A BRUTE-FORCE CNN MODEL SELECTION FOR ACCURATE CLASSIFICATION OF SENSORIMOTOR RHYTHMS IN BCIS
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Date
2020-06-09
Authors
Abibullaev, Berdakh
Dolzhikova, Irina
Zollanvari, Amin
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers
Abstract
The ultimate goal of Brain-Computer Interface (BCI) research is to enable individuals to
interact with their environment by translating their mental imagery. In this regard, a salient issue is the
identification of brain activity patterns that can be used to classify intention. Using Electroencephalographic
(EEG) signals as archetypical, this classification problem generally possesses two stages: (i) extracting
features from collected EEG waveforms; and (ii) constructing a classifier using extracted features. With
the advent of deep learning, however, the former stage is generally absorbed into the latter. Nevertheless,
the burden has now shifted from trying a number of feature extraction methods to tuning a large number of
hyperparameters and architectures. Among existing deep learning architectures used in BCI, Convolutional
Neural Networks (CNN) have become an attractive choice. Most of the existing studies that use these
networks are based on well-known architectures such as AlexNet or ResNet, use the domain knowledge
to construct the final architecture or have an unclear strategy deployed for model selection. This raises the
question as to whether constructing accurate CNN-based classifiers is possible using a principled model
selection, with the most straightforward one being the brute-force search or, alternatively, experience and
developing high intuition regarding hyperparameters combined with an ad hoc approach is the most prudent
way to go about designing them. To this end, in this paper, we first define a space of hyperparameters
restricted by our computing power. Then we show that an exhaustive search within this limited space of CNN
hyperparameters leads to accurate classification of sensorimotor rhythms that arise during motor imagery
tasks.
Description
Keywords
Brain-computer interfaces, motor imagery, deep learning, convolutional neural network, model selection, Research Subject Categories::TECHNOLOGY, brute-force search, sensory-motor rhythms
Citation
Abibullaev, B., Dolzhikova, I., & Zollanvari, A. (2020). A Brute-Force CNN Model Selection for Accurate Classification of Sensorimotor Rhythms in BCIs. IEEE Access, 8, 101014–101023. https://doi.org/10.1109/access.2020.2997681