Aperiodic Neural Activity is a Better Predictor of Schizophrenia than Neural Oscillations
Clin EEG Neurosci. 2023 Jun 7:15500594231165589. doi: 10.1177/15500594231165589. Online ahead of print.ABSTRACTDiagnosis and symptom severity in schizophrenia are associated with irregularities across neural oscillatory frequency bands, including theta, alpha, beta, and gamma. However, electroencephalographic signals consist of both periodic and aperiodic activity characterized by the (1/fX) shape in the power spectrum. In this paper, we investigated oscillatory and aperiodic activity differences between patients with schizophrenia and healthy controls during a target detection task. Separation into periodic and aperiodic ...
Source: Clinical EEG and Neuroscience - June 8, 2023 Category: Neuroscience Authors: Erik J Peterson Burke Q Rosen Aysenil Belger Bradley Voytek Alana M Campbell Source Type: research
Aperiodic Neural Activity is a Better Predictor of Schizophrenia than Neural Oscillations
Clin EEG Neurosci. 2023 Jun 7:15500594231165589. doi: 10.1177/15500594231165589. Online ahead of print.ABSTRACTDiagnosis and symptom severity in schizophrenia are associated with irregularities across neural oscillatory frequency bands, including theta, alpha, beta, and gamma. However, electroencephalographic signals consist of both periodic and aperiodic activity characterized by the (1/fX) shape in the power spectrum. In this paper, we investigated oscillatory and aperiodic activity differences between patients with schizophrenia and healthy controls during a target detection task. Separation into periodic and aperiodic ...
Source: Clinical EEG and Neuroscience - June 8, 2023 Category: Neuroscience Authors: Erik J Peterson Burke Q Rosen Aysenil Belger Bradley Voytek Alana M Campbell Source Type: research
Aperiodic Neural Activity is a Better Predictor of Schizophrenia than Neural Oscillations
Clin EEG Neurosci. 2023 Jun 7:15500594231165589. doi: 10.1177/15500594231165589. Online ahead of print.ABSTRACTDiagnosis and symptom severity in schizophrenia are associated with irregularities across neural oscillatory frequency bands, including theta, alpha, beta, and gamma. However, electroencephalographic signals consist of both periodic and aperiodic activity characterized by the (1/fX) shape in the power spectrum. In this paper, we investigated oscillatory and aperiodic activity differences between patients with schizophrenia and healthy controls during a target detection task. Separation into periodic and aperiodic ...
Source: Clinical EEG and Neuroscience - June 8, 2023 Category: Neuroscience Authors: Erik J Peterson Burke Q Rosen Aysenil Belger Bradley Voytek Alana M Campbell Source Type: research
An Improved AlexNet Model and Cepstral Coefficient-Based Classification of Autism Using EEG
This study evaluates the performance of various deep learning networks, including AlexNet, VGG16, and ResNet50, using 5 cepstral coefficient features for ASD detection. The main contributions of this study are the utilization of Cepstral Coefficients in the processing stage to construct spectrograms and the modification of the AlexNet architecture for precise classification. Experimental observations indicate that the AlexNet with Linear Frequency Cepstral Coefficients (LFCC) yields the highest accuracy of 85.1%, while a customized AlexNet with LFCC achieves 90% accuracy.PMID:37246419 | DOI:10.1177/15500594231178274 (Sourc...
Source: Clinical EEG and Neuroscience - May 29, 2023 Category: Neuroscience Authors: R Menaka R Karthik S Saranya M Niranjan S Kabilan Source Type: research
An Improved AlexNet Model and Cepstral Coefficient-Based Classification of Autism Using EEG
This study evaluates the performance of various deep learning networks, including AlexNet, VGG16, and ResNet50, using 5 cepstral coefficient features for ASD detection. The main contributions of this study are the utilization of Cepstral Coefficients in the processing stage to construct spectrograms and the modification of the AlexNet architecture for precise classification. Experimental observations indicate that the AlexNet with Linear Frequency Cepstral Coefficients (LFCC) yields the highest accuracy of 85.1%, while a customized AlexNet with LFCC achieves 90% accuracy.PMID:37246419 | DOI:10.1177/15500594231178274 (Sourc...
