573 research outputs found
Prediction of Memory Retrieval Performance Using Ear-EEG Signals
Many studies have explored brain signals during the performance of a memory
task to predict later remembered items. However, prediction methods are still
poorly used in real life and are not practical due to the use of
electroencephalography (EEG) recorded from the scalp. Ear-EEG has been recently
used to measure brain signals due to its flexibility when applying it to real
world environments. In this study, we attempt to predict whether a shown
stimulus is going to be remembered or forgotten using ear-EEG and compared its
performance with scalp-EEG. Our results showed that there was no significant
difference between ear-EEG and scalp-EEG. In addition, the higher prediction
accuracy was obtained using a convolutional neural network (pre-stimulus:
74.06%, on-going stimulus: 69.53%) and it was compared to other baseline
methods. These results showed that it is possible to predict performance of a
memory task using ear-EEG signals and it could be used for predicting memory
retrieval in a practical brain-computer interface.Comment: Accected for publication at EMBC 202
End-to-End Automatic Sleep Stage Classification Using Spectral-Temporal Sleep Features
Sleep disorder is one of many neurological diseases that can affect greatly
the quality of daily life. It is very burdensome to manually classify the sleep
stages to detect sleep disorders. Therefore, the automatic sleep stage
classification techniques are needed. However, the previous automatic sleep
scoring methods using raw signals are still low classification performance. In
this study, we proposed an end-to-end automatic sleep staging framework based
on optimal spectral-temporal sleep features using a sleep-edf dataset. The
input data were modified using a bandpass filter and then applied to a
convolutional neural network model. For five sleep stage classification, the
classification performance 85.6% and 91.1% using the raw input data and the
proposed input, respectively. This result also shows the highest performance
compared to conventional studies using the same dataset. The proposed framework
has shown high performance by using optimal features associated with each sleep
stage, which may help to find new features in the automatic sleep stage method
Assessment of Unconsciousness for Memory Consolidation Using EEG Signals
The assessment of consciousness and unconsciousness is a challenging issue in
modern neuroscience. Consciousness is closely related to memory consolidation
in that memory is a critical component of conscious experience. So far, many
studies have been reported on memory consolidation during consciousness, but
there is little research on memory consolidation during unconsciousness.
Therefore, we aim to assess the unconsciousness in terms of memory
consolidation using electroencephalogram signals. In particular, we used
unconscious state during a nap; because sleep is the only state in which
consciousness disappears under normal physiological conditions. Seven
participants performed two memory tasks (word-pairs and visuo-spatial) before
and after the nap to assess the memory consolidation during unconsciousness. As
a result, spindle power in central, parietal, occipital regions during
unconsciousness was positively correlated with the performance of location
memory. With the memory performance, there was also a negative correlation
between delta connectivity and word-pairs memory, alpha connectivity and
location memory, and spindle connectivity and word-pairs memory. We
additionally observed the significant relationship between unconsciousness and
brain changes during memory recall before and after the nap. These findings
could help present new insights into the assessment of unconsciousness by
exploring the relationship with memory consolidation.Comment: Submitted to IEEE International Conference on System, Man, and
Cybernetics (IEEE SMC 2020
Decoding Event-related Potential from Ear-EEG Signals based on Ensemble Convolutional Neural Networks in Ambulatory Environment
Recently, practical brain-computer interface is actively carried out,
especially, in an ambulatory environment. However, the electroencephalography
(EEG) signals are distorted by movement artifacts and electromyography signals
when users are moving, which make hard to recognize human intention. In
addition, as hardware issues are also challenging, ear-EEG has been developed
for practical brain-computer interface and has been widely used. In this paper,
we proposed ensemble-based convolutional neural networks in ambulatory
environment and analyzed the visual event-related potential responses in scalp-
and ear-EEG in terms of statistical analysis and brain-computer interface
performance. The brain-computer interface performance deteriorated as 3-14%
when walking fast at 1.6 m/s. The proposed methods showed 0.728 in average of
the area under the curve. The proposed method shows robust to the ambulatory
environment and imbalanced data as well.Comment: Submitted IEEE the 9th International Winter Conference on
Brain-Computer Interface. arXiv admin note: text overlap with
arXiv:2002.0108
Network of Evolvable Neural Units: Evolving to Learn at a Synaptic Level
Although Deep Neural Networks have seen great success in recent years through
various changes in overall architectures and optimization strategies, their
fundamental underlying design remains largely unchanged. Computational
neuroscience on the other hand provides more biologically realistic models of
neural processing mechanisms, but they are still high level abstractions of the
actual experimentally observed behaviour. Here a model is proposed that bridges
Neuroscience, Machine Learning and Evolutionary Algorithms to evolve individual
soma and synaptic compartment models of neurons in a scalable manner. Instead
of attempting to manually derive models for all the observed complexity and
diversity in neural processing, we propose an Evolvable Neural Unit (ENU) that
can approximate the function of each individual neuron and synapse. We
demonstrate that this type of unit can be evolved to mimic Integrate-And-Fire
neurons and synaptic Spike-Timing-Dependent Plasticity. Additionally, by
constructing a new type of neural network where each synapse and neuron is such
an evolvable neural unit, we show it is possible to evolve an agent capable of
learning to solve a T-maze environment task. This network independently
discovers spiking dynamics and reinforcement type learning rules, opening up a
new path towards biologically inspired artificial intelligence
Prediction of Event Related Potential Speller Performance Using Resting-State EEG
Event-related potential (ERP) speller can be utilized in device control and
communication for locked-in or severely injured patients. However, problems
such as inter-subject performance instability and ERP-illiteracy are still
unresolved. Therefore, it is necessary to predict classification performance
before performing an ERP speller in order to use it efficiently. In this study,
we investigated the correlations with ERP speller performance using a
resting-state before an ERP speller. In specific, we used spectral power and
functional connectivity according to four brain regions and five frequency
bands. As a result, the delta power in the frontal region and functional
connectivity in the delta, alpha, gamma bands are significantly correlated with
the ERP speller performance. Also, we predicted the ERP speller performance
using EEG features in the resting-state. These findings may contribute to
investigating the ERP-illiteracy and considering the appropriate alternatives
for each user.Comment: Accepted to IEEE EMBC 202
Decoding Visual Recognition of Objects from EEG Signals based on Attention-Driven Convolutional Neural Network
The ability to perceive and recognize objects is fundamental for the
interaction with the external environment. Studies that investigate them and
their relationship with brain activity changes have been increasing due to the
possible application in an intuitive brain-machine interface (BMI). In
addition, the distinctive patterns when presenting different visual stimuli
that make data differentiable enough to be classified have been studied.
