Open AccessAn Evaluation of Different Fast Fourier Transform - Transfer Learning Pipelines for the Classification of Wink-based EEG Signals
Author(s) -
Jothi Letchumy Mahendra Kumar,
Mamunur Rashid,
Rabiu Muazu Musa,
Mohd Azraai Mohd Razman,
Norizam Sulaiman,
Rozita Jailani,
Anwar Pp Abdul Majeed
Publication year - 2020
Publication title -
mekatronika : journal of intelligent manufacturing and mechatronics
Language(s) - English
Resource type - Journals
ISSN - 2637-0883
DOI - 10.15282/mekatronika.v2i1.5939
Subject(s) - brain–computer interface , computer science , electroencephalography , fast fourier transform , short time fourier transform , artificial intelligence , classifier (uml) , transfer of learning , pattern recognition (psychology) , feature extraction , random forest , spectrogram , hyperparameter optimization , fourier transform , machine learning , support vector machine , fourier analysis , mathematics , psychology , mathematical analysis , algorithm , psychiatry
Brain Computer-Interfaces (BCI) offers a means of controlling prostheses for neurological disorder patients, primarily owing to their inability to control such devices due to their inherent physical limitations. More often than not, the control of such devices exploits the use of Electroencephalogram (EEG) signals. Nonetheless, it is worth noting that the extraction of the features is often a laborious undertaking. The use of Transfer Learning (TL) has been demonstrated to be able to mitigate the issue. However, the employment of such a method towards BCI applications, particularly with regards to EEG signals are limited. The present study aims to assess the effectiveness of a number of DenseNet TL models, viz. DenseNet169, DenseNet121 and DenseNet201 in extracting features for the classification of wink-based EEG signals. The extracted features are then classified through an optimised Random Forest (RF) classifier. The raw EEG signals are transformed into a spectrogram image via Fast Fourier Transform (FFT) before it was fed into selected TL models. The dataset was split with a stratified ratio of 60:20:20 into train, test, and validation datasets, respectively. The hyperparameters of the RF model was optimised through the grid search approach that utilises the five-fold cross-validation technique. It was established from the study that amongst the DenseNet pipelines evaluated, the DenseNet169 performed the best with an overall validation and test accuracy of 89%. The findings of the present investigation could facilitate BCI applications, e.g., for a grasping exoskeleton.