Source: Clinical EEG and Neuroscience - May 29, 2023 Category: Neuroscience Authors: R Menaka R Karthik S Saranya M Niranjan S Kabilan Source Type: research
An Improved AlexNet Model and Cepstral Coefficient-Based Classification of Autism Using EEG
This study evaluates the performance of various deep learning networks, including AlexNet, VGG16, and ResNet50, using 5 cepstral coefficient features for ASD detection. The main contributions of this study are the utilization of Cepstral Coefficients in the processing stage to construct spectrograms and the modification of the AlexNet architecture for precise classification. Experimental observations indicate that the AlexNet with Linear Frequency Cepstral Coefficients (LFCC) yields the highest accuracy of 85.1%, while a customized AlexNet with LFCC achieves 90% accuracy.PMID:37246419 | DOI:10.1177/15500594231178274 (Sourc...
Source: Clinical EEG and Neuroscience - May 29, 2023 Category: Neuroscience Authors: R Menaka R Karthik S Saranya M Niranjan S Kabilan Source Type: research
An Improved AlexNet Model and Cepstral Coefficient-Based Classification of Autism Using EEG
This study evaluates the performance of various deep learning networks, including AlexNet, VGG16, and ResNet50, using 5 cepstral coefficient features for ASD detection. The main contributions of this study are the utilization of Cepstral Coefficients in the processing stage to construct spectrograms and the modification of the AlexNet architecture for precise classification. Experimental observations indicate that the AlexNet with Linear Frequency Cepstral Coefficients (LFCC) yields the highest accuracy of 85.1%, while a customized AlexNet with LFCC achieves 90% accuracy.PMID:37246419 | DOI:10.1177/15500594231178274 (Sourc...
Source: Clinical EEG and Neuroscience - May 29, 2023 Category: Neuroscience Authors: R Menaka R Karthik S Saranya M Niranjan S Kabilan Source Type: research
An Improved AlexNet Model and Cepstral Coefficient-Based Classification of Autism Using EEG
This study evaluates the performance of various deep learning networks, including AlexNet, VGG16, and ResNet50, using 5 cepstral coefficient features for ASD detection. The main contributions of this study are the utilization of Cepstral Coefficients in the processing stage to construct spectrograms and the modification of the AlexNet architecture for precise classification. Experimental observations indicate that the AlexNet with Linear Frequency Cepstral Coefficients (LFCC) yields the highest accuracy of 85.1%, while a customized AlexNet with LFCC achieves 90% accuracy.PMID:37246419 | DOI:10.1177/15500594231178274 (Sourc...
Source: Clinical EEG and Neuroscience - May 29, 2023 Category: Neuroscience Authors: R Menaka R Karthik S Saranya M Niranjan S Kabilan Source Type: research
An Improved AlexNet Model and Cepstral Coefficient-Based Classification of Autism Using EEG
This study evaluates the performance of various deep learning networks, including AlexNet, VGG16, and ResNet50, using 5 cepstral coefficient features for ASD detection. The main contributions of this study are the utilization of Cepstral Coefficients in the processing stage to construct spectrograms and the modification of the AlexNet architecture for precise classification. Experimental observations indicate that the AlexNet with Linear Frequency Cepstral Coefficients (LFCC) yields the highest accuracy of 85.1%, while a customized AlexNet with LFCC achieves 90% accuracy.PMID:37246419 | DOI:10.1177/15500594231178274 (Sourc...
Source: Clinical EEG and Neuroscience - May 29, 2023 Category: Neuroscience Authors: R Menaka R Karthik S Saranya M Niranjan S Kabilan Source Type: research
An Improved AlexNet Model and Cepstral Coefficient-Based Classification of Autism Using EEG
This study evaluates the performance of various deep learning networks, including AlexNet, VGG16, and ResNet50, using 5 cepstral coefficient features for ASD detection. The main contributions of this study are the utilization of Cepstral Coefficients in the processing stage to construct spectrograms and the modification of the AlexNet architecture for precise classification. Experimental observations indicate that the AlexNet with Linear Frequency Cepstral Coefficients (LFCC) yields the highest accuracy of 85.1%, while a customized AlexNet with LFCC achieves 90% accuracy.PMID:37246419 | DOI:10.1177/15500594231178274 (Sourc...
Source: Clinical EEG and Neuroscience - May 29, 2023 Category: Neuroscience Authors: R Menaka R Karthik S Saranya M Niranjan S Kabilan Source Type: research
An Improved AlexNet Model and Cepstral Coefficient-Based Classification of Autism Using EEG
This study evaluates the performance of various deep learning networks, including AlexNet, VGG16, and ResNet50, using 5 cepstral coefficient features for ASD detection. The main contributions of this study are the utilization of Cepstral Coefficients in the processing stage to construct spectrograms and the modification of the AlexNet architecture for precise classification. Experimental observations indicate that the AlexNet with Linear Frequency Cepstral Coefficients (LFCC) yields the highest accuracy of 85.1%, while a customized AlexNet with LFCC achieves 90% accuracy.PMID:37246419 | DOI:10.1177/15500594231178274 (Sourc...
