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An end-to-end CNN with attentional mechanism applied to raw EEG in a BCI classification task

Lashgari, Elnaz and Ott, Jordan and Connelly, Akima and Baldi, Pierre and Maoz, Uri (2021) An end-to-end CNN with attentional mechanism applied to raw EEG in a BCI classification task. Journal of Neural Engineering, 18 (4). Art. No. 0460e3. ISSN 1741-2560. doi:10.1088/1741-2552/ac1ade. https://resolver.caltech.edu/CaltechAUTHORS:20210901-154524895

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Abstract

Objective. Motor-imagery (MI) classification base on electroencephalography (EEG) has been long studied in neuroscience and more recently widely used in healthcare applications such as mobile assistive robots and neurorehabilitation. In particular, EEG-based MI classification methods that rely on convolutional neural networks (CNNs) have achieved relatively high classification accuracy. However, naively training CNNs to classify raw EEG data from all channels, especially for high-density EEG, is computationally demanding and requires huge training sets. It often also introduces many irrelevant input features, making it difficult for the CNN to extract the informative ones. This problem is compounded by a dearth of training data, which is particularly acute for MI tasks, because these are cognitively demanding and thus fatigue inducing. Approach. To address these issues, we proposed an end-to-end CNN-based neural network with attentional mechanism together with different data augmentation (DA) techniques. We tested it on two benchmark MI datasets, brain–computer interface (BCI) competition IV 2a and 2b. In addition, we collected a new dataset, recorded using high-density EEG, and containing both MI and motor execution (ME) tasks, which we share with the community. Main results. Our proposed neural-network architecture outperformed all state-of-the-art methods that we found in the literature, with and without DA, reaching an average classification accuracy of 93.6% and 87.83% on BCI 2a and 2b, respectively. We also directly compare decoding of MI and ME tasks. Focusing on MI classification, we find optimal channel configurations and the best DA techniques as well as investigate combining data across participants and the role of transfer learning. Significance. Our proposed approach improves the classification accuracy for MI in the benchmark datasets. In addition, collecting our own dataset enables us to compare MI and ME and investigate various aspects of EEG decoding critical for neuroscience and BCI.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1088/1741-2552/ac1adeDOIArticle
ORCID:
AuthorORCID
Lashgari, Elnaz0000-0002-2290-2131
Connelly, Akima0000-0001-6480-1278
Maoz, Uri0000-0002-7899-1241
Additional Information:© 2021 IOP Publishing Ltd. Received 4 June 2021; Accepted 5 August 2021; Published 25 August 2021.
Funders:
Funding AgencyGrant Number
Fetzer Institute4189.00
John Templeton Foundation61283
Fundação BIAL251/20
Issue or Number:4
DOI:10.1088/1741-2552/ac1ade
Record Number:CaltechAUTHORS:20210901-154524895
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210901-154524895
Official Citation:Elnaz Lashgari et al 2021 J. Neural Eng. 18 0460e3
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:110690
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:01 Sep 2021 17:40
Last Modified:01 Sep 2021 17:40

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