Deep learning for electroencephalogram (EEG) classification tasks: a review
| Autor: | Yongtian He, Jose L. Contreras-Vidal, Alexander Craik |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
0206 medical engineering Biomedical Engineering 02 engineering and technology Electroencephalography Machine learning computer.software_genre Convolutional neural network 03 medical and health sciences Cellular and Molecular Neuroscience Deep belief network Deep Learning 0302 clinical medicine medicine Animals Humans Artificial neural network medicine.diagnostic_test business.industry Deep learning Brain Neural engineering Perceptron 020601 biomedical engineering ComputingMethodologies_PATTERNRECOGNITION Recurrent neural network Brain-Computer Interfaces Neural Networks Computer Artificial intelligence business computer Psychomotor Performance 030217 neurology & neurosurgery |
| Zdroj: | Journal of Neural Engineering. 16:031001 |
| ISSN: | 1741-2552 1741-2560 |
| DOI: | 10.1088/1741-2552/ab0ab5 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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