the putamen of macaque monkeys trained to learn values from tactile and visual inputs. Here, we demonstrated that the population representation of bimodal value-coding neurons in the putamen preserved both value and modality information, and these representations were shared to efficiently maintain quality. Notably, these representations were generalized across identical modalities and values, resulting in an efficient low-dimensional representation. Furthermore, a faster transformation to a generalized value representation within neural geometry reflected greater confidence in value-guided choice behavior--this correlation not observed in conventional decoding. Our results suggest that bimodal value-coding neurons play a key role in balancing efficiency and information fidelity, facilitating cognitive states required for confident decision-making.

neuroscience↗

bioRxiv2024

Task-Relevant Stimulus Design Improves P300-Based Brain-Computer Interfaces

ObjectiveIn the pursuit of refining P300-based brain-computer interfaces (BCIs), our research aims to propose a novel stimulus design focused on selective attention and task relevance to address the challenges of P300-based BCIs, including the necessity of repetitive stimulus presentations, accuracy improvement, user variability, and calibration demands. ApproachIn the oddball task for P300-based BCIs, we develop a stimulus design involving task-relevant dynamic stimuli implemented as finger-tapping to enhance the elicitation and consistency of event-related potentials (ERPs). We further improve the performance of P300-based BCIs by optimizing ERP feature extraction and classification in offline analyses. Main ResultsWith the proposed stimulus design, online P300-based BCIs in 37 healthy participants achieves the accuracy of 91.2% and the information transfer rate (ITR) of 28.37 bits/min with two stimulus repetitions. With optimized computational modeling in BCIs, our offline analyses reveal the possibility of single-trial execution, showcasing the accuracy of 91.7% and the ITR of 59.92 bits/min. Furthermore, our exploration into the feasibility of across-subject zero-calibration BCIs through offline analyses, where a BCI built on a dataset of 36 participants is directly applied to a left-out participant with no calibration, yields the accuracy of 94.23% and the ITR of 31.56 bits/min with two stimulus repetitions and the accuracy of 87.75% and the ITR of 52.61 bits/min with single-trial execution. When using the finger-tapping stimulus, the variability in performance among participants is the lowest, and a greater increase in performance is observed especially for those showing lower performance using the conventional color-changing stimulus. SignficanceUsing a novel task-relevant dynamic stimulus design, this study achieves one of the highest levels of P300-based BCI performance to date. This underscores the importance of coupling stimulus paradigms with computational methods for improving P300-based BCIs.

bioengineering↗

bioRxiv2024

Distraction Impact of Concurrent Conversation on Event-Related Potential Based Brain-Computer Interfaces

ObjectiveThis study investigates the impact of conversation on the performance of visual event-related potential (ERP)-based brain-computer interfaces (BCIs), considering distractions in real life environment. The research aims to understand how cognitive distractions from speaking and listening activities affect ERP-BCI performance. ApproachThe experiment employs a dual-task paradigm where participants control a smart light using visual ERP-BCIs while simultaneously conducting speaking or listening tasks. Main resultsThe findings reveal that speaking notably degrades BCI accuracy and the amplitude of ERP components, while increases the latency variability of ERP components and occipital alpha power. In contrast, listening and simple syllable repetition tasks have a lesser impact on these variables. The results suggest that speaking activity significantly distracts visual attentional processes critical for BCI operation SignificanceThis study highlights the need to take distractions by daily conversation into account of the design and implementation of ERP-BCIs.

bioengineering↗

bioRxiv2024

Behavior can be decoded across the cortex when individual differences are considered

