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Imagined word pairs recognition from non-invasive brain signals using Hilbert transform

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Abstract

With the advent of new algorithms, brain-computer interfacing has been extensively used in medical and non-medical fields. In this regard, an experiment was conducted by the authors to recognize the imagined speech, the results of which are reported in this paper. This work can act as a speech prosthesis for completely paralyzed patients who cannot communicate normally. Thirteen subjects imagined five English words (sos, stop, medicine, comehere, washroom) while their electroencephalogram (EEG) signals were recorded simultaneously. The word pairs were analyzed in six natural frequencies of the brain. The envelopes of analytical signals acquired from Hilbert transform were calculated for all the frequency bands and the resulting features were classified using seven classifiers. The maximum accuracy reached up to 88.36%. The experimental study showed that alpha and theta frequency bands were able to classify the highest amount of imagined speech with a maximum average accuracy of 72.73% and 69.41% respectively. The results were comparable to state-of-the-art methods. The findings reported in this work will encourage the research community to use non-invasive modalities like EEG for exploring more in this area.

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  1. Department of Electronics and Communication Engineering, National Institute of Technology, Delhi, India

    Prabhakar Agarwal & Sandeep Kumar

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  1. Prabhakar Agarwal
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  2. Sandeep Kumar
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Correspondence to Sandeep Kumar.

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The subjects voluntarily participated and submitted written consent for the experiment. Their identity was maintained confidential throughout the work. The approval of the work was taken from the competent authority of the institute. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration.

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Agarwal, P., Kumar, S. Imagined word pairs recognition from non-invasive brain signals using Hilbert transform. Int J Syst Assur Eng Manag 13, 385–394 (2022). https://doi.org/10.1007/s13198-021-01283-9

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  • DOI: https://doi.org/10.1007/s13198-021-01283-9

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