DCLL—A Deep Network for Possible Real-Time Decoding of Imagined Words

Panachakel, JT and Ramakrishnan, AG (2023) DCLL—A Deep Network for Possible Real-Time Decoding of Imagined Words. In: 7th International Symposium on Intelligent Informatics, ISI 2022, 31 August - 2 September 2022, Thiruvananthapuram, pp. 3-12.

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Abstract

We present a novel architecture for classifying imagined words from electroencephalogram (EEG) captured during speech imagery. The proposed architecture employs a sliding window with overlap for data augmentation (DA) and common spatial pattern (CSP) in order to derive the features. The dimensionality of features is reduced using linear discriminant analysis (LDA). Long short-term memory (LSTM) along with majority voting is used as the classifier. We call the proposed architecture the DCLL (DA-CSP-LDA-LSTM) architecture. On a publicly available imagined word dataset, the DCLL architecture achieves an accuracy of 85.2% for classifying the imagined words “in” and “cooperate”. Although this is around 7% less than the best result in the literature on this dataset, the DCLL architecture is roughly 300 times faster than the latter, making it a potential candidate for imagined word-based online BCI systems where the EEG signal needs to be classified in real time.

Item Type:	Conference Paper
Publication:	Smart Innovation, Systems and Technologies
Publisher:	Springer Science and Business Media Deutschland GmbH
Additional Information:	The copyright for this article belongs to Springer Science and Business Media Deutschland GmbH.
Keywords:	Brain computer interface; Discriminant analysis; Electroencephalography; Network architecture; Online systems; Real time systems, Csp; Data augmentation; Eeg; Imagined speech; Imagined word; Lda; Lstm; Online bci; Real- time; Speech imagery, Long short-term memory
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	19 May 2023 07:16
Last Modified:	19 May 2023 07:16
URI:	https://eprints.iisc.ac.in/id/eprint/81573

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