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Rizvi SMA, Buriro AB, Ahmed I, Memon AA. Analyzing neural activity under prolonged mask usage through EEG. Brain Res 2024; 1822:148624. [PMID: 37838190 DOI: 10.1016/j.brainres.2023.148624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 09/17/2023] [Accepted: 10/09/2023] [Indexed: 10/16/2023]
Abstract
In recent COVID times, mask has been a compulsion at workplaces and institutes as a preventive measure against multiple viral diseases including coronavirus (COVID-19) disease. However, the effects of prolonged mask-wearing on humans' neural activity are not well known. This paper is to investigate the effect of prolonged mask usage on the human brain through electroencephalogram (EEG), which acquires neural activity and translates it into comprehensible electrical signals. The performances of 10 human subjects with and without mask were assessed on a random patterned alphabet game. Besides EEG, physiological parameters of oxygen saturation, heart rate, blood pressure, and body temperature were recorded. Spectral and statistical analysis were performed on the recorded entities along with linear discriminant analysis (LDA) on extracted spectral features. The mean EEG spectral power in alpha, beta, and gamma sub-bands of the subjects with mask was smaller than the subjects without mask. The performances on the task and the oxygen saturation level between the two groups differed significantly (p < 0.05). Whereas, the blood pressure, body temperature, and heart rate of both groups were similar. Based on the LDA analysis, the occipital and frontal lobes exhibited the greatest variability in channel measurements, with O1 and O2 channels in the occipital lobe demonstrating significant variations within the alpha band due to visual focus, while the F3, AF3, and F7 channels were found to be differentiating within the beta and gamma frequency bands due to the cognitive stimulating tasks. All other channels were observed to be non-discriminatory.
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Affiliation(s)
| | - Abdul Baseer Buriro
- Department of Electrical Engineering, Sukkur IBA University, 65200 Sukkur, Pakistan
| | - Irfan Ahmed
- Department of Electrical Engineering, Sukkur IBA University, 65200 Sukkur, Pakistan; Department of Electrical and Electronics Engineering, City University, Hong Kong.
| | - Abdul Aziz Memon
- Department of Electrical Engineering, Sukkur IBA University, 65200 Sukkur, Pakistan
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Hua J, Wolff A, Zhang J, Yao L, Zang Y, Luo J, Ge X, Liu C, Northoff G. Alpha and theta peak frequency track on- and off-thoughts. Commun Biol 2022; 5:209. [PMID: 35256748 PMCID: PMC8901672 DOI: 10.1038/s42003-022-03146-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Accepted: 02/08/2022] [Indexed: 11/09/2022] Open
Abstract
Our thoughts are highly dynamic in their contents. At some points, our thoughts are related to external stimuli or tasks focusing on single content (on-single thoughts), While in other moments, they are drifting away with multiple simultaneous items as contents (off-multiple thoughts). Can such thought dynamics be tracked by corresponding neurodynamics? To address this question, here we track thought dynamics during post-stimulus periods by electroencephalogram (EEG) neurodynamics of alpha and theta peak frequency which, as based on the phase angle, must be distinguished from non-phase-based alpha and theta power. We show how, on the psychological level, on-off thoughts are highly predictive of single-multiple thought contents, respectively. Using EEG, on-single and off-multiple thoughts are mediated by opposite changes in the time courses of alpha (high in on-single but low in off-multiple thoughts) and theta (low in on-single but high in off-multiple thoughts) peak frequencies. In contrast, they cannot be distinguished by frequency power. Overall, these findings provide insight into how alpha and theta peak frequency with their phase-related processes track on- and off-thoughts dynamically. In short, neurodynamics track thought dynamics.
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Affiliation(s)
- Jingyu Hua
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China.,Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou Normal University, Hangzhou, China.,Department of Psychology, Faculty of Social Sciences, University of Ottawa, Ottawa, ON, Canada.,Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada.,School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China
| | - Annemarie Wolff
- Department of Psychology, Faculty of Social Sciences, University of Ottawa, Ottawa, ON, Canada.,Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada
| | - Jianfeng Zhang
- Center for Brain Disorder and Cognitive Science, Shenzhen University, Shenzhen, Guangdong, China.,College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Lin Yao
- Department of Neurobiology, NHC and CAMS Key Laboratory of Medical Neurobiology, School of Brain Science and Brain Medicine, and the MOE Frontier Science Center for Brain Research and Brain-Machine Integration, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yufeng Zang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China.,Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou Normal University, Hangzhou, China.,TMS center, Deqing Hospital of Hangzhou Normal university, Deqing 313200, China
| | - Jing Luo
- School of Psychology, Capital Normal University, Beijing, China
| | - Xianliang Ge
- Center for Psychological Sciences at Zhejiang University, Zhejiang University, Hangzhou, China
| | - Chang Liu
- School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China.
| | - Georg Northoff
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China. .,Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China. .,Department of Psychology, Faculty of Social Sciences, University of Ottawa, Ottawa, ON, Canada. .,Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada. .,Mental Health Centre, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
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Shoorangiz R, Buriro AB, Weddell SJ, Jones RD. Detection and Prediction of Microsleeps from EEG using Spatio-Temporal Patterns. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:522-525. [PMID: 31945952 DOI: 10.1109/embc.2019.8857962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
A microsleep is a brief lapse in performance due to an involuntary sleep-related loss of consciousness. These episodes are of particular importance in occupations requiring extended unimpaired visuomotor performance, such as driving. Detection and even prediction of microsleeps has the potential to prevent catastrophic events and fatal accidents. In this study, we examined detection and prediction of microsleeps using EEG data of 8 subjects who performed two 1-h sessions of continuous 1-D tracking. A regularized spatio-temporal filtering and classification (RSTFC) method was used to extract features from 5-s EEG segments. These features were then used to train three different linear classifiers: linear discriminant analysis (LDA), sparse Bayesian learning (SBL), and variational Bayesian logistic regression (VBLR). The performance of microsleep state detection and prediction was evaluated using leave-one-subject-out cross-validation. The detection performance measures were AUCROC 0.96, AUCPR 0.52, and phi 0.47. As expected, prediction of microsleep states with a 0.25-s ahead prediction time resulted in slightly lower performances compared to the detection. Prediction performance measures were substantially higher than those achieved with log-power spectral features, i.e., AUCROC 0.95 (cf. 0.90), AUCPR 0.50 (cf. 0.36), and phi 0.46 (cf. 0.34).
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Automatic Detection of Epileptic Seizures in EEG Using Sparse CSP and Fisher Linear Discrimination Analysis Algorithm. J Med Syst 2020; 44:43. [PMID: 31897615 DOI: 10.1007/s10916-019-1504-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Accepted: 11/14/2019] [Indexed: 10/25/2022]
Abstract
In order to realize the automatic epileptic seizure detection, feature extraction and classification of electroencephalogram (EEG) signals are performed on the interictal, the pre-ictal, and the ictal status of epilepsy patients. There is no effective strategy for selecting the number of channels and spatial filters in feature extraction of multichannel EEG data. Therefore, this paper combined sparse idea and greedy search algorithm to improve the feature extraction of common space pattern. The feature extraction can effectively overcome the repeating selection problem of feature pattern in the eigenvector space by the traditional method. Then we used the Fisher linear discriminant analysis to realize the classification. The results show that the proposed method can get high classification accuracy using fewer data. For 10 subjects, the averaged accuracy of epilepsy detection is more than 99%. So, the detection of an epileptic seizure based on sparse features using Fisher linear discriminant analysis classifiers is more suitable for a reliable, automatic epileptic seizure detection system to enhance the patient's care and the quality of life.
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