Decoding imagined speech using wavelet features and deep neural networks

Panachakel, JT and Ramakrishnan, AG and Ananthapadmanabha, TV (2019) Decoding imagined speech using wavelet features and deep neural networks. In: 2019 IEEE 16th India Council International Conference (INDICON), 13-15 Dec. 2019, Rajkot, India.

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Abstract

This paper proposes a novel approach that uses deep neural networks for classifying imagined speech, significantly increasing the classification accuracy. The proposed approach employs only the EEG channels over specific areas of the brain for classification, and derives distinct feature vectors from each of those channels. This gives us more data to train a classifier, enabling us to use deep learning approaches. Wavelet and temporal domain features are extracted from each channel. The final class label of each test trial is obtained by applying a majority voting on the classification results of the individual channels considered in the trial. This approach is used for classifying all the 11 prompts in the KaraOne dataset of imagined speech. The proposed architecture and the approach of treating the data have resulted in an average classification accuracy of 57.15, which is an improvement of around 35 over the state- of-the-art results.

Item Type:	Conference Paper
Publication:	2019 IEEE 16th India Council International Conference, INDICON 2019 - Symposium Proceedings
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	cited By 0; Conference of 16th IEEE India Council International Conference, INDICON 2019 ; Conference Date: 13 December 2019 Through 15 December 2019; Conference Code:158465
Keywords:	Deep learning; Deep neural networks, Classification accuracy; Classification results; Feature vectors; Learning approach; Proposed architectures; State of the art; Temporal domain; Wavelet features, Classification (of information)
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	21 Sep 2020 10:30
Last Modified:	21 Sep 2020 10:30
URI:	http://eprints.iisc.ac.in/id/eprint/65201

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