However, reported classification accuracy still low or employed techniques for
obtaining brain signals are impractical to use in real environments. In this
study, we aim to decode electroencephalography (EEG) signals depending on the
provided visual stimulus. Subjects were presented with 72 photographs belonging
to 6 different semantic categories. We classified 6 categories and 72 exemplars
according to visual stimuli using EEG signals. In order to achieve a high
classification accuracy, we proposed an attention driven convolutional neural
network and compared our results with conventional methods used for classifying
EEG signals. We reported an accuracy of 50.37% and 26.75% for 6-class and
72-class, respectively. These results statistically outperformed other
conventional methods. This was possible because of the application of the
attention network using human visual pathways. Our findings showed that EEG
signals are possible to differentiate when subjects are presented with visual
stimulus of different semantic categories and at an exemplar-level with a high
classification accuracy; this demonstrates its viability to be applied it in a
real-world BMI
Spatio-Temporal Dynamics of Visual Imagery for Intuitive Brain-Computer Interface
Visual imagery is an intuitive brain-computer interface paradigm, referring
to the emergence of the visual scene. Despite its convenience, analysis of its
intrinsic characteristics is limited. In this study, we demonstrate the effect
of time interval and channel selection that affects the decoding performance of
the multi-class visual imagery. We divided the epoch into time intervals of 0-1
s and 1-2 s and performed six-class classification in three different brain
regions: whole brain, visual cortex, and prefrontal cortex. In the time
interval, 0-1 s group showed 24.2 % of average classification accuracy, which
was significantly higher than the 1-2 s group in the prefrontal cortex. In the
three different regions, the classification accuracy of the prefrontal cortex
showed significantly higher performance than the visual cortex in 0-1 s
interval group, implying the cognitive arousal during the visual imagery. This
finding would provide crucial information in improving the decoding
performance.Comment: 5 pages, 4 figures, 3 table
Reconstructing ERP Signals Using Generative Adversarial Networks for Mobile Brain-Machine Interface
Practical brain-machine interfaces have been widely studied to accurately
detect human intentions using brain signals in the real world. However, the
electroencephalography (EEG) signals are distorted owing to the artifacts such
as walking and head movement, so brain signals may be large in amplitude rather
than desired EEG signals. Due to these artifacts, detecting accurately human
intention in the mobile environment is challenging. In this paper, we proposed
the reconstruction framework based on generative adversarial networks using the
event-related potentials (ERP) during walking. We used a pre-trained
convolutional encoder to represent latent variables and reconstructed ERP
through the generative model which shape similar to the opposite of encoder.
Finally, the ERP was classified using the discriminative model to demonstrate
the validity of our proposed framework. As a result, the reconstructed signals
had important components such as N200 and P300 similar to ERP during standing.
The accuracy of reconstructed EEG was similar to raw noisy EEG signals during
walking. The signal-to-noise ratio of reconstructed EEG was significantly
increased as 1.3. The loss of the generative model was 0.6301, which is
comparatively low, which means training generative model had high performance.
The reconstructed ERP consequentially showed an improvement in classification
performance during walking through the effects of noise reduction. The proposed
framework could help recognize human intention based on the brain-machine
interface even in the mobile environment.Comment: Submitted to IEEE International Conference on System, Man, and
Cybernetics (SMC 2020
Classification of Imagined Speech Using Siamese Neural Network
Imagined speech is spotlighted as a new trend in the brain-machine interface
due to its application as an intuitive communication tool. However, previous
studies have shown low classification performance, therefore its use in
real-life is not feasible. In addition, no suitable method to analyze it has
been found. Recently, deep learning algorithms have been applied to this
paradigm. However, due to the small amount of data, the increase in
classification performance is limited. To tackle these issues, in this study,
we proposed an end-to-end framework using Siamese neural network encoder, which
learns the discriminant features by considering the distance between classes.
The imagined words (e.g., arriba (up), abajo (down), derecha (right), izquierda
(left), adelante (forward), and atr\'as (backward)) were classified using the
raw electroencephalography (EEG) signals. We obtained a 6-class classification
accuracy of 31.40% for imagined speech, which significantly outperformed other
methods. This was possible because the Siamese neural network, which increases
the distance between dissimilar samples while decreasing the distance between
similar samples, was used. In this regard, our method can learn discriminant
features from a small dataset. The proposed framework would help to increase
the classification performance of imagined speech for a small amount of data
and implement an intuitive communication system
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