Investigation of the noise effect on fractal dimension of EEG in schizophrenia patients using wavelet and SSA-based approaches

Akar, Saime Akdemir; Kara, Sadık; Latifoğlu, Fatma; Bilgiç, Vedat

doi:10.1016/j.bspc.2014.11.004

Cited by 32 publications

(27 citation statements)

References 43 publications

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“…Therefore, in future study, we should select the more appropriate cool executive tasks and increase the number of subjects to clearly discriminate the complexity of EEG signals between tasks. Moreover, we found small differences in the FD values between patients and controls, which is similar to a previous study by Akar [30], and may be related to less damage to executive function in first-episode schizophrenia patients. However, the difference in the FD values between normal and schizophrenia subjects is in the opposite direction in these two studies.…”

Section: Discussionsupporting

confidence: 91%

Estimation of the cool executive function using frontal electroencephalogram signals in first-episode schizophrenia patients

Yun

et al. 2016

BioMed Eng OnLine

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BackgroundIn schizophrenia, executive dysfunction is the most critical cognitive impairment, and is associated with abnormal neural activities, especially in the frontal lobes. Complexity estimation using electroencephalogram (EEG) recording based on nonlinear dynamics and task performance tests have been widely used to estimate executive dysfunction in schizophrenia.MethodsThe present study estimated the cool executive function based on fractal dimension (FD) values of EEG data recorded from first-episode schizophrenia patients and healthy controls during the performance of three cool executive function tasks, namely, the Trail Making Test-A (TMT-A), Trail Making Test-B (TMT-B), and Tower of Hanoi tasks.ResultsThe results show that the complexity of the frontal EEG signals that were measured using FD was different in first-episode schizophrenia patients during the manipulation of executive function. However, no differences between patients and controls were found in the FD values of the EEG data that was recorded during the performance of the Tower of Hanoi task.ConclusionsThese results suggest that cool executive function exhibits little impairment in first-episode schizophrenia patients.

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Section: Discussionsupporting

confidence: 91%

Estimation of the cool executive function using frontal electroencephalogram signals in first-episode schizophrenia patients

Yun

et al. 2016

BioMed Eng OnLine

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“…Katz's Fractal Dimension (KFD) is commonly used to measure the complexity of EEG signals. KFD is described as a following (Akar et al, 2015, Esteller et al, 1999;…”

Section: Katz's Fractal Dimensionmentioning

confidence: 99%

“…where Lis the total length of the time series, a symbolize the average number of steps and d describes as the Euclidian distance between the first sample and the sample which provides farthest distance (Akar et al, 2015;Esteller et al, 1999).…”

Section: Katz's Fractal Dimensionmentioning

confidence: 99%

Two Stepped Majority Voting for Efficient EEG based Emotion Classification

Ismael

Alçın

Abdalla

et al. 2020

Preprint

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In this paper, a novel approach that is based on two-stepped majority voting is proposed for efficient EEG based emotion classification. Emotion recognition is important for human-machine interactions. Facial-features and body-gestures based approaches have been generally proposed for emotion recognition. Recently, EEG based approaches become more popular in emotion recognition. In the proposed approach, the raw EEG signals are initially low-pass filtered for noise removal and band-pass filters are used for rhythms extraction. For each rhythm, the best performed EEG channels are determined based on wavelet-based entropy features and fractal dimension based features. The k-nearest neighbor (KNN) classifier is used in classification. The best five EEG channels are used in majority voting for getting the final predictions for each EEG rhythm. In the second majority voting step, the predictions from all rhythms are used to get a final prediction. The DEAP dataset is used in experiments and classification accuracy, sensitivity and specificity are used for performance evaluation metrics. The experiments are carried out to classify the emotions into two binary classes such as high valence (HV) vs low valence (LV) and high arousal (HA) vs low arousal (LA). The experiments show that 86.3% HV vs LV discrimination accuracy and 85.0% HA vs LA discrimination accuracy is obtained. The obtained results are also compared with some of the existing methods. The comparisons show that the proposed method has potential in the use of EEG based emotion classification.

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“…After artifacts removal, the EEG alpha rhythms in the eyes-open and eyes-closed states were extracted. In order to verify the rhythms extraction using the adaptive SSA, another recently reported SSA method (Mohammadi et al 2016) and wavelet decomposition method (Akar et al 2015), which will be called SSA 2# and WDec respectively in this paper, were used for comparison.…”

Section: Experimental Eeg Datamentioning

confidence: 99%

Peer Review #1 of "An adaptive singular spectrum analysis method for extracting brain rhythms of electroencephalography (v0.2)"

2017

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Artifacts removal and rhythms extraction from electroencephalography (EEG) signals is important for portable and wearable EEG recording devices. Incorporating a novel grouping rule, we proposed an adaptive singular spectrum analysis (SSA) method for artifacts removal and rhythms extraction. Based on the EEG signal amplitude, the grouping rule determines adaptively the first one or two SSA reconstructed components as artifacts and removes them. The remaining reconstructed components are then grouped based on their peak frequencies in the Fourier transform to extract the desired rhythms. The grouping rule thus enables SSA to be adaptive to EEG signals containing different levels of artifacts and rhythms. The simulated EEG data based on the Markov Process Amplitude (MPA) EEG model and the experimental EEG data in the eyes-open and eyes-closed states were used to verify the adaptive SSA method. Results showed a better performance in artifacts removal and rhythms extraction, compared with the wavelet decomposition (WDec) and another two recently reported SSA methods. Features of the extracted alpha rhythms using adaptive SSA were calculated to distinguish between the eyes-open and eyes-closed states. Results showed a higher accuracy (95.8%) than those of the WDec method (79.2%) and the infinite impulse response (IIR) filtering method (83.3%).

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Investigation of the noise effect on fractal dimension of EEG in schizophrenia patients using wavelet and SSA-based approaches

Cited by 32 publications

References 43 publications

Estimation of the cool executive function using frontal electroencephalogram signals in first-episode schizophrenia patients

Estimation of the cool executive function using frontal electroencephalogram signals in first-episode schizophrenia patients

Two Stepped Majority Voting for Efficient EEG based Emotion Classification

Peer Review #1 of "An adaptive singular spectrum analysis method for extracting brain rhythms of electroencephalography (v0.2)"

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