Brain Decoding over the MEG Signals Using Riemannian Approach and Machine Learning

Authors : Zeynep ÖZER, Onursal ÇETİN, Kutlucan GÖRÜR, Feyzullah TEMURTAŞ

Pages : 207-218

Doi:10.17694/bajece.1144279

View : 93 | Download : 70

Publication Date : 2023-08-21

Article Type : Research Paper

Abstract :Brain decoding is an emerging approach for understanding the face perception mechanism in the human brain. Face visual stimuli and perception mechanism are considered as a challenging ongoing research of the neuroscience field. In this study, face/scrambled face visual stimulations were implemented over the sixteen participants to be decoded the face or scrambled face classification using machine learning insert ignore into journalissuearticles values(ML); algorithms via magnetoencephalography insert ignore into journalissuearticles values(MEG); signals. This noninvasive and high spatial/temporal resolution signal is a neurophysiological technique which measures the magnetic fields generated by the neuronal activity of the brain. The Riemannian approach was used as a highly promising feature extraction technique. Then Long Short-Term Memory insert ignore into journalissuearticles values(LSTM);, Gated Recurrent Unit insert ignore into journalissuearticles values(GRU);, Convolutional Neural Network insert ignore into journalissuearticles values(CNN); were employed as deep learning algorithms, Linear Discriminant Analysis insert ignore into journalissuearticles values(LDA); and Quadratic Discriminant Analysis insert ignore into journalissuearticles values(QDA); were implemented as shallow algorithms. The improved classification performances are very encouraging, especially for deep learning algorithms. The LSTM and GRU have achieved 92.99% and 91.66% accuracy and 0.977 and 0.973 of the area under the curve insert ignore into journalissuearticles values(AUC); scores, respectively. Moreover, CNN has yielded 90.62% accuracy. As our best knowledge, the improved outcomes and the usage of the deep learning on the MEG dataset signals from 16 participants are critical to expand the literature of brain decoding after visual stimuli. And this study is the first attempt with these methods in systematic comparison. Moreover, MEG-based Brain-Computer Interface insert ignore into journalissuearticles values(BCI); approaches may also be implemented for Internet of Things insert ignore into journalissuearticles values(IoT); applications, including biometric authentication, thanks to the specific stimuli of individual’s brainwaves.
Keywords : Magnetoencephalography, Brain Decoding, Riemannian Approach, Deep Learning