A novel module based approach for classifying epileptic seizures using EEG signals

Sood, Meenakshi; Bhooshan, Sunil

doi:10.1109/iccsii.2016.7462406

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…To perform this task, different applications use techniques such as linear discriminant analysis (LDA), logistic regression, k-nearest neighbors (k-NN), up to computationally demanding methods such as support vector machines (SVM) and artificial neural networks (ANN) [19]. Among the classification methods explored are ANNs, which are mathematical models inspired by the functional aspects of brain structures [20] and allow for their implementation in hardware and embedded platforms for their simple structure [20].…”

Section: Introductionmentioning

confidence: 99%

Classification of the Angular Position During Wrist Flexion-extension Based on EMG Signals

Fajardo-Perdomo

Guardo-Gómez

Orjuela-Cañón

et al. 2021

IyU

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Objective: To evaluate a group of features in a myoelectric pattern recognition algorithm to differentiate between five angular positions of the wrist during flexion-extension movements. Materials and Methods: An experimental configuration was made to capture the EMG and wrist joint angle related to flexion-extension movements. After that, a myoelectric pattern recognition algorithm based on a multilayer perceptron artificial neural network (ANN) was implemented. Three different groups were used: Time domain characteristics, autoregressive (AR) model parameters, and representation of time frequency using Wavelet transform (WT). Results and Discussion: The experimental results of 10 healthy subjects indicate that the coefficients of the AR models offer the best parameters for classification, with a differentiation rate of 78 % for the five angular positions studied. The combination of frequency and time frequency resulted in a differentiation rate that reached 82 %. Conclusions: An algorithm based on pattern recognition of EMG signals was used to carry out a comparative study of groups of features that allow for the differentiation of the angular position of the wrist in terms of flexion-extension movements. The method has the potential for application in the field of rehabilitation engineering to detect the user’s movement intent.

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Section: Introductionmentioning

confidence: 99%