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Authors: Stuti Chug and Vandana Agarwal

Affiliation: Department of Computer Science and Information Systems, BITS Pilani, Pilani Campus, India

Keyword(s): Brain Computer Interface, Clustering, Radial Basis Function Neural Network, Particle Swarm Optimization.

Abstract: The EEG-based motor imagery task classification has been a challenge for researchers due to the complex nature of EEG data. Human thoughts are a complex combination of different body limb activations and it is difficult to capture only one thought at a time. The data belonging to different motor imagery thought classes are also not separable linearly. In this paper, a novel technique for efficient and improved motor imagery task classification is proposed. Two major issues in motor imagery task classification of EEG data are addressed - channel selection and radial basis function neural network centers. The channel selection is posed as a combinatorial problem and an evolutionary nature-inspired algorithm PSOCS is proposed to select the most informative and discriminative channels using the Particle Swarm Optimization algorithm. The features are extracted using the selected channels and are subjected to classification. In this paper, a self-evolving radial basis functions neural netw ork (SENN) is proposed based on sub-clusters within each motor imagery task class. The number, centers, and spread of hidden neurons are obtained by the k-means clustering algorithm. The proposed algorithm is validated using the benchmarked datasets BCI Competition IV 2a and BCI Competition IV 2b data set. The proposed technique outperforms some of the existing techniques and classifies the motor imagery tasks efficiently. (More)

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Paper citation in several formats:
Chug, S. and Agarwal, V. (2022). SENN: Self-evolving Neural Network to Recognize Motor Imagery Thought Patterns. In Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - NCTA; ISBN 978-989-758-611-8; ISSN 2184-3236, SciTePress, pages 349-358. DOI: 10.5220/0011526800003332

@conference{ncta22,
author={Stuti Chug. and Vandana Agarwal.},
title={SENN: Self-evolving Neural Network to Recognize Motor Imagery Thought Patterns},
booktitle={Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - NCTA},
year={2022},
pages={349-358},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011526800003332},
isbn={978-989-758-611-8},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - NCTA
TI - SENN: Self-evolving Neural Network to Recognize Motor Imagery Thought Patterns
SN - 978-989-758-611-8
IS - 2184-3236
AU - Chug, S.
AU - Agarwal, V.
PY - 2022
SP - 349
EP - 358
DO - 10.5220/0011526800003332
PB - SciTePress

- Science and Technology Publications, Lda.
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