Abstract
Brain signals reflect the different psychological states and conditions of the human being. These signals can be recorded using the sensing mechanisms like EEG. EEG is the group of frequencies that ranges from 0.1 to 64 Hz. These bands indicate different mental states and activities. Hence, on separating these bands, particular signal pattern can be identified for the selected activity. Also, the band separation can be used for removing the noise from EEG signals. This paper focuses on different methods for separating EEG signal rhythms. In this paper, temporal filtering, wavelet-based filtering and empirical mode decompositions are used to realize the separation of EEG signal bands. From the selected methods, it is found that EMD is more promising.
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Dongare, S., Padole, D. (2022). Implementation of Different Methods for Decomposing the Rhythms of EEG Signal. In: Joshi, A., Mahmud, M., Ragel, R.G., Thakur, N.V. (eds) Information and Communication Technology for Competitive Strategies (ICTCS 2020). Lecture Notes in Networks and Systems, vol 191. Springer, Singapore. https://doi.org/10.1007/978-981-16-0739-4_46
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DOI: https://doi.org/10.1007/978-981-16-0739-4_46
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