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

Avci, Meryem Beyza; Sayılgan, Ebru

doi:10.1109/tiptekno56568.2022.9960170

Cited by 3 publications

(2 citation statements)

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“…Other than designing CNN models from scratch, some researchers choose to modify existing CNN models used in computer vision to classify the SSVEP signal. Avci converted an SSVEP signal into a spectrogram and routed it to GoogLeNet deep learning model for binary classification [ 66 ]. Paula encoded EEG data to images using time-series imaging techniques and then used four 2D-kernel-based CNNs in the computer vision field, including ResNet, GoogLeNet, DenseNet and AlexNet, to classify the SSVEP signal [ 75 ].…”

Section: Discussionmentioning

confidence: 99%

“…This prevents the implementation of deep learning models in the computer vision area. However, in 2022, Avci demonstrated that by converting SSVEP signals into spectrograms, deep learning models in computer vision can be applied to SSVEP signal classification [ 66 ]. Avci’s work is inspirational and hopefully, in the future, by changing SSVEP signals into two-dimensional graph data, more models in the computer vision area can be implemented in SSVEP classification and demonstrated to be effective.…”

Section: Opening Challenges and Future Directionsmentioning

confidence: 99%

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An Analysis of Deep Learning Models in SSVEP-Based BCI: A Survey

Tang

et al. 2023

Brain Sciences

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The brain–computer interface (BCI), which provides a new way for humans to directly communicate with robots without the involvement of the peripheral nervous system, has recently attracted much attention. Among all the BCI paradigms, BCIs based on steady-state visual evoked potentials (SSVEPs) have the highest information transfer rate (ITR) and the shortest training time. Meanwhile, deep learning has provided an effective and feasible solution for solving complex classification problems in many fields, and many researchers have started to apply deep learning to classify SSVEP signals. However, the designs of deep learning models vary drastically. There are many hyper-parameters that influence the performance of the model in an unpredictable way. This study surveyed 31 deep learning models (2011–2023) that were used to classify SSVEP signals and analyzed their design aspects including model input, model structure, performance measure, etc. Most of the studies that were surveyed in this paper were published in 2021 and 2022. This survey is an up-to-date design guide for researchers who are interested in using deep learning models to classify SSVEP signals.

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Section: Discussionmentioning

confidence: 99%

Section: Opening Challenges and Future Directionsmentioning

confidence: 99%