EEG Signal Analysis and Classification

Siuly, Siuly; Li, Yan; Zhang, Yanchun

doi:10.1007/978-3-319-47653-7

Cited by 128 publications

(96 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…LS-SVM is a well-known classifier and is used in the classification of many biomedical signals, like electrocardiograph (ECG) signals [70], heart rate variability signals [71], cardiac sound signals [72], brain MRI classification [73], EEG signals [74,75], etc. In this work, the classification performance of the LS-SVM classifier is evaluated by employing the ten-fold cross-validation procedure.…”

Section: Discussionmentioning

confidence: 99%

Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals

Sharma

Pachori

Acharya

2015

Entropy

281

173

View full text Add to dashboard Cite

The brain is a complex structure made up of interconnected neurons, and its electrical activities can be evaluated using electroencephalogram (EEG) signals. The characteristics of the brain area affected by partial epilepsy can be studied using focal and non-focal EEG signals. In this work, a method for the classification of focal and non-focal EEG signals is presented using entropy measures. These entropy measures can be useful in assessing the nonlinear interrelation and complexity of focal and non-focal EEG signals. These EEG signals are first decomposed using the empirical mode decomposition (EMD) method to extract intrinsic mode functions (IMFs). The entropy features, namely, average Shannon entropy (ShEn Avg ), average Renyi's entropy (RenEn Avg ), average approximate entropy (ApEn Avg ), average sample entropy (SpEn Avg ) and average phase entropies (S1 Avg and S2 Avg ), are computed from different IMFs of focal and non-focal EEG signals. These entropies are used as the input feature set for the least squares support vector machine (LS-SVM) classifier to classify into focal and non-focal EEG signals. Experimental results show that our proposed method is able to differentiate the focal and non-focal EEG signals with an average classification accuracy of 87% correct.

show abstract

Section: Discussionmentioning

confidence: 99%

Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals

Sharma

Pachori

Acharya

2015

Entropy

281

173

View full text Add to dashboard Cite

show abstract

“…The most important criteria of evaluation EEG are frequency. This is a criterion for assessing abnormalities in clinical EEG and for understanding functional behaviours in cognitive research [5].…”

Section: Signal Acquisitionmentioning

confidence: 99%

Introduction to the identification of brain waves based on their frequency

Koudelková

Strmiska

2018

MATEC Web Conf.

View full text Add to dashboard Cite

A Brain Computer Interface (BCI) enables to get electrical signals from the brain. In this paper, the research type of BCI was non-invasive, which capture the brain signals using electroencephalogram (EEG). EEG senses the signals from the surface of the head, where one of the important criteria is the brain wave frequency. This paper provides the measurement of EEG using the Emotiv EPOC headset and applications developed by Emotiv System. Two types of the measurements were taken to describe brain waves by their frequency. The first type of the measurements was based on logical and analytical reasoning, which was captured during solving mathematical exercise. The second type was based on relax mind during listening three types of relaxing music. The results of the measurements were displayed as a visualization of a brain activity.

show abstract

“…To avoid stimulus artifact bias, training and testing subsets alternated the same number of trials with opposite stimulus polarity (Bidelman et al, 2013; Skoe & Kraus, 2010). To avoid sample selection bias, the selection of testing and training subsets was cross-validated using a K-fold partition, with K equal to the possible number of disjoint testing selections available in the corresponding tone dataset (Kuhn & Johnson, 2013; Siuly, Li, & Zhang, 2017). For example, if the testing size is 100 trials and the number of FFRs to the tone data set is 500, the possible number of disjoint testing selections is 5.…”

Section: Hidden Markov Modeling Of Neural Pitch Patternsmentioning

confidence: 99%

Hidden Markov modeling of frequency-following responses to Mandarin lexical tones

Llanos

Xie

Chandrasekaran

2017

Journal of Neuroscience Methods

View full text Add to dashboard Cite

Background The frequency-following response (FFR) is a scalp-recorded electrophysiological potential reflecting phase-locked activity from neural ensembles in the auditory system. The FFR is often used to assess the robustness of subcortical pitch processing. Due to low signal-to-noise ratio at the single-trial level, FFRs are typically averaged across thousands of stimulus repetitions. Prior work using this approach has shown that subcortical encoding of linguistically-relevant pitch patterns is modulated by long-term language experience. New method We examine the extent to which a machine learning approach using hidden Markov modeling (HMM) can be utilized to decode Mandarin tone-categories from scalp-record electrophysiolgical activity. We then assess the extent to which the HMM can capture biologically-relevant effects (language experience-driven plasticity). To this end, we recorded FFRs to four Mandarin tones from 14 adult native speakers of Chinese and 14 of native English. We trained a HMM to decode tone categories from the FFRs with varying size of averages. Results and comparisons with existing methods Tone categories were decoded with above-chance accuracies using HMM. The HMM derived metric (decoding accuracy) revealed a robust effect of language experience, such that FFRs from native Chinese speakers yielded greater accuracies than native English speakers. Critically, the language experience-driven plasticity was captured with average sizes significantly smaller than those used in the extant literature. Conclusions Our results demonstrate the feasibility of HMM in assessing the robustness of neural pitch. Machine-learning approaches can complement extant analytical methods that capture auditory function and could reduce the number of trials needed to capture biological phenomena.

show abstract

EEG Signal Analysis and Classification

Cited by 128 publications

References 0 publications

Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals

Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals

Introduction to the identification of brain waves based on their frequency

Hidden Markov modeling of frequency-following responses to Mandarin lexical tones

Contact Info

Product

Resources

About