An Analysis of Traditional Methods and Deep Learning Methods in SSVEP-Based BCI: A Survey
Abstract
:1. Introduction
1.1. Literature Search and Inclusion Criteria
1.2. Contributions of This Survey
- (1)
- A detailed overview of the progress of SSVEP from paradigm to signal decoding and, finally, application is provided to help researchers better understand the various research progresses of SSVEP-BCI.
- (2)
- We analyze and summarize the current mainstream research direction and research content of SSVEP-BCI based on deep learning, and outline the development history of deep learning. The traditional KNN, MLP, and SVM classification algorithms and neural-network-based deep learning classification algorithms are described in detail, comparing and analyzing the advantages, disadvantages, and adaptation scenarios to provide design guidelines for researchers.
- (3)
- We point out SSVEP-BCI’s deficiencies and difficulties, the main breakthrough points in future research, and provide an outlook on the future direction of SSVEP-BCI application based on deep learning classification algorithms.
1.3. The Structure of This Survey
2. Research Progress of BCI Based on SSVEP
2.1. Research Progress of SSVEP Paradigm
2.2. Research Progress of Decoding Methods
2.3. Research Progress of System and Application
3. SSVEP-BCI Classification Algorithm Based on Deep Learning
3.1. The Development Process of Deep Learning
- Inception: From 1980, when the backpropagation (BP) algorithm was proposed, to 2006, the BP algorithm was proposed to make the training of neural networks simple and feasible [65]. However, due to the problems of neural network algorithms and the limitations of computer power, very few scientists were able to stay in the field. Shallow learning methods became the mainstream of the era, and algorithms such as KNN, MLP, and SVM have received widespread attention [66,67,68]. These shallow learning methods are achieving good results in practice, which makes deep neural networks unpopular.
- Rapid development period: From 2006 to 2012. In 2006, Hinton and his team [69,70] published a paper in Science, proposing a downscaling and layer-by-layer pretraining method that made the practical implementation of deep networks possible. In the same year, they also published an important paper proposing a solution to the problem of gradient disappearance during deep network training, and proposed the concept of “deep autoencoder”. Since then, researchers’ studies on neural networks began to enter the era of deep learning, and the curtain of the development of deep learning was opened. In 2012, Hinton et al. [71] made a breakthrough in research, proposing the “dropout” technology, which can effectively reduce the overfitting in deep learning, improve the generalization ability, and simplify the design of neural networks; the proposed technology has inspired the deep learning research boom.
- Explosion period: from 2012 to present. In 2012, Hinton’s student Krizhevsky et al. [72] used convolutional neural networks as the basis for ImageNet image recognition and achieved superior performance results to all traditional methods. From then on, convolutional neural networks began to shine in the field of computer vision. In 2014, Krizhevsky [73] proposed two parallelization methods, data parallelism and model parallelism, which significantly improved the training speed of convolutional neural networks through parallelized training and promoted the development of deep learning frameworks. In 2016, He et al. [74] proposed residual networks, which solved the problem of difficult training of deep networks and provided important ideas and methods. In 2017, Vaswani et al. [75] proposed the Transformer model for the first time, which revolutionized the field of deep learning, and not only improved the performance of deep learning models, but also greatly accelerated the training speed and inference speed of the model. In 2018, Devlin et al. [76] proposed the Bidirectional Encoder Representations from Transformers (BERT), a new language model, which has had a significant impact on the application of deep learning in the field of natural language. In 2019, Rejer et al. [77] minimized the number of channels by adjusting the number of channels for acquiring EEG signals while maintaining high accuracy, which greatly contributed to the research on SSVEP-BCI. In 2022, Du et al. [78] discussed in-depth research on SSVEP for augmented reality (AR) and provided a comparative analysis on the color selection of visual stimuli in AR-SSVEP for researchers using SSVEP.
3.2. Data Preprocessing
3.2.1. Frequency Filter
3.2.2. Time–Frequency Conversion
3.2.3. Filter Bank
3.3. Types and Layers of Network Architecture
3.3.1. Traditional Classification Algorithm
3.3.2. Deep Learning Classification Algorithms
3.3.3. Comparative Analysis of Classification Algorithms
4. Challenges and Future Directions of SSVEP-BCI
- The background noise of SSVEP signal data is relatively large, and external sound, light source interference, magnetic field, electric field, etc., may cause interference to the data acquisition process.
- The ITR of the SSVEP-BCI system has room for improvement. When the number of stimulus targets is certain, the information transfer rate mainly depends on the length of the recognition window of the classification recognition algorithm, and there are still very few SSVEP recognition algorithms that can achieve high efficiency with short time windows.
- Most current SSVEP-BCIs use low-frequency stimulus targets as the stimulus source, but prolonged use of low-frequency SSVEP-BCIs can fatigue users, and low-frequency SSVEP-BCIs increase the risk of inducing photosensitive epilepsy.
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Wu, J.; Wang, J. An Analysis of Traditional Methods and Deep Learning Methods in SSVEP-Based BCI: A Survey. Electronics 2024, 13, 2767. https://doi.org/10.3390/electronics13142767
Wu J, Wang J. An Analysis of Traditional Methods and Deep Learning Methods in SSVEP-Based BCI: A Survey. Electronics. 2024; 13(14):2767. https://doi.org/10.3390/electronics13142767
Chicago/Turabian StyleWu, Jiaxuan, and Jingjing Wang. 2024. "An Analysis of Traditional Methods and Deep Learning Methods in SSVEP-Based BCI: A Survey" Electronics 13, no. 14: 2767. https://doi.org/10.3390/electronics13142767
APA StyleWu, J., & Wang, J. (2024). An Analysis of Traditional Methods and Deep Learning Methods in SSVEP-Based BCI: A Survey. Electronics, 13(14), 2767. https://doi.org/10.3390/electronics13142767