EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks

Jahankhani, Pari; Kodogiannis, Vassilis; Revett, Kenneth

doi:10.1109/jva.2006.17

Cited by 241 publications

(120 citation statements)

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“…However, the AR model cannot capture the transient features in EEG data, and the timefrequency information is not easily seen in the AR parameters [14]. Therefore, several researchers have used wavelet coe cients that provide localization of signal components with spectro-temporal characteristics [15][16][17][18][19]. The main bene t of wavelets is the time-frequency localization.…”

Section: Previous Work On Algorithmsmentioning

confidence: 99%

An Efficient Hardware Implementation for a Motor Imagery Brain Computer Interface System

et al. 2018

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Abstract. Brain Computer Interface (BCI) systems, which are based on motor imagery, enable humans to command arti cial peripherals by merely thinking about the task. There is a tremendous interest in implementing BCIs on portable platforms, such as Field Programmable Gate Arrays (FPGAs) due to their low-cost, low-power and portability characteristics. This article presents the design and implementation of a Brain Computer Interface (BCI) system based on motor imagery on a Virtex-6 FPGA. In order to design an accurate algorithm, the proposed method avails statistical learning methods such as Mutual Information (MI), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM). It also uses Separable Common Spatio Spectral Pattern (SCSSP) method in order to extract features. Simulation results prove achieved performances of 73.54% for BCI competition III-dataset V, 67.2% for BCI competition IV-dataset 2a with all four classes, 80.55% for BCI competition IV-dataset 2a with the rst two classes, and 81.9% for captured signals. Moreover, the nal reported hardware resources determine its e ciency as a result of using retiming and folding techniques from the VLSI architecture' perspective. The complete proposed BCI system achieves not only excellent recognition accuracy, but also remarkable implementation e ciency in terms of portability, power, time, and cost.

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Section: Previous Work On Algorithmsmentioning

confidence: 99%

An Efficient Hardware Implementation for a Motor Imagery Brain Computer Interface System

et al. 2018

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“…Wavelet transform forms a general mathematical tool forsignal processing with many applications in EEG data analysis (Glavinovitch et al, 2005;Johankhani et al, 2006;Dimoulas et al, 2007;Selesnick et al, 2005 ;Nazareth et al, 2006) as well. Its basic use includes time-scale signal analysis, signal decomposition and signal compression.…”

Section: Wavelet Transform (Wt)mentioning

confidence: 99%

A review on EEG based brain computer interface systems feature extraction methods

2016

Int. J. Adv. Biol. Biom. Res.

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“…The times series model generation method has been compared with other approaches on electroencephalographic (EEG) time series data [37], using publicly available datasets (described in [38]). …”

Section: Reference Model Generationmentioning

confidence: 99%

Generating Reference Models for Structurally Complex Data

Alonso¹,

Lara²,

Martínez³

et al. 2013

Methods Inf Med

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SummaryObjectives: We present a framework specially designed to deal with structurally complex data, where all individuals have the same structure, as is the case in many medical domains. A structurally complex individual may be composed of any type of singlevalued or multivalued attributes, including time series, for example. These attributes are structured according to domain-dependent hierarchies. Our aim is to generate reference models of population groups. These models represent the population archetype and are very useful for supporting such important tasks as diagnosis, detecting fraud, analyzing patient evolution, identifying control groups, etc. Methods: We have developed a conceptual model to represent structurally complex data hierarchically. Additionally, we have devised a method that uses the similarity tree concept to measure how similar two structurally complex individuals are, plus an outlier detection and filtering method. These methods provide the groundwork for the method that we have designed for generating reference models of a set of structurally complex individuals. A key idea of this method is to use event-based analysis for modeling time series. Results: The proposed framework has been applied to the medical field of stabilometry. To validate the outlier detection method we used 142 individuals, and there was a match between the outlier ratings by the experts and by the system for 139 individuals (97.8%). To validate the reference model generation method, we applied k-fold cross validation (k = 5) with 60 athletes (basketball players and ice-skaters), and the system correctly classified 55 (91.7%). We then added 30 non-athletes as a control group, and the method output the correct result in a very high percentage of cases (96.6%). Conclusions: We have achieved very satisfactory results for the tests on data from such a complex domain as stabilometry and for the comparison of the reference model generation method with other methods. This supports the validity of this framework.

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EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks

Cited by 241 publications

References 10 publications

An Efficient Hardware Implementation for a Motor Imagery Brain Computer Interface System

An Efficient Hardware Implementation for a Motor Imagery Brain Computer Interface System

A review on EEG based brain computer interface systems feature extraction methods

Generating Reference Models for Structurally Complex Data

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