Group-level analyses have typically linked behavioral signatures to a constrained set of brain areas. Here we show that two behavioral metrics - reaction time (RT) and confidence - can be decoded across the cortex when each individual is considered separately. Subjects (N=50) completed a perceptual decision-making task with confidence. We built models decoding trial-level RT and confidence separately for each subject using the activation patterns in one brain area at a time after splitting the entire cortex into 200 regions of interest (ROIs). First, we developed a simple test to determine the robustness of decoding performance, which showed that several hundred trials per subject are required for robust decoding. We then examined the decoding performance at the group and subject levels. At the group level, we replicated previous results by showing that both RT and confidence could be decoded from a small number of ROIs (12.0% and 3.5%, respectively). Critically, at the subject level, both RT and confidence could be decoded from most brain regions even after Bonferroni correction (90.0% and 72.5%, respectively). Surprisingly, we observed that many brain regions exhibited opposite brain-behavior relationships across individuals, such that, for example, higher activations predicted fast RTs in some subjects but slow RTs in others. All results were replicated in a second dataset. These findings show that behavioral signatures can be decoded from a much broader range of cortical areas than previously recognized and suggest the need to study the brain-behavior relationship at both the group and subject levels.

neuroscience↗

bioRxiv2024

Freeing P300-Based Brain-Computer Interfaces from Daily Calibration by Extracting Daily Common ERPs

When people use brain-computer interfaces (BCIs) based on event-related potentials (ERPs) over different days, they often need to repeatedly calibrate BCIs every day using ERPs acquired on the same day. This cumbersome recalibration procedure would make it difficult to use BCIs on a daily basis. We aim to address the daily calibration issue by examining across-day variation of the BCI performance and proposing a method to avoid daily calibration. To this end, we implemented a P300-based BCI system designed to control a home appliance over five days in nineteen healthy subjects. We first examined how the BCI performance varied across days with or without daily calibration. On each day, P300-based BCIs were tested using calibration-based and calibration-free decoders (CB and CF), with a CB or a CF decoder being built on the training data on each day or those on the first day, respectively. Using the CF decoder resulted in lower BCI performance on subsequent days compared to the CB decoder. Then, we developed a method to extract daily common ERP patterns from observed ERP signals using the sparse dictionary learning algorithm. We applied this method to the CF decoder and retested the BCI performance over days. Using the proposed method improved the CF decoder performance on subsequent days; the performance was closer to the level of the CB decoder, with improvement of accuracy by 2.28%, 1.93%, 1.75%, and 3.86 % on the subsequent four days, respectively, compared to the original CF decoder. The method proposed by our study may provide a novel approach to addressing the daily-calibration issue for P300-based BCIs, which is essential to implementing BCIs into daily life.

bioengineering↗

bioRxiv2023

Somatosensory cortical representations of assimilation effect by vibrotactile stimulation

This study investigated neural activity related to the assimilation effect in the perception of vibrotactile stimuli. The assimilation effect refers to a tactile perceptual bias in which the perception of the vibrotactile frequency on one finger is biased towards a distracting vibrotactile stimulus targeting other types of mechanoreceptors on the different finger. The assimilation effect occurs not only between fingers on the same hand (in-hand) but also between fingers on different hands (across-hand). These behavioral aspects of the assimilation effect lead to an assumption that neural processes related to the assimilation effect would involve the integration of different tactile information mediated by the somatosensory cortex. We addressed this hypothesis by investigating brain responses using functional magnetic resonance imaging (fMRI) to vibrotactile stimuli that induced assimilation effect under in-hand and across-hand conditions. We first observed that the vibrotactile stimuli presented in this study activated primary (S1) and secondary (S2) somatosensory cortices. Yet, neural responses in these regions did not show correlations with individual assimilation effects, indicating that neural processing of vibrotactile signals in S1 and S2 would not be directly linked to the assimilation effect. Instead, we found that connectivity between S1 and medial prefrontal cortex (mPFC) was correlated with individual in-hand assimilation effects and that connectivity between S2 and posterior parietal cortex (PPC) was correlated with individual across-hand assimilation effects. These results suggest that the assimilation effect may be related to tactile information integration via functional connections between the somatosensory cortex and higher-order brain regions.

neuroscience↗

bioRxiv2023

Differential formation of motor cortical dynamics during movement preparation according to the predictability of go timing

