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An optimized artificial intelligence based technique for identifying motor imagery from EEGs for advanced brain computer interface technology

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Abstract

Motor disability affects a person's ability to move and maintain balance. To remove this pain from the society, brain computer interface (BCI) system with the assistance of motor imagery (MI) tasks classification plays an important role. BCI translates human intension by brain activity into control signals to communicate with their external environment without direct physical movement. The current BCI system works with massive data through electroencephalogram (EEG) signals. Traditional methods in BCI are limited in efficiency, accuracy, and speed. To overcome these limitations, this study aims to develop an optimized artificial Intelligence-based technique for identifying human intentions of physical movement through EEG data for an advanced BCI system. The proposed technique is designed involving Common Spatial Pattern (CSP) and Medium K Nearest Neighbour (MKNN) technique to achieve higher classifier accuracy, which was tested on a publicly available EEG dataset (IVa) of BCI Competition III. This study produces the highest accuracy score in the case of all subjects above 90%, and the average score is above 97% for our proposed model, which outperforms the existing methods. This study's findings will assist technologists in creating frontier medical science technology and significantly improve BCI systems in Australia and worldwide. This proposed technique will help to identify the intensions of motor disabled people rapidly, assisting patients’ rehabilitation and daily living.

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Authors and Affiliations

  1. Institute for Sustainable Industries and Liveable Cities, Victoria University, Ballarat Road, Melbourne, VIC, 3011, Australia

    Taslima Khanam, Siuly Siuly & Hua Wang

Authors
  1. Taslima Khanam
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  2. Siuly Siuly
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  3. Hua Wang
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Contributions

Conceptualization: SS, HW; Methodology: SS, TK; Formal analysis and investigation: TK, …; Writing—original draft preparation: TK; Writing—review and editing: SS, HW, TK; Funding acquisition: HW, SS; Supervision: SS, HW.

Corresponding author

Correspondence to Taslima Khanam.

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Conflict of interest

The authors declare that they do not have any kind of conflict of interest.

Ethical approval

This study applied secondary data which are publicly available online, on the following link: (http://www.bbci.de/competition/iii/desc_IVa.html). All the respondents engaged in this survey signed the consent paper and, they gave permission to use their personal details with confidentiality for research purpose. Our study project does not have any direct network with any participants. So, we do not need any ethical approval for conducting this research.

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Khanam, T., Siuly, S. & Wang, H. An optimized artificial intelligence based technique for identifying motor imagery from EEGs for advanced brain computer interface technology. Neural Comput & Applic 35, 6623–6634 (2023). https://doi.org/10.1007/s00521-022-08027-1

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  • DOI: https://doi.org/10.1007/s00521-022-08027-1

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