Biomedical Signal Analysis Using Entropy Measures: A Case Study of Motor Imaginary BCI in End Users with Disability

Heidari, Hanif

doi:10.1007/978-3-030-97845-7_8

Cited by 2 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here, we used 60,000 handwritten images for training and 10,000 handwritten images for testing the proposed entropy accuracy. Several studies have tested this method successfully [29,[31][32][33][34][35]. Li et al considered NNetEn as the characteristic of ship-radiated noise signals [31].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neural Network Entropy (NNetEn): Entropy-Based EEG Signal and Chaotic Time Series Classification, Python Package for NNetEn Calculation

et al. 2023

View full text Add to dashboard Cite

Entropy measures are effective features for time series classification problems. Traditional entropy measures, such as Shannon entropy, use probability distribution function. However, for the effective separation of time series, new entropy estimation methods are required to characterize the chaotic dynamic of the system. Our concept of Neural Network Entropy (NNetEn) is based on the classification of special datasets in relation to the entropy of the time series recorded in the reservoir of the neural network. NNetEn estimates the chaotic dynamics of time series in an original way and does not take into account probability distribution functions. We propose two new classification metrics: R2 Efficiency and Pearson Efficiency. The efficiency of NNetEn is verified on separation of two chaotic time series of sine mapping using dispersion analysis. For two close dynamic time series (r = 1.1918 and r = 1.2243), the F-ratio has reached the value of 124 and reflects high efficiency of the introduced method in classification problems. The electroencephalography signal classification for healthy persons and patients with Alzheimer disease illustrates the practical application of the NNetEn features. Our computations demonstrate the synergistic effect of increasing classification accuracy when applying traditional entropy measures and the NNetEn concept conjointly. An implementation of the algorithms in Python is presented.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Li et al considered NNetEn as the characteristic of ship-radiated noise signals [31]. Heidari used NNetEn of electroencephalogram (EEG) signals for classifying motor imaginary of end users with disability [32]. He found that eight channels are enough for the classification, while other existed methods considered 30 channels.…”

Section: Introductionmentioning

confidence: 99%