Left and Right Hand Movements EEG Signals Classification Using Wavelet Transform and Probabilistic Neural Network

Hossain, Arafat; Rahman, Md. Ashikur; Riheen, Manjurul Ahsan

doi:10.11591/ijece.v5i1.pp92-101

Cited by 16 publications

(13 citation statements)

References 21 publications

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“…The author attempt to categorize 2 various emotional states by signle chsnnel of EEG recordings in paper [29]. This work is the continuation of prior study [30] where beta-band was discovered compatible for the analysis of hand movement. The DWT has been utilized to separate the beta-band of EEG-signal to extract the features.…”

Section: Literature Surveymentioning

confidence: 99%

Improved feature exctraction process to detect seizure using CHBMIT-dataset

Kumar¹,

Narayanappa

2021

IJECE

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One of the most dangerous neurological disease, which is occupying worldwide, is epilepsy. Fraction of second nerves in the brain starts impulsion i.e. electrical discharge, which is higher than the normal pulsing. So many researches have done the investigation and proposed the numerous methodology. However, our methodology will give effective result in feature extraction. Moreover, we used numerous number of statistical moments features. Existing approaches are implemented on few statistical moments with respect to time and frequency. Our proposed system will give the way to find out the seizure-effected part of the brain very easily using TDS, FDS, Correlation and Graph presentation. The resultant value will give the huge difference between normal and seizure effected brain. It also explore the hidden features of the brain.

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Section: Literature Surveymentioning

confidence: 99%

Improved feature exctraction process to detect seizure using CHBMIT-dataset

Kumar¹,

Narayanappa

2021

IJECE

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“…For the study of EEG signals, many signal analysis and processing methods have recently been suggested [9][10][11][12][13][14]. Traditional Fourier spectral analysis was used among these methods to extract EEG signals for dementia detection [10].…”

Section: Introductionmentioning

confidence: 99%

Vascular dementia classification based on hilbert huang transform as feature extractor

Musa

Shapiai

Fauzi

et al. 2020

IJEECS

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Impairment of cognitive and working memory after stroke was common. Vascular dementia (VaD) was a prevalent type of dementia that was caused by an impaired blood supply to the brain because of a series of small strokes. Electroencephalogram (EEG) gives information about brain status and activity, so it had a lot of potential to be used in diagnosing people with dementia. Since the EEG signal is extremely non-linear and non-stationary data, traditional Fourier analysis such as Fast Fourier Transform (FFT) that broadens sinusoidal signals cannot describe the amplitude contribution of each frequency value in specific time. Meanwhile, Hilbert Huang Transform (HHT) was based on the characteristic local time scale of the signal, it can efficiently obtain instantaneous frequency and instantaneous amplitude for nonstationary and nonlinear data. In this paper, HHT was employed as feature extraction method to extract the energy features of frequency bands from post stroke patients and healthy subjects. The extracted features were fed into extreme learning machine (ELM) for classifying post stroke patient with VaD and healthy subjects. The results of classification accuracy using HHT as feature extractor and FFT as feature extractor were compared. The mean accuracy of classification using HHT was 59.14%, respectively, while mean accuracy of classification using FFT was 94.4%, respectively, in classifying post stroke patient with VaD and healthy subjects.

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“…The decoding function that links neural activity to behavior may be relatively unstable as well as degrading decoding performance. In [15], authors present right and left hand movements decoding by a probabilistic neural network using EEG signal and wavelet transform based on for classification and feature extraction. Perge et al evaluated in [16,17] the nature and extent of instability in spiking populations recorded in the context of an ongoing pilot clinical trial of people with tetraplegia: they found that systematic rate changes occur commonly and they can cause estimation errors in the decoded kinematic parameters leading to degraded performance that presents itself as a directional bias.…”

Section: Introductionmentioning

confidence: 99%

Tracking times in temporal patterns embodied in intra-cortical data for controling neural prosthesis an animal simulation study

Kifouche

Guessoum

2020

IJECE

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Brain-machines capture brain signals in order to restore communication and movement to disabled people who suffer from brain palsy or motor disorders. In brain regions, the ensemble firing of populations of neurons represents spatio-temporal patterns that are transformed into outgoing spatio-temporal patterns which encode complex cognitive task. This transformation is dynamic, non-stationary (time-varying) and highly nonlinear. Hence, modeling such complex biological patterns requires specific model structures to uncover the underlying physiological mechanisms and their influences on system behavior. In this study, a recent multi-electrode technology allows the record of the simultaneous neuron activities in behaving animals. Intra-cortical data are processed according to these steps: spike detection and sorting, than desired action extraction from the rate of the obtained signal. We focus on the following important questions about (i) the possibility of linking the brain signal time events with some time-delayed mapping tools; (ii) the use of some suitable inputs than others for the decoder; (iii) a consideration of separated data or a special representation founded on multi-dimensional statistics. This paper concentrates mostly on the analysis of parallel spike train when certain critical hypotheses are ignored by the data for the working method. We have made efforts to define explicitly whether the underlying hypotheses are actually achieved. In this paper, we propose an algorithm to define the embedded memory order of NARX recurrent neural networks to the hand trajectory tracking process. We also demonstrate that this algorithm can improve performance on inference tasks.

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Left and Right Hand Movements EEG Signals Classification Using Wavelet Transform and Probabilistic Neural Network

Cited by 16 publications

References 21 publications

Improved feature exctraction process to detect seizure using CHBMIT-dataset

Improved feature exctraction process to detect seizure using CHBMIT-dataset

Vascular dementia classification based on hilbert huang transform as feature extractor

Tracking times in temporal patterns embodied in intra-cortical data for controling neural prosthesis an animal simulation study

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