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A deep neural network approach for P300 detection-based BCI using single-channel EEG scalogram images

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Abstract

Brain–computer interfaces (BCIs) acquire electroencephalogram (EEG) signals and interpret them into a command that helps people with severe motor disabilities using single channel. The goal of BCI is to achieve a prototype that supports disabled people to develop the relevant function. Various studies have been implemented in the literature to achieve a superior design using multi-channel EEG signals. This paper proposed a novel framework for the automatic P300 detection-based BCI model using a single EEG electrode. In the present study, we introduced a denoising approach using the bandpass filter technique followed by the transformation of scalogram images using continuous wavelet transform. The derived images were trained and validated using a deep neural network based on the transfer learning approach. This paper presents a BCI model based on the deep network that delivers higher performance in terms of classification accuracy and bitrate for disabled subjects using a single-channel EEG signal. The proposed P300 based BCI model has the highest average information transfer rates of 13.23 to 26.48 bits/min for disabled subjects. The classification performance has shown that the deep network based on the transfer learning approach can offer comparable performance with other state-of-the-art-method.

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Acknowledgements

We acknowledge the Eureka career Project with [Ref: NECBH/2019-20/177] under North East Centre for Biological Sciences and Healthcare Engineering (NECBH) Twinning Outreach Programme hosted by Indian Institute of Technology Guwahati (IITG), Guwahati, Assam funded by the Department of Biotechnology (DBT), Ministry of Science and Technology, Govt. of India with number BT/COE/34/SP28408/2018 for providing necessary financial support.

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Authors and Affiliations

  1. Tripura University, Agartala, India

    Sinam Ajitkumar Singh & Swanirbhar Majumder

  2. National Chiao Tung University, Hsinchu, Taiwan

    Takhellambam Gautam Meitei

  3. Regional Institute of Medical Sciences, Imphal, India

    Ningthoujam Dinita Devi

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  1. Sinam Ajitkumar Singh
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  2. Takhellambam Gautam Meitei
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  3. Ningthoujam Dinita Devi
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  4. Swanirbhar Majumder
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Correspondence to Sinam Ajitkumar Singh.

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This article does not contain any studies with human participants performed by any of the authors. However, the data were collected by EPFL, which is available for the researchers to work-upon [1].

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Singh, S.A., Meitei, T.G., Devi, N.D. et al. A deep neural network approach for P300 detection-based BCI using single-channel EEG scalogram images. Phys Eng Sci Med 44, 1221–1230 (2021). https://doi.org/10.1007/s13246-021-01057-4

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