SENN: Self-evolving Neural Network to Recognize Motor Imagery Thought Patterns

Stuti Chug, Vandana Agarwal

2022

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 network (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.

Paper Citation

in Harvard Style

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) - Volume 1: NCTA; ISBN 978-989-758-611-8, SciTePress, pages 349-358. DOI: 10.5220/0011526800003332

in Bibtex Style

@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) - Volume 1: NCTA},
year={2022},
pages={349-358},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011526800003332},
isbn={978-989-758-611-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - Volume 1: NCTA
TI - SENN: Self-evolving Neural Network to Recognize Motor Imagery Thought Patterns
SN - 978-989-758-611-8
AU - Chug S.
AU - Agarwal V.
PY - 2022
SP - 349
EP - 358
DO - 10.5220/0011526800003332
PB - SciTePress