Abstract
Electroencephalography (EEG) analysis is used in neuroscience and commercial models. Machine learning algorithms have historically been used to discover important knowledge of classification. Access to large EEG datasets in recent years has led to the use of deep learning models, particularly in the EEG signal analysis. The automatic classification of EEG signals with the help of Deep Learning is one of the changing points in EEG analysis. While using machine learning algorithms we had to handpick the features, which is now not required using deep learning. EEG classification tasks are done using convolutional neural networks and recurrent neural networks. EEG signals of 32 participants were recorded as each watched 40 music videos which were 1-minute long. Participants rated each video in terms of the levels of arousal, valence, like/dislike, and dominance. This paper gives detailed information on the dataset used, EEG preprocessing method, and also deep learning architecture. This paper also addresses specific recommendations for hyperparameter tuning.
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Acharya, D., Ahmed Sayyad, R., Dwivedi, P., Shaji, A., Sriram, P., Bhardwaj, A. (2021). EEG Signal Classification Using Deep Learning. In: Tiwari, A., Ahuja, K., Yadav, A., Bansal, J.C., Deep, K., Nagar, A.K. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1392 . Springer, Singapore. https://doi.org/10.1007/978-981-16-2709-5_30
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DOI: https://doi.org/10.1007/978-981-16-2709-5_30
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