Wavelet based analysis of EEG signal for evaluating mental behavior

Ahmed, Tazrin; Islam, Mohammad Mahfuzul; Yusuf, Salah Uddin; Ahmad, Mohiuddin

doi:10.1109/iciev.2013.6572706

Cited by 8 publications

(5 citation statements)

References 5 publications

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“…The data is collected from PhysioBank (Web: http://www.physionet.org/cgi-bin/atm/ATM) [14]. Among several sleep disorders, in this paper two types are considered and evaluated.…”

Section: A Database Usedmentioning

confidence: 99%

Identification of Fatal Familial Insomnia Sleep Disorder Using Ga Optimization with Svm Classifier

Karri*,

Alli,

Rao

2020

IJITEE

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Among the genetic disorders in brain Fatal familial insomnia (FFI) is one of the rare disorder. The inability of sleeping is called as FFI which leads to the process of becoming progressively worse mentally and physically. The recordings of brain functioning is known as Electroencephalogram (EEG) and which seeks a vital role in observing and finding the sleep disorders like FFI. The EEG senses the brain functionality. In this paper , genetic optimization based SVM classifier is used to identify the sleep disorder. The proposed technique is used to optimize the features which are obtained from features reduction techniques like PCA, ICA and LDA. For performing the experimental results PhysioNet database is obtained. Preprocessing of the data, performing feature reduction, next feature optimization and then classification using Support vector machine is the process for identifying the FFI. The performance measures such as accuracy, sensitivity, specificity, F1-Score, Recall are computed . The comparison of results obtained using different condition are observed and the ranges are determined for normal class, Bruxism class and FFI class.

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“…The data is collected from PhysioBank (Web: http://www.physionet.org/cgi-bin/atm/ATM) [14]. Among several sleep disorders, in this paper two types are considered and evaluated.…”

Section: A Database Usedmentioning

confidence: 99%

Identification of Fatal Familial Insomnia Sleep Disorder Using Ga Optimization with Svm Classifier

Karri*,

Alli,

Rao

2020

IJITEE

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“…3. The data acquisition method was also discussed in [13,14]. In case of signal acquisition, the electrodes are placed in occipital lobe region which give mainly the variation in alpha amplitude of EEG with subject mental activities.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…Each epoch of the EEG data is processed with Hamming window by zero padding for 128-point fast Fourier transform. The detailed analysis was discussed in [13,14]. Different features for FFT analysis of alpha component of EEG signal is given by,…”

Section: B Frequency Domain Featurementioning

confidence: 99%

“…In the present paper, the Daubechies4 wavelet function ("db4") is used for extracting features from the EEG signal in time frequency domain. The detailed analysis was discussed in [14].…”

Section: Time-frequency Domain Featurementioning

confidence: 99%

“…Authors in [12] employed SVM based classifier to detect epileptic seizure activity from background electroencephalographs (EEGs). In our previous work an emotion recognition system was developed using some salient global features such as amplitude (maximum and minimum, peak to peak value), mean, median, standard deviation, skew, kurtosis, frequency components and wavelet coefficients using FFT and DWT transformation [13,14]. …”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Human emotion modeling based on salient global features of EEG signal

Ahmed

Islam

Ahmad

2013

2013 2nd International Conference on Advances in Electrical Engineering (ICAEE)

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Feature extraction and accurate classification of the emotion-related EEG-characteristics have a key role in success of emotion recognition systems. This paper proposes an emotion modeling from EEG (Electroencephalogram) signals based on both time and frequency domain features by applying some statistical measures, Fourier and wavelet transform. After collecting the EEG signals, the various kinds of EEG features are investigated to build an emotion classification system. The main objective of this work is to compare the efficacy of the extracted features for classifying five types of emotional states relax, mental task, memory related task, pleasant, and fear. For this purpose support vector machine classifier was employed to classify the five emotional states by using salient global features. In case of statistical features the overall accuracy was obtained 54.2%, which is improved for FFT features 55.00% and the highest accuracy was obtained by DWT features 60.15%.

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