Deep learning for electroencephalogram (EEG) classification tasks: a review

By Alexander Craik, Yongtian He, José Luis Contreras-Vidal. Journal of Neural Engineering, 2019

1,577 citations recorded by the source corpus.

Objective. Electroencephalography (EEG) analysis has been an important tool in neuroscience with applications in neuroscience, neural engineering (e.g. Brain–computer interfaces, BCI’s), and even commercial applications. Many of the analytical tools used in EEG studies have used machine learning to uncover relevant information for neural classification and neuroimaging. Recently, the availability of large EEG data sets and advances in machine learning have both led to the deployment of deep learning architectures, especially in the analysis of EEG signals and in understanding the information it may contain for brain functionality. The robust automatic classification of these signals is an important step towards making the use of EEG more practical in many applications and less reliant on trained professionals. Towards this goal, a systematic review of the literature on deep learning applications to EEG classification was performed to address the following critical questions: (1) Which EEG classification tasks have been explored with deep learning? (2) What input formulations have been used for training the deep networks? (3) Are there specific deep learning network structures suitable for specific types of tasks? Approach. A systematic literature review of EEG classification using deep learning was performed on Web of Science and PubMed databases, resulting in 90 identified studies. Those studies were analyzed based on type of task, EEG preprocessing methods, input type, and deep learning architecture. Main results. For EEG classification tasks, convolutional neural networks, recurrent neural networks, deep belief networks outperform stacked auto-encoders and multi-layer perceptron neural networks in classification accuracy. The tasks that used deep learning fell into five general groups: emotion recognition, motor imagery, mental workload, seizure detection, event related potential detection, and sleep scoring. For each type of task, we describe the specific input formulation, major characteristics, and end classifier recommendations found through this review. Significance. This review summarizes the current practices and performance outcomes in the use of deep learning for EEG classification. Practical suggestions on the selection of many hyperparameters are provided in the hope that they will promote or guide the deployment of deep learning to EEG datasets in future research.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/7f002c2059f0714b5423f084913e54a50fe1ac58

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Citations in the corpus

EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications (2020, background): “Although [32] believes that deep learning methods have not demonstrated convincing enhancement over some state-of-the-art BCI methods, the results reviewed recently in [33] illustrated that some deep learning methods, for instance, convolutional neural networks (CNNs), generative adversarial network”
Data Augmentation for Deep-Learning-Based Electroencephalography. (2020, background): “(This lacuna is discussed in other review papers [23].”
EEG-Based Emotion Recognition via Channel-Wise Attention and Self Attention (2023, background): “puters better understand the current emotional state of the user [21].”
Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review (2020, background): “The ability to acquire a robust automatic classiﬁcation of EEG signals is an essential step toward making the use of EEG more practical in many applications and less reliant on trained professionals (Alexander et al., 2018).”
Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review (2021, positive): “[37] specifically investigated the DLbased techniques.”
Epileptic Seizures Detection Using Deep Learning Techniques: A Review (2020, methodology): “DL models, unlike conventional machine learning techniques, require huge data in the training phase [29].”
EEG Based Emotion Recognition: A Tutorial and Review (2022, methodology): “[29] focuses on introducing the Deep Learning-based methodologies applied to EEG modeling.”