2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 16 , Issue 1 , PP: 37-51, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
P. Radhakrishnan 1 , Abullaıs Nehal Ahmed 2 , K. Kalaiarasi 3 * , Koppisetti Giridhar 4 , S. Thenappan 5
Doi: https://doi.org/10.54216/FPA.160103
Brain-computer interface (BCI) is a procedure of connecting the central nervous system to the device. In the past few years, BCI was conducted by Electroencephalography (EEG). By linking EEG with other neuro imaging technologies like functional Near Infrared Spectroscopy (fNIRS), promising outcomes were attained. An important stage of BCI is brain state identification from verified signal properties. Classifying EEG signals for motor imagery (MI) is a common use in the BCI system. Motor imagery includes imagining the movement of certain body parts without executing the physical movement. Deep Artificial Neural Network (DNN) obtained unprecedented complex classification outcomes. Such performances were obtained by an effective learning algorithm, improved computation power, restricted or back-fed neuron connection, and valuable activation function. Therefore, this study develops a Gazelle Optimization Algorithm with Deep Learning based Motor-Imagery Classification (GOADL-MIC) technique for EEG-Based BCI. The GOADL-MIC technique aims to exploit hyperparameter-tuned DL model for the recognition and identification of MI signals. To achieve this, the GOADL-MIC model initially undergoes the conversion of one dimensional-EEG signals into 2D time-frequency amplitude one. Besides, the EfficientNet-B3 system is applied for the effectual derivation of feature vector and its hyperparameters can be selected by using GOA. Finally, the classification of MIs takes place using bi-directional long short-term memory (Bi-LSTM). The experimentation result analysis of the GOADL-MIC method is verified utilizing the BCI dataset and the results demonstrate the promising results of the GOADL-MIC algorithm over its counter techniques
Brain-computer interfaces , EEG signals , Deep learning , Motor imagery signals , Image classification
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