606 research outputs found
Changes in Power and Information Flow in Resting-state EEG by Working Memory Process
Many studies have analyzed working memory (WM) from electroencephalogram
(EEG). However, little is known about changes in the brain neurodynamics among
resting-state (RS) according to the WM process. Here, we identified
frequency-specific power and information flow patterns among three RS EEG
before and after WM encoding and WM retrieval. Our results demonstrated the
difference in power and information flow among RS EEG in delta (1-3.5 Hz),
alpha (8-13.5 Hz), and beta (14-29.5 Hz) bands. In particular, there was a
marked increase in the alpha band after WM retrieval. In addition, we
calculated the association between significant characteristics of RS EEG and WM
performance, and interestingly, correlations were found only in the alpha band.
These results suggest that RS EEG according to the WM process has a significant
impact on the variability and WM performance of brain mechanisms in relation to
cognitive function.Comment: Submitted to 2023 11th IEEE International Winter Conference on
Brain-Computer Interfac
Siamese Sleep Transformer For Robust Sleep Stage Scoring With Self-knowledge Distillation and Selective Batch Sampling
In this paper, we propose a Siamese sleep transformer (SST) that effectively
extracts features from single-channel raw electroencephalogram signals for
robust sleep stage scoring. Despite the significant advances in sleep stage
scoring in the last few years, most of them mainly focused on the increment of
model performance. However, other problems still exist: the bias of labels in
datasets and the instability of model performance by repetitive training. To
alleviate these problems, we propose the SST, a novel sleep stage scoring model
with a selective batch sampling strategy and self-knowledge distillation. To
evaluate how robust the model was to the bias of labels, we used different
datasets for training and testing: the sleep heart health study and the
Sleep-EDF datasets. In this condition, the SST showed competitive performance
in sleep stage scoring. In addition, we demonstrated the effectiveness of the
selective batch sampling strategy with a reduction of the standard deviation of
performance by repetitive training. These results could show that SST extracted
effective learning features against the bias of labels in datasets, and the
selective batch sampling strategy worked for the model robustness in training.Comment: Submitted to 2023 11th IEEE International Winter Conference on
Brain-Computer Interfac
Association of circulating 25-hydroxyvitamin d levels with hypertension and blood pressure values in korean adults:A Mendelian randomization study on a subset of the Korea National Health and Nutrition Survey 2011-2012 population
Multi-Signal Reconstruction Using Masked Autoencoder From EEG During Polysomnography
Polysomnography (PSG) is an indispensable diagnostic tool in sleep medicine,
essential for identifying various sleep disorders. By capturing physiological
signals, including EEG, EOG, EMG, and cardiorespiratory metrics, PSG presents a
patient's sleep architecture. However, its dependency on complex equipment and
expertise confines its use to specialized clinical settings. Addressing these
limitations, our study aims to perform PSG by developing a system that requires
only a single EEG measurement. We propose a novel system capable of
reconstructing multi-signal PSG from a single-channel EEG based on a masked
autoencoder. The masked autoencoder was trained and evaluated using the
Sleep-EDF-20 dataset, with mean squared error as the metric for assessing the
similarity between original and reconstructed signals. The model demonstrated
proficiency in reconstructing multi-signal data. Our results present promise
for the development of more accessible and long-term sleep monitoring systems.
This suggests the expansion of PSG's applicability, enabling its use beyond the
confines of clinics.Comment: Proc. 12th IEEE International Winter Conference on Brain-Computer
Interfac
Impact of Nap on Performance in Different Working Memory Tasks Using EEG
Electroencephalography (EEG) has been widely used to study the relationship
between naps and working memory, yet the effects of naps on distinct working
memory tasks remain unclear. Here, participants performed word-pair and
visuospatial working memory tasks pre- and post-nap sessions. We found marked
differences in accuracy and reaction time between tasks performed pre- and
post-nap. In order to identify the impact of naps on performance in each
working memory task, we employed clustering to classify participants as high-
or low-performers. Analysis of sleep architecture revealed significant
variations in sleep onset latency and rapid eye movement (REM) proportion. In
addition, the two groups exhibited prominent differences, especially in the
delta power of the Non-REM 3 stage linked to memory. Our results emphasize the
interplay between nap-related neural activity and working memory, underlining
specific EEG markers associated with cognitive performance.Comment: Submitted to 2024 12th IEEE International Winter Conference on
Brain-Computer Interfac
Relationship Between Mood, Sleepiness, and EEG Functional Connectivity by 40 Hz Monaural Beats
The monaural beat is known that it can modulate brain and personal states.
