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Diego Fabian Collazos Huertas
Universidad Nacional de Colombia Sede Manizales
Corresponding Author
ORCiD: https://orcid.org/0000-0002-0434-3444
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View the published article at Brain Informatics.
Interpretation of brain activity responses using Motor Imagery (MI) paradigms is vital for medical diagnosis and monitoring. Assessed by machine learning techniques, identification of imagined actions is hindered by substantial intra and inter subject variability. Here, we develop an architecture of Convolutional Neural Networks (CNN) with enhanced interpretation of the spatial brain neural patterns that mainly contribute to the classification of MI tasks. Two methods of 2D-feature extraction from EEG data are contrasted: Power Spectral Density and Continuous Wavelet Transform. For preserving the spatial interpretation of extracting EEG patterns, we project the multi-channel data using a topographic interpolation. Besides, we include a spatial dropping algorithm to remove the learned weights that reflect the localities not engaged with the elicited brain response. Obtained results in a bi-task MI database show that the thresholding strategy in combination with Continuous Wavelet Transform improves the accuracy and enhances the interpretability of CNN architecture, showing that the highest contribution clusters over the sensorimotor cortex with differentiated behavior between μ and β rhythms.
Keywords
Motor imagery, convolutional neural networks, spatial dropping
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CURRENT STATUS: Published
Version 1
Posted 21 May, 2020
Published
On 03 Sep, 2020
No community comments so far
Editorial decision: Minor revision
On 28 Jul, 2020
Review #2 received
Received 17 Jul, 2020
Review #1 received
Received 19 Jun, 2020
Reviewer #2 agreed
On 18 Jun, 2020
Reviewers invited
Invitations sent on 22 May, 2020
Reviewer #1 agreed
On 22 May, 2020
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On 15 May, 2020
Editor invited
On 15 May, 2020
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Subject Areas
Computational Neuroscience
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This preprint is under consideration at Brain Informatics. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Diego Fabian Collazos Huertas
Universidad Nacional de Colombia Sede Manizales
Corresponding Author
ORCiD: https://orcid.org/0000-0002-0434-3444
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
Version 1
Posted 21 May, 2020
Published
On 03 Sep, 2020
No community comments so far
Editorial decision: Minor revision
On 28 Jul, 2020
Review #2 received
Received 17 Jul, 2020
Review #1 received
Received 19 Jun, 2020
Reviewer #2 agreed
On 18 Jun, 2020
Reviewers invited
Invitations sent on 22 May, 2020
Reviewer #1 agreed
On 22 May, 2020
Editor assigned
On 16 May, 2020
Submission checks complete
On 15 May, 2020
Editor invited
On 15 May, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Computational Neuroscience
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Brain Informatics.
Interpretation of brain activity responses using Motor Imagery (MI) paradigms is vital for medical diagnosis and monitoring. Assessed by machine learning techniques, identification of imagined actions is hindered by substantial intra and inter subject variability. Here, we develop an architecture of Convolutional Neural Networks (CNN) with enhanced interpretation of the spatial brain neural patterns that mainly contribute to the classification of MI tasks. Two methods of 2D-feature extraction from EEG data are contrasted: Power Spectral Density and Continuous Wavelet Transform. For preserving the spatial interpretation of extracting EEG patterns, we project the multi-channel data using a topographic interpolation. Besides, we include a spatial dropping algorithm to remove the learned weights that reflect the localities not engaged with the elicited brain response. Obtained results in a bi-task MI database show that the thresholding strategy in combination with Continuous Wavelet Transform improves the accuracy and enhances the interpretability of CNN architecture, showing that the highest contribution clusters over the sensorimotor cortex with differentiated behavior between μ and β rhythms.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
This preprint is available for download as a PDF.
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