3,801 research outputs found
Modeling cognitive load as a self-supervised brain rate with electroencephalography and deep learning
The principal reason for measuring mental workload is to quantify the
cognitive cost of performing tasks to predict human performance. Unfortunately,
a method for assessing mental workload that has general applicability does not
exist yet. This research presents a novel self-supervised method for mental
workload modelling from EEG data employing Deep Learning and a continuous brain
rate, an index of cognitive activation, without requiring human declarative
knowledge. This method is a convolutional recurrent neural network trainable
with spatially preserving spectral topographic head-maps from EEG data to fit
the brain rate variable. Findings demonstrate the capacity of the convolutional
layers to learn meaningful high-level representations from EEG data since
within-subject models had a test Mean Absolute Percentage Error average of 11%.
The addition of a Long-Short Term Memory layer for handling sequences of
high-level representations was not significant, although it did improve their
accuracy. Findings point to the existence of quasi-stable blocks of learnt
high-level representations of cognitive activation because they can be induced
through convolution and seem not to be dependent on each other over time,
intuitively matching the non-stationary nature of brain responses.
Across-subject models, induced with data from an increasing number of
participants, thus containing more variability, obtained a similar accuracy to
the within-subject models. This highlights the potential generalisability of
the induced high-level representations across people, suggesting the existence
of subject-independent cognitive activation patterns. This research contributes
to the body of knowledge by providing scholars with a novel computational
method for mental workload modelling that aims to be generally applicable, does
not rely on ad-hoc human-crafted models supporting replicability and
falsifiability.Comment: 18 pages, 12 figures, 1 tabl
Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals
An electroencephalography (EEG) based Brain Computer Interface (BCI) enables
people to communicate with the outside world by interpreting the EEG signals of
their brains to interact with devices such as wheelchairs and intelligent
robots. More specifically, motor imagery EEG (MI-EEG), which reflects a
subjects active intent, is attracting increasing attention for a variety of BCI
applications. Accurate classification of MI-EEG signals while essential for
effective operation of BCI systems, is challenging due to the significant noise
inherent in the signals and the lack of informative correlation between the
signals and brain activities. In this paper, we propose a novel deep neural
network based learning framework that affords perceptive insights into the
relationship between the MI-EEG data and brain activities. We design a joint
convolutional recurrent neural network that simultaneously learns robust
high-level feature presentations through low-dimensional dense embeddings from
raw MI-EEG signals. We also employ an Autoencoder layer to eliminate various
artifacts such as background activities. The proposed approach has been
evaluated extensively on a large- scale public MI-EEG dataset and a limited but
easy-to-deploy dataset collected in our lab. The results show that our approach
outperforms a series of baselines and the competitive state-of-the- art
methods, yielding a classification accuracy of 95.53%. The applicability of our
proposed approach is further demonstrated with a practical BCI system for
typing.Comment: 10 page
Self-supervised Learning for Electroencephalogram: A Systematic Survey
Electroencephalogram (EEG) is a non-invasive technique to record
bioelectrical signals. Integrating supervised deep learning techniques with EEG
signals has recently facilitated automatic analysis across diverse EEG-based
tasks. However, the label issues of EEG signals have constrained the
development of EEG-based deep models. Obtaining EEG annotations is difficult
that requires domain experts to guide collection and labeling, and the
variability of EEG signals among different subjects causes significant label
shifts. To solve the above challenges, self-supervised learning (SSL) has been
proposed to extract representations from unlabeled samples through
well-designed pretext tasks. This paper concentrates on integrating SSL
frameworks with temporal EEG signals to achieve efficient representation and
proposes a systematic review of the SSL for EEG signals. In this paper, 1) we
introduce the concept and theory of self-supervised learning and typical SSL
frameworks. 2) We provide a comprehensive review of SSL for EEG analysis,
including taxonomy, methodology, and technique details of the existing
EEG-based SSL frameworks, and discuss the difference between these methods. 3)
We investigate the adaptation of the SSL approach to various downstream tasks,
including the task description and related benchmark datasets. 4) Finally, we
discuss the potential directions for future SSL-EEG research.Comment: 35 pages, 12 figure
Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis
An electroencephalography (EEG) based brain activity recognition is a
fundamental field of study for a number of significant applications such as
intention prediction, appliance control, and neurological disease diagnosis in
smart home and smart healthcare domains. Existing techniques mostly focus on
binary brain activity recognition for a single person, which limits their
deployment in wider and complex practical scenarios. Therefore, multi-person
and multi-class brain activity recognition has obtained popularity recently.
Another challenge faced by brain activity recognition is the low recognition
accuracy due to the massive noises and the low signal-to-noise ratio in EEG
signals. Moreover, the feature engineering in EEG processing is time-consuming
and highly re- lies on the expert experience. In this paper, we attempt to
solve the above challenges by proposing an approach which has better EEG
interpretation ability via raw Electroencephalography (EEG) signal analysis for
multi-person and multi-class brain activity recognition. Specifically, we
analyze inter-class and inter-person EEG signal characteristics, based on which
to capture the discrepancy of inter-class EEG data. Then, we adopt an
Autoencoder layer to automatically refine the raw EEG signals by eliminating
various artifacts. We evaluate our approach on both a public and a local EEG
datasets and conduct extensive experiments to explore the effect of several
factors (such as normalization methods, training data size, and Autoencoder
hidden neuron size) on the recognition results. The experimental results show
that our approach achieves a high accuracy comparing to competitive
state-of-the-art methods, indicating its potential in promoting future research
on multi-person EEG recognition.Comment: 10 page
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