A method for assessing separability of EEG signals associated with three classes of brain activity is proposed. The EEG signals are acquired from 23 subjects, gathered from a headset consisting of 14 electrodes. Data are processed by applying Discrete Wavelet Transform (DWT) for the signal analysis and an autoencoder neural network for the brain activity separation. Processing involves 74 wavelets from 3 DWT families: Coiflets, Daubechies and Symlets. Euclidean distance between clusters normalized with respect to the standard deviation of the whole set of data are used to separate each task performed by participants. The results of this stage allow for an assessment of separability between subsets of data associated with each activity performed by experiment participants. The speed of convergence of the training process employing deep learning-based clustering is also measured.
Authors
Additional information
- DOI
- Digital Object Identifier link open in new tab 10.3233/fi-2019-1831
- Category
- Publikacja w czasopiśmie
- Type
- artykuły w czasopismach
- Language
- angielski
- Publication year
- 2019
