Wavelet Sub Band Entropy Based Feature Extraction Method for BCI

Sankar, A. Siva; Nair, Suparna S.; Dharan, Venu S.; Sankaran, Praveen

doi:10.1016/j.procs.2015.02.067

Cited by 18 publications

(8 citation statements)

References 4 publications

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“…Wavelet NN models with various activation functions and different feature extraction techniques were analyzed in the task of epileptic seizure classification with high classification rates over 90% in [7]. Wavelet multi-resolution analysis was also performed to extract the various sub-bands of the raw EEG signal for the analysis of event related potentials in a study [8]. Left and right hand imagery movements were identified using STFT [9].…”

Section: Related Studiesmentioning

confidence: 99%

Analysis of Time – Frequency EEG Feature Extraction Methods for Mental Task Classification

Uyulan¹,

Ergüzel²

2017

IJCIS

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Many endogenous and external components may affect the physiological, mental and behavioral states in humans. Monitoring tools are required to evaluate biomarkers, identify biological events, and predict their outcomes. Being one of the valuable indicators, brain biomarkers derived from temporal or spectral electroencephalography (EEG) signals processing, allow for the classification of mental disorders and mental tasks. An EEG signal has a nonstationary nature and individual frequency feature, hence it can be concluded that each subject has peculiar timing and data to extract unique features. In order to classify data, which are collected by performing four mental task (reciting the alphabet backwards, imagination of rotation of a cube, imagination of right hand movements (open/close) and performing mathematical operations), discriminative features were extracted using four competitive time-frequency techniques; Wavelet Packet Decomposition (WPD), Morlet Wavelet Transform (MWT), Short Time Fourier Transform (STFT) and Wavelet Filter Bank (WFB), respectively. The extracted features using both time and frequency domain information were then reduced using a principal component analysis for subset reduction. Finally, the reduced subsets were fed into a multi-layer perceptron neural network (MP-NN) trained with back propagation (BP) algorithm to generate a predictive model. This study mainly focuses on comparing the relative performance of time-frequency feature extraction methods that are used to classify mental tasks. The real-time (RT) conducted experimental results underlined that the WPD feature extraction method outperforms with 92% classification accuracy compared to three other aforementioned methods for four different mental tasks.

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Section: Related Studiesmentioning

confidence: 99%

Analysis of Time – Frequency EEG Feature Extraction Methods for Mental Task Classification

Uyulan¹,

Ergüzel²

2017

IJCIS

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“…2: it has two main parts, data acquisition (composed of transducers, pre-amplifying, filtering, amplifying and sampling modules) and data processing stage (data segmentation, feature extraction, feature selection, classification and controller) [33] [34] [35].…”

Section: Bioelectrical Signal-based Interfacementioning

confidence: 99%

Control systems of rehabilitation engineering equipment — A review

Ianoşi-Andreeva-Dimitrova

Mândru

2015

2015 E-Health and Bioengineering Conference (EHB)

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This article aims to review the state of the art of control systems that interface individuals with motor and sensorial disabilities and their rehabilitation equipment. It begins with the simplest form of body-powered interfaces and continues with the most recent tendencies. A rehabilitation equipment control system taxonomy is presented, as well as its associated human-body topography. (Abstract) Keywords: Assistive technology, Man machine systems, Braincomputer interfacesI.

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“…shows the functional model of a BCI system [1]. The figure depicts a generic BCI system in which a person controls a device in an operating environment (e.g., a powered wheelchair in a house) through a series of functional components.…”

mentioning

confidence: 99%

“…The user monitors the state of the device to determine the result of his/her control efforts. In some systems, the user may also be presented with a control display, which displays the control signals generated by the BCI system from his/her brain activity [1,2].…”

mentioning

confidence: 99%