3,351 research outputs found
A large-scale evaluation framework for EEG deep learning architectures
EEG is the most common signal source for noninvasive BCI applications. For
such applications, the EEG signal needs to be decoded and translated into
appropriate actions. A recently emerging EEG decoding approach is deep learning
with Convolutional or Recurrent Neural Networks (CNNs, RNNs) with many
different architectures already published. Here we present a novel framework
for the large-scale evaluation of different deep-learning architectures on
different EEG datasets. This framework comprises (i) a collection of EEG
datasets currently including 100 examples (recording sessions) from six
different classification problems, (ii) a collection of different EEG decoding
algorithms, and (iii) a wrapper linking the decoders to the data as well as
handling structured documentation of all settings and (hyper-) parameters and
statistics, designed to ensure transparency and reproducibility. As an
applications example we used our framework by comparing three publicly
available CNN architectures: the Braindecode Deep4 ConvNet, Braindecode Shallow
ConvNet, and two versions of EEGNet. We also show how our framework can be used
to study similarities and differences in the performance of different decoding
methods across tasks. We argue that the deep learning EEG framework as
described here could help to tap the full potential of deep learning for BCI
applications.Comment: 7 pages, 3 figures, final version accepted for presentation at IEEE
SMC 2018 conferenc
Multiattention Adaptation Network for Motor Imagery Recognition
This work was supported in part by the National Natural Science Foundation of China under Grants Nos. 61873181 and 61922062Peer reviewedPostprin
LMDA-Net:A lightweight multi-dimensional attention network for general EEG-based brain-computer interface paradigms and interpretability
EEG-based recognition of activities and states involves the use of prior
neuroscience knowledge to generate quantitative EEG features, which may limit
BCI performance. Although neural network-based methods can effectively extract
features, they often encounter issues such as poor generalization across
datasets, high predicting volatility, and low model interpretability. Hence, we
propose a novel lightweight multi-dimensional attention network, called
LMDA-Net. By incorporating two novel attention modules designed specifically
for EEG signals, the channel attention module and the depth attention module,
LMDA-Net can effectively integrate features from multiple dimensions, resulting
in improved classification performance across various BCI tasks. LMDA-Net was
evaluated on four high-impact public datasets, including motor imagery (MI) and
P300-Speller paradigms, and was compared with other representative models. The
experimental results demonstrate that LMDA-Net outperforms other representative
methods in terms of classification accuracy and predicting volatility,
achieving the highest accuracy in all datasets within 300 training epochs.
Ablation experiments further confirm the effectiveness of the channel attention
module and the depth attention module. To facilitate an in-depth understanding
of the features extracted by LMDA-Net, we propose class-specific neural network
feature interpretability algorithms that are suitable for event-related
potentials (ERPs) and event-related desynchronization/synchronization
(ERD/ERS). By mapping the output of the specific layer of LMDA-Net to the time
or spatial domain through class activation maps, the resulting feature
visualizations can provide interpretable analysis and establish connections
with EEG time-spatial analysis in neuroscience. In summary, LMDA-Net shows
great potential as a general online decoding model for various EEG tasks.Comment: 20 pages, 7 Figure
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Trends in Machine Learning and Electroencephalogram (EEG): A Review for Undergraduate Researchers
This paper presents a systematic literature review on Brain-Computer
Interfaces (BCIs) in the context of Machine Learning. Our focus is on
Electroencephalography (EEG) research, highlighting the latest trends as of
2023. The objective is to provide undergraduate researchers with an accessible
overview of the BCI field, covering tasks, algorithms, and datasets. By
synthesizing recent findings, our aim is to offer a fundamental understanding
of BCI research, identifying promising avenues for future investigations.Comment: 14 pages, 1 figure, HCI International 2023 Conferenc
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