Abstract
Electroencephalography (EEG) is a technique to quantitatively measure brain activity with high temporal resolution. EEG converts brain activity to time series data with amplitude on the y-axis, and this data can then be used to understand brain functions. Mathematical tools can be applied to this data to extract features and to discriminate them in several classes. Once EEG data is recorded, it is needed to make sense of that data. In the past couple of decades, EEG data has revolutionised the healthcare industry and brain–computer interface (BCI) systems. This is made possible by continuous improvements in EEG data classification methods, which includes improvements in feature extraction and classification algorithms. In this study, methods to classify EEG data for various applications such as medical diagnostics, BCI and emotion detection are reviewed.
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Jadhav, V., Tiwari, N., Chawla, M. (2023). A Review on EEG Data Classification Methods for Brain–Computer Interface. In: Gupta, D., Khanna, A., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Lecture Notes in Networks and Systems, vol 473. Springer, Singapore. https://doi.org/10.1007/978-981-19-2821-5_63
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