Review of EEG Signal Classification

Chakole, Ashlesha R.; Barekar, Praful V.; Ambulkar, Rajeshree V.; Kamble, Shailesh D.

doi:10.1007/978-981-13-1747-7_11

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…The main objective is to define a proper classifier able to correlate the EEG signals with the related user's cognitive state and, Artificial Neural Network (ANN) appear one of the promising methodologies to be applied. In (Chakole et al, 2019), a review on EEG signals classification is proposed while (Yger et al, 2017) presents a review on BCI.…”

Section: Introductionmentioning

confidence: 99%

A BCI Driving System to Understand Brain Signals Related to Steering

Zero

Graffione

Bersani

et al. 2021

Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics

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In the last years, the manufactured vehicles were designed to focus on prevention of some risky situations caused by a human driver. The aim of this paper is to illustrate the design and implementation of a BCI system which can detect the arm movements by the EEG signal during a simulated driving session. The proposed approach to realize a classifier able to recognize the arm movement by EEG feature analysis is based on the consecutive application of a Time Delay Neural Network (TDNN) and a Pattern Recognition Neural Network (PRNN). Preliminary tests are shown on three different participants between 24 and 45 years old.

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Section: Introductionmentioning

confidence: 99%

A BCI Driving System to Understand Brain Signals Related to Steering

Zero

Graffione

Bersani

et al. 2021

Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics

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Automated Detection of Seizure and Nonseizure EEG Signals Using Two Band Biorthogonal Wavelet Filter Banks

Bhati¹,

Pachori

Sharma

et al. 2019

Series in BioEngineering

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The automated feature identification and classification of nonseizure and seizure electroencephalogram (EEG) is very useful for the diagnosis of epilepsy. In this chapter two band biorthogonal wavelet filter banks are used for classification of nonseizure and seizure EEG signals, and their classification accuracy has been evaluated. The energy or the bispectral phase entropies of the wavelet subbands can be used to discriminate nonseizure and seizure EEG signals. We compare the performance of energy measure and the bispectral phase entropies to discriminate EEG signals. We compare the classification accuracy of thirty biorthogonal filter banks with respect to the regularity orders of the synthesis and analysis of low pass filters and the number of wavelet decompositions. It is found that the energy measure performs better than the bispectral phase entropy for the cases for which the regularity order is greater than or equal to five independent of the wavelet decomposition level. For the fifth and sixth levels of wavelet decomposition, it is found that the energy measure always performed better than the bispectral phase entropy measure independent of the regularity of the filter bank. For the energy measure, the filter banks with higher regularity orders are found to perform better than the filter banks with lower regularity orders at almost all the decomposition levels. However, for the bispectral phase entropy measure, the filter banks with lower regularity orders are

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