Source: Clinical EEG and Neuroscience - May 29, 2023 Category: Neuroscience Authors: R Menaka R Karthik S Saranya M Niranjan S Kabilan Source Type: research
Connectivity Disturbances in Self-Limited Epilepsy with Centrotemporal Spikes: A Partial Directed Coherence Analysis of Electroencephalogram
This study enrolled 38 participants (19 patients with SeLECTS and 19 healthy controls) for PDC analysis. Our results demonstrated that the controls had significantly higher PDC inflow connectivity in the F7, T3, FP1, and F8 channels than patients with SeLECTS. By contrast, the patients with SeLECTS demonstrated significantly higher PDC inflow connectivity than did the controls in the T5, Pz, and P4 channels. We also compared the PDC connectivity in different Brodmann areas between the patients with SeLECTS and the controls. The results revealed that the inflow connectivity in the BA9_46_L area was significantly higher in t...
Source: Clinical EEG and Neuroscience - May 25, 2023 Category: Neuroscience Authors: Ching-Tai Chiang Rei-Cheng Yang Yu-Chia Kao Rong-Ching Wu Chen-Sen Ouyang Lung-Chang Lin Source Type: research
Connectivity Disturbances in Self-Limited Epilepsy with Centrotemporal Spikes: A Partial Directed Coherence Analysis of Electroencephalogram
This study enrolled 38 participants (19 patients with SeLECTS and 19 healthy controls) for PDC analysis. Our results demonstrated that the controls had significantly higher PDC inflow connectivity in the F7, T3, FP1, and F8 channels than patients with SeLECTS. By contrast, the patients with SeLECTS demonstrated significantly higher PDC inflow connectivity than did the controls in the T5, Pz, and P4 channels. We also compared the PDC connectivity in different Brodmann areas between the patients with SeLECTS and the controls. The results revealed that the inflow connectivity in the BA9_46_L area was significantly higher in t...
Source: Clinical EEG and Neuroscience - May 25, 2023 Category: Neuroscience Authors: Ching-Tai Chiang Rei-Cheng Yang Yu-Chia Kao Rong-Ching Wu Chen-Sen Ouyang Lung-Chang Lin Source Type: research
Connectivity Disturbances in Self-Limited Epilepsy with Centrotemporal Spikes: A Partial Directed Coherence Analysis of Electroencephalogram
This study enrolled 38 participants (19 patients with SeLECTS and 19 healthy controls) for PDC analysis. Our results demonstrated that the controls had significantly higher PDC inflow connectivity in the F7, T3, FP1, and F8 channels than patients with SeLECTS. By contrast, the patients with SeLECTS demonstrated significantly higher PDC inflow connectivity than did the controls in the T5, Pz, and P4 channels. We also compared the PDC connectivity in different Brodmann areas between the patients with SeLECTS and the controls. The results revealed that the inflow connectivity in the BA9_46_L area was significantly higher in t...
Source: Clinical EEG and Neuroscience - May 25, 2023 Category: Neuroscience Authors: Ching-Tai Chiang Rei-Cheng Yang Yu-Chia Kao Rong-Ching Wu Chen-Sen Ouyang Lung-Chang Lin Source Type: research
Connectivity Disturbances in Self-Limited Epilepsy with Centrotemporal Spikes: A Partial Directed Coherence Analysis of Electroencephalogram
This study enrolled 38 participants (19 patients with SeLECTS and 19 healthy controls) for PDC analysis. Our results demonstrated that the controls had significantly higher PDC inflow connectivity in the F7, T3, FP1, and F8 channels than patients with SeLECTS. By contrast, the patients with SeLECTS demonstrated significantly higher PDC inflow connectivity than did the controls in the T5, Pz, and P4 channels. We also compared the PDC connectivity in different Brodmann areas between the patients with SeLECTS and the controls. The results revealed that the inflow connectivity in the BA9_46_L area was significantly higher in t...
Source: Clinical EEG and Neuroscience - May 25, 2023 Category: Neuroscience Authors: Ching-Tai Chiang Rei-Cheng Yang Yu-Chia Kao Rong-Ching Wu Chen-Sen Ouyang Lung-Chang Lin Source Type: research
