Pattern recognition for brain-computer interface on disabled subjects using a wavelet transformation

Bassani, Thiago; Nievola, Júlio César

doi:10.1109/cibcb.2008.4675776

Cited by 10 publications

(2 citation statements)

References 14 publications

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“…In order to decrease the amplitude of outliers, the amplitude of obtained signals from each electrode was calculated, from which those with amplitudes higher than 90% or lower than 10% were clipped and ultimately normalized. Numerous studies have used this database in the fields of signal processing and feature extraction [240,241], feature selection [239,242], and classifiers [243][244][245]. In the present study, the acquired signals from eight electrodes, namely P8, P4, P3, P7, Oz, Pz, Cz and Fz are used.…”

Section: Databasementioning

confidence: 99%

A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain–machine interface systems

2018

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Section: Databasementioning

confidence: 99%

A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain–machine interface systems

2018

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“…The wavelet transform [10] is suitable for analyzing transient signals, since it contains both frequency and time information. For spectral analysis, wavelet transform can be more suitable than Fourier transform [11] depending on the properties of the mother wavelet [7]. There are two types of wavelet analysis: continuous wavelet transform (CWT) and discrete wavelet transform (DWT).…”

Section: Introductionmentioning

confidence: 99%

Discrete wavelet transform EEG features of Alzheimer'S disease in activated states

Ghorbanian

Devilbiss

Simon³

et al. 2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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In this study, electroencephalogram (EEG) signals obtained by a single-electrode device from 24 subjects - 10 with Alzheimer's disease (AD) and 14 age-matched Controls (CN) - were analyzed using Discrete Wavelet Transform (DWT). The focus of the study is to determine the discriminating EEG features of AD patients while subjected to cognitive and auditory tasks, since AD is characterized by progressive impairments in cognition and memory. At each recording block, DWT extracts EEG features corresponding to major brain frequency bands. T-test and Kruskal-Wallis methods were used to determine the statistically significant features of EEG signals from AD patients compared to Controls. A decision tree algorithm was then used to identify the dominant features for AD patients. It was determined that the mean value of the low-δ (1 - 2 Hz) frequency band during the Paced Auditory Serial Addition Test with 2.0 (s) interval and the mean value of the δ frequency band (12 - 30 Hz) during 6 Hz auditory stimulation have higher mean values in AD patients than Controls. Due to artifacts, the less reliable low-δ features were removed and it was determined that the mean value of β frequency band during 6 Hz auditory stimulation followed by the standard deviation of θ (4 - 8 Hz) frequency band of one card learning cognitive task are higher for AD patients compared to Controls and thus the most dominant discriminating features of the disease.

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Mathematical Methods of Signal Processing in Neuroscience

Hramov

Короновский

Makarov

et al. 2021

Wavelets in Neuroscience

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Pattern recognition for brain-computer interface on disabled subjects using a wavelet transformation

Cited by 10 publications

References 14 publications

A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain–machine interface systems

A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain–machine interface systems

Discrete wavelet transform EEG features of Alzheimer'S disease in activated states

Mathematical Methods of Signal Processing in Neuroscience

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