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November 21, 2017Frontiers in Computational NeuroscienceOpen Access

The proposed method yielded 99.11% accuracy via SVM classifier for coefficient approximations (A5) of low frequencies ranging from 0 to 3.90 Hz.

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Population

High density EEG dataset and a public dataset of EEG signals recorded during cognitive tasks and baseline

Comparison

Pattern recognition approach using wavelet-based… vs Extant quantitative feature extraction methods

Design

Other

Authors

HAHafeez Ullah AminWMWajid MumtazASAhmad Rauf Subhani

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Overview

May aid EEG-based cognitive research pipelines; leaves open clinical validation and prospective testing.

Structured PICO

P
Population
High density EEG dataset (128-channels) and a public dataset of EEG signals recorded during cognitive tasks (Raven's Advance Progressive Metric test) and baseline (eyes open)
I
Intervention
Pattern recognition approach using wavelet-based feature extraction, Fisher's discriminant ratio (FDR), principal component analysis (PCA), and machine learning classifiers (SVM, KNN, MLP, NB)
C
Comparator
Extant quantitative feature extraction methods
O
Outcome
Classification accuracy of EEG signalssurrogate

A pattern recognition approach using wavelet-based feature extraction and machine learning classifiers achieved high accuracy in classifying EEG signals during cognitive tasks.

Cite This Study

Amin et al. (2017) studied this question.

synapsesocial.com/papers/6a7cface3e89d9be1fec2433https://doi.org/10.3389/fncom.2017.00103
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  5. 5Using Mathematical Techniques to Analyse Biomedical Data: A K-complexes EEG Signal Classification Study2024