Motor cortex not only executes but also prepares movement, as motor cortical neurons exhibit preparatory activity that predicts upcoming movements. In movement preparation, animals adopt different strategies in response to uncertainties existing in nature such as the unknown timing of when a predator will attack--an environmental cue informing go. However, how motor cortical neurons cope with such uncertainties is less understood. In this study, we aim to investigate whether and how preparatory activity is altered depending on the predictability of go timing. We analyze firing activities of anterior lateral motor cortex (ALM) in mice during two auditory delayed-response tasks each with predictable or unpredictable go timing. When go timing is unpredictable, preparatory activities immediately reach and stay in a neural state capable of producing movement anytime to a sudden go cue. When go timing is predictable, preparation activity reaches the movement-producible state more gradually, to secure more accurate decisions. Surprisingly, this preparation process entails a longer reaction time (RT). We find that as preparatory activity increase in accuracy, it takes longer for a neural state to transition from the end of preparation to the start of movement. Our results suggest that motor cortex fine-tunes preparatory activity for more accurate movement using the predictability of go timing. Significant statementsAnticipating when to move is important in movement preparation. However, it is unclear how motor cortex prepares movement depending on how easy that anticipation is. To answer this, we examine motor cortical activity of mice during a delayed-response task. While motor cortical activity rapidly reaches a "movement-ready" state with unpredictable timing of a go signal (go timing), it does so more gradually when go timing is predictable. Moreover, when go timing is more predictable, motor cortex produces more accurate movement with, unexpectedly, a longer response time. This suggests that rodent motor cortical neurons can resource time information.

neuroscience↗

bioRxiv2023

Multiple brain activation patterns for the same task

Meaningful variation in internal states that impacts cognition and behavior remains challenging to discover and characterize. Here we leveraged trial-to-trial fluctuations in the brain-wide signal recorded using functional MRI to test if distinct sets of brain regions are activated on different trials when accomplishing the same task. Across three different perceptual decision-making experiments, we estimated the brain activations for each trial. We then clustered the trials based on their similarity using modularity-maximization, a data-driven classification method. In each experiment, we found multiple distinct but stable subtypes of trials, suggesting that the same task can be accomplished in the presence of widely varying brain activation patterns. Surprisingly, in all experiments, one of the subtypes exhibited strong activation in the default mode network, which is typically thought to decrease in activity during tasks that require externally focused attention. The remaining subtypes were characterized by activations in different task-positive areas. The default mode network subtype was characterized by behavioral signatures that were similar to the other subtypes exhibiting activation with task-positive regions. These findings demonstrate that the same perceptual decision-making task is accomplished through multiple brain activation patterns.

neuroscience↗

bioRxiv2022

Idiosyncratic relation between human brain activity and behavior

Research in neuroscience often assumes universal neural mechanisms, but increasing evidence points towards sizeable individual differences in brain activations. What remains unclear is the extent of the idiosyncrasy and whether different types of analyses are associated with different levels of idiosyncrasy. Here we develop a new method for addressing these questions. The method consists of computing the within-subject reliability and subject-to-group similarity of brain activations and submitting these values to a computational model that quantifies the relative strength of group- and subject-level factors. We apply this method to a perceptual decision-making task (N=50) and find that activations related to task, reaction time (RT), and confidence are influenced equally strongly by group- and subject-level factors. Both group- and subject-level factors are dwarfed by a noise factor, though higher levels of smoothing increases their contributions relative to noise. Overall, our method allows for the quantification of group- and subject-level factors of brain activations and thus provides a more detailed understanding of the idiosyncrasy levels in brain activations.

