Convolutional neural networks for decoding electroencephalography responses and visualizing trial by trial changes in discriminant features.

Aellen, Florence Marcelle; Göktepe, Pinar; Apostolopoulos, Stefanos; Tzovara, Athina (2021). Convolutional neural networks for decoding electroencephalography responses and visualizing trial by trial changes in discriminant features. Journal of neuroscience methods, 364, p. 109367. Elsevier 10.1016/j.jneumeth.2021.109367

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BACKGROUND

Deep learning has revolutionized the field of computer vision, where convolutional neural networks (CNNs) extract complex patterns of information from large datasets. The use of deep networks in neuroscience is mainly focused to neuroimaging or brain computer interface -BCI- applications. In electroencephalography (EEG) research, multivariate pattern analysis (MVPA) mainly relies on linear algorithms, which require a homogeneous dataset and assume that discriminant features appear at consistent latencies and electrodes across trials. However, neural responses may shift in time or space during an experiment, resulting in under-estimation of discriminant features. Here, we aimed at using CNNs to classify EEG responses to external stimuli, by taking advantage of time- and space- unlocked neural activity, and at examining how discriminant features change over the course of an experiment, on a trial by trial basis.

NEW METHOD

We present a novel pipeline, consisting of data augmentation, CNN training, and feature visualization techniques, fine-tuned for MVPA on EEG data.

RESULTS

Our pipeline provides high classification performance and generalizes to new datasets. Additionally, we show that the features identified by the CNN for classification are electrophysiologically interpretable and can be reconstructed at the single-trial level to study trial-by-trial evolution of class-specific discriminant activity.

COMPARISON WITH EXISTING TECHNIQUES

The developed pipeline was compared to commonly used MVPA algorithms like logistic regression and support vector machines, as well as to shallow and deep convolutional neural networks. Our approach yielded significantly higher classification performance than existing MVPA techniques (p = 0.006) and comparable results to other CNNs for EEG data.

CONCLUSION

In summary, we present a novel deep learning pipeline for MVPA of EEG data, that can extract trial-by-trial discriminative activity in a data-driven way.

Item Type:

Journal Article (Original Article)

Division/Institute:

08 Faculty of Science > Institute of Computer Science (INF) > Cognitive Computational Neuroscience (CCN)
04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurology
08 Faculty of Science > Institute of Computer Science (INF)

UniBE Contributor:

Aellen, Florence Marcelle, Göktepe, Pinar, Tzovara, Athina

Subjects:

000 Computer science, knowledge & systems
600 Technology > 610 Medicine & health
500 Science > 510 Mathematics

ISSN:

0165-0270

Publisher:

Elsevier

Language:

English

Submitter:

Chantal Kottler

Date Deposited:

06 Dec 2021 17:00

Last Modified:

13 Mar 2024 13:18

Publisher DOI:

10.1016/j.jneumeth.2021.109367

PubMed ID:

34563599

Additional Information:

Athina Tzovara has a double affiliation INF and Dept. of Neurology

Uncontrolled Keywords:

Classification Convolutional neural networks Deep learning Electroencephalography Feature extraction Multivariate pattern analysis

BORIS DOI:

10.48350/161894

URI:

https://boris.unibe.ch/id/eprint/161894

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