However, which changes in brain waves are related to changes in state is still
unclear. Therefore, we aimed to investigate the effects of monaural beats and
find the relationship between them. Ten participants took part in five separate
random sessions, which included a baseline session and four sessions with
monaural beats stimulation: one audible session and three inaudible sessions.
Electroencephalogram (EEG) were recorded and participants completed pre- and
post-stimulation questionnaires assessing mood and sleepiness. As a result,
audible session led to increased arousal and positive mood compared to other
conditions. From the neurophysiological analysis, statistical differences in
frontal-central, central-central, and central-parietal connectivity were
observed only in the audible session. Furthermore, a significant correlation
was identified between sleepiness and EEG power in the temporal and occipital
regions. These results suggested a more detailed correlation for stimulation to
change its personal state. These findings have implications for applications in
areas such as cognitive enhancement, mood regulation, and sleep management
Neurophysiological Response Based on Auditory Sense for Brain Modulation Using Monaural Beat
Brain modulation is a modification process of brain activity through external
stimulations. However, which condition can induce the activation is still
unclear. Therefore, we aimed to identify brain activation conditions using 40
Hz monaural beat (MB). Under this stimulation, auditory sense status which is
determined by frequency and power range is the condition to consider. Hence, we
designed five sessions to compare; no stimulation, audible (AB), inaudible in
frequency, inaudible in power, and inaudible in frequency and power. Ten
healthy participants underwent each stimulation session for ten minutes with
electroencephalogram (EEG) recording. For analysis, we calculated the power
spectral density (PSD) of EEG for each session and compared them in frequency,
time, and five brain regions. As a result, we observed the prominent power peak
at 40 Hz in only AB. The induced EEG amplitude increase started at one minute
and increased until the end of the session. These results of AB had significant
differences in frontal, central, temporal, parietal, and occipital regions
compared to other stimulations. From the statistical analysis, the PSD of the
right temporal region was significantly higher than the left. We figure out the
role that the auditory sense is important to lead brain activation. These
findings help to understand the neurophysiological principle and effects of
auditory stimulation.Comment: Accepted to EMBC 202
Scaling Law for Recommendation Models: Towards General-purpose User Representations
Recent advancement of large-scale pretrained models such as BERT, GPT-3,
CLIP, and Gopher, has shown astonishing achievements across various task
domains. Unlike vision recognition and language models, studies on
general-purpose user representation at scale still remain underexplored. Here
we explore the possibility of general-purpose user representation learning by
training a universal user encoder at large scales. We demonstrate that the
scaling law is present in user representation learning areas, where the
training error scales as a power-law with the amount of computation. Our
Contrastive Learning User Encoder (CLUE), optimizes task-agnostic objectives,
and the resulting user embeddings stretch our expectation of what is possible
to do in various downstream tasks. CLUE also shows great transferability to
other domains and companies, as performances on an online experiment shows
significant improvements in Click-Through-Rate (CTR). Furthermore, we also
investigate how the model performance is influenced by the scale factors, such
as training data size, model capacity, sequence length, and batch size.
Finally, we discuss the broader impacts of CLUE in general.Comment: Accepted at AAAI 2023. This version includes the technical appendi
Robust and Reproducible Generation of Induced Neural Stem Cells from Human Somatic Cells by Defined Factors
A Case of Pituitary Metastasis from Breast Cancer That Presented as Left Visual Disturbance
Tumors that metastasize to the pituitary gland are unusual, and are typically seen in elderly patients with diffuse malignant disease. The most common metastases to the pituitary are from primary breast and lung cancers. We report a 65-year-old woman with pituitary metastasis from breast cancer who presented with recent-onset left progressive deterioration of visual acuity and visual field. The clinical diagnosis was made after brain and sellar magnetic resonance imaging showed a large sellar mass compressing the optic chiasm and invading the pituitary stalk. An otorhinolaryngology and neurosurgery team removed the tumor via a transsphenoidal approach, and this procedure obtained symptomatic relief. Postoperatively, metastasis from breast invasive ductal adenocarcinoma was confirmed histologically. We report this unusual case with a review of the relevant literature
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