Effective SSVEP Frequency Pair Selection over the GoogLeNet Deep Convolutional Neural Network

Abstract

The acquired Electroencephalography (EEG) signal while applying a blinking image on a screen is called steady-state visually-evoked potential (SSVEP). SSVEP is a popular control signal of the EEG in real-life applications because of the advantages such as; higher information transfer rate, simplicity in structure, and short training time. Most of the studies related to the SSVEP tried to discriminate which image (frequency) is gazed at while recording and turn this frequency into control commands. In this study, we focused on the selection of the stimulating frequency pair, which has the best accuracy rate, to investigate whether there is a correlation between stimulation frequencies. To achieve this goal, first of all, recorded SSVEP signals, which include seven different frequencies (6 - 6.5 - 7 7.5 - 8.2 - 9.3 - 10 Hz) were converted into spectrogram images. After dividing the spectrogram images into folders with respect to the frequencies, they were routed to GoogLeNet deep learning algorithm for binary classification. Consequently, we obtained the best performance in 8.2 & 10 Hz frequency pairs with 91.28% accuracy.