neuroscience↗

bioRxiv2022

Decoding Imagined Musical Pitch from Human Scalp Electroencephalograms

Brain-computer interfaces (BCIs) can restore the functions of communication and control in people with paralysis. In addition to the currently proven functions restored by BCIs, it would enrich life if one could regain a function of musical activity. However, it remains largely unknown whether it is feasible to decode imagined musical information directly from neural activity. Among various musical information, this study aimed to decode pitch information directly from scalp electroencephalography (EEG). Twenty healthy participants performed a task to imagine one of the seven musical pitches (C4 - B4) randomly. To find EEG features for pitch imagination, we took two approaches: exploring multi-band spectral power at individual channels (IC); and exploring power differences between bilaterally symmetric channels (DC). We classified these features into the seven pitch classes using various types of classifiers. The selected spectral power features revealed marked contrasts between left and right hemispheres, between low-, (<13 Hz) and high-frequency (> 13 Hz) bands, and between frontal and parietal areas. The best classification performance for seven pitches was obtained using the IC feature and SVM with the average accuracy of 35.68{+/-}7.47% (max. 50%) and the average information transfer rate (ITR) of 0.37{+/-}0.22 bits/sec. Yet, when we decoded a different number of classes (K = 2 [~] 6) by grouping adjacent pitches, ITR was similar across K as well as between IC and DC features, suggesting efficiency of DC features. This study would be the first to demonstrate the feasibility of decoding imagined musical pitch directly from human EEG.

bioengineering↗

bioRxiv2021

Spatio-temporally efficient coding assigns functions to hierarchical structures of the visual system

Hierarchical structures constitute a wide array of brain areas, including the visual system. One of the important questions regarding visual hierarchical structures is to identify computational principles for assigning functions that represent the external world to hierarchical structures of the visual system. Given that visual hierarchical structures contain both bottom-up and top-down pathways, the derived principles should encompass these bidirectional pathways. However, existing principles such as predictive coding do not provide an effective principle for bidirectional pathways. Therefore, we propose a novel computational principle for visual hierarchical structures as spatio-temporally efficient coding underscored by the efficient use of given resources in both neural activity space and processing time. This coding principle optimises bidirectional information transmissions over hierarchical structures by simultaneously minimising temporal differences in neural responses and maximising entropy in neural representations. Simulations demonstrated that the proposed spatio-temporally efficient coding was able to assign the function of appropriate neural representations of natural visual scenes to visual hierarchical structures. Furthermore, spatio-temporally efficient coding was able to predict well-known phenomena, including deviations in neural responses to unlearned inputs and bias in preferred orientations. Our proposed spatio-temporally efficient coding may facilitate deeper mechanistic understanding of the computational processes of hierarchical brain structures.

neuroscience↗

bioRxiv2021

In vivo direct imaging of neuronal activity at high temporo-spatial resolution

There has been a longstanding demand for noninvasive neuroimaging methods capable of detecting neuronal activity at both high temporal and spatial resolution. Here, we propose a novel method that enables Direct Imaging of Neuronal Activity for functional MRI (termed DIANA-fMRI) that can dynamically image spiking activity in milliseconds precision, while retaining the original benefit of high spatial resolution of MRI. DIANA-fMRI was demonstrated through in vivo mice brain imaging at 9.4 T applying electrical whisker-pad stimulation, directly imaging the spiking activity as well as capturing its sequential propagation along the thalamocortical pathway, as further confirmed through in vivo spike recording and optogenetics. DIANA-fMRI will open up new avenues in brain science by providing a deeper understanding of the brains functional organization including neural networks.

neuroscience↗

bioRxiv2020

Keeping things in mind for future thinking: Arithmetic booster reduces delay discounting

People have higher preference for immediate over delayed rewards, and it is suggested that such an impulsive tendency is governed by ones ability to simulate future rewards. Consistent with this view, recent studies have shown that enforcing individuals to focus on episodic future thoughts reduces their impulsivity. Inspired by these successful reports, we hypothesized that administration of a simple cognitive task which is linked with future thinking might also function as an effective tool for modulating individuals preference for immediate (or delayed) rewards. Specifically, we used one associative memory task and one working memory task that each of which was administered to intervene acquired amount of information and individuals ability to construct a coherent future event, respectively. Among the set of cognitive tasks, we found that only the arithmetic working memory task had a significant effect of reducing individuals impulsivity. However, in our follow-up experiment, this result was not replicated. Across these two independent experiments, we observed a significant main effect of repetition in individuals impulsivity measure, such that participants showed more patient choices at the second compared to the first assessment task. In conclusion, there was no clear evidence supporting that our suggested intervention tasks effectively reduce individuals impulsivity, while the current results call attention to the importance of taking into account task repetition effects in studying the impacts of cognitive training and intervention.

neuroscience↗