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the channel locations by name. The 10-20 international recording system was used as the electrode positioning layout.
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Measuring mental fatigue is essential in assessing the performance of those subjects whose careers involve severe mental activity. Recently, many analytical methods have been applied to electroencephalograms (EEGs) in order to quantitatively detect the fatigue state, but their accuracy is still not satisfactory. Factorization methods have been empl...
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... signals were recorded using the Neuroscan LT setup along with a 32-electrode EEG cap. The 10-20 international recording system was used as the electrode positioning layout as shown in Fig. 1, and the channel numbers were in the following order (Fp1, Fp2, F3, F4, FC3, FC4, C3, C4, CP3, CP4, P3, P4, O1, O2, F7, F8, FT7, FT8, T3, T4, TP7, TP8, T5, T6, Fz, FCz, Cz, CPz, Pz, Oz). Also, the electrode- contact impedances were kept below 5KΩ during the entire recording period. The recording sampling rate was set to 250 Hz, and the ...
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... This brain activity-related dataset is amenable to evaluation and interpretation through diverse machine learning methodologies. Analysis of EEG is used for the diagnosis of different neuropsychiatric disorders such as Schizophrenia [6]- [8], Alzheimer's [9], ADHD [10], [11], dementia [12], brain fatigue [13], [14], sleep disorders [15], [16], bipolar manic depression (BMD) [17], and Seizure [18]. ...
Depression severity can be classified into distinct phases based on the Beck depression inventory (BDI) test scores, a subjective questionnaire. However, quantitative assessment of depression may be attained through the examination and categorization of electroencephalography (EEG) signals. Spiking neural networks (SNNs), as the third generation of neural networks, incorporate biologically realistic algorithms, making them ideal for mimicking internal brain activities while processing EEG signals. This study introduces a novel framework that for the first time, combines an SNN architecture and a long short-term memory (LSTM) structure to model the brain’s underlying structures during different stages of depression and effectively classify individual depression levels using raw EEG signals. By employing a brain-inspired SNN model, our research provides fresh perspectives and advances knowledge of the neurological mechanisms underlying different levels of depression. The methodology employed in this study includes the utilization of the synaptic time dependent plasticity (STDP) learning rule within a 3-dimensional brain-template structured SNN model. Furthermore, it encompasses the tasks of classifying and predicting individual outcomes, visually representing the structural alterations in the brain linked to the anticipated outcomes, and offering interpretations of the findings. Notably, our method achieves exceptional accuracy in classification, with average rates of 98% and 96% for eyes-closed and eyes-open states, respectively. These results significantly outperform state-of-the-art deep learning methods.
... This brain activity-related dataset is amenable to evaluation and interpretation through diverse machine learning methodologies. Analysis of EEG is used for the diagnosis of different neuropsychiatric disorders such as Schizophrenia [6]- [8], Alzheimer's [9], ADHD [10], [11], dementia [12], brain fatigue [13], [14], sleep disorders [15], [16], bipolar manic depression (BMD) [17], and Seizure [18]. ...
p>Depression severity can be classified into distinct phases based on the Beck depression inventory (BDI) test scores, a subjective questionnaire. However, quantitative assessment of depression may be attained through the examination and categorization of electroencephalography (EEG) signals. Spiking neural networks (SNNs), as the third generation of neural networks, incorporate biologically realistic algorithms, making them ideal for mimicking internal brain activities while processing EEG signals. This study introduces a novel framework that for the first time, combines an SNN architecture and a long short-term memory (LSTM) structure to model the brain’s underlying structures during different stages of depression and effectively classify individual depression levels using raw EEG signals. By employing a brain-inspired SNN model, our research provides fresh perspectives and advances knowledge of the neurological mechanisms underlying different levels of depression. The methodology employed in this study includes the utilization of the synaptic time dependent plasticity (STDP) learning rule within a 3-dimensional braintemplate structured SNN model. Furthermore, it encompasses the tasks of classifying and predicting individual outcomes, visually representing the structural alterations in the brain linked to the anticipated outcomes, and offering interpretations of the findings. Notably, our method achieves exceptional accuracy in classification, with average rates of 98% and 96% for eyes-closed and eyes-open states, respectively. These results significantly outperform state-of-the-art deep learning methods.</p
... This brain activity-related dataset is amenable to evaluation and interpretation through diverse machine learning methodologies. Analysis of EEG is used for the diagnosis of different neuropsychiatric disorders such as Schizophrenia [6]- [8], Alzheimer's [9], ADHD [10], [11], dementia [12], brain fatigue [13], [14], sleep disorders [15], [16], bipolar manic depression (BMD) [17], and Seizure [18]. ...
p>Depression severity can be classified into distinct phases based on the Beck depression inventory (BDI) test scores, a subjective questionnaire. However, quantitative assessment of depression may be attained through the examination and categorization of electroencephalography (EEG) signals. Spiking neural networks (SNNs), as the third generation of neural networks, incorporate biologically realistic algorithms, making them ideal for mimicking internal brain activities while processing EEG signals. This study introduces a novel framework that for the first time, combines an SNN architecture and a long short-term memory (LSTM) structure to model the brain’s underlying structures during different stages of depression and effectively classify individual depression levels using raw EEG signals. By employing a brain-inspired SNN model, our research provides fresh perspectives and advances knowledge of the neurological mechanisms underlying different levels of depression. The methodology employed in this study includes the utilization of the synaptic time dependent plasticity (STDP) learning rule within a 3-dimensional braintemplate structured SNN model. Furthermore, it encompasses the tasks of classifying and predicting individual outcomes, visually representing the structural alterations in the brain linked to the anticipated outcomes, and offering interpretations of the findings. Notably, our method achieves exceptional accuracy in classification, with average rates of 98% and 96% for eyes-closed and eyes-open states, respectively. These results significantly outperform state-of-the-art deep learning methods.</p
... By a hard look over the literature, it can be concluded that the majority of pain detection studies use Electroencephalogram (EEG) signal as a cheap and fast data acquisition method (Huishi Zhang, Sohrabpour, Lu, & He, 2016;Misra, Wang, Archer, Roy, & Coombes, 2017;Razavipour, Boostani, Kouchaki, & Afrasiabi, 2014;Schulz, 2015). Taking into account the high temporal resolution of EEG, analysis of these signals seems to provide real time physiological-based data to quantitatively track the dynamic changes in the brain activity during pain. ...
Past research emphasized on revealing the pain in different bands of electroencephalogram (EEG) including alpha band. In this study, we proposed an accurate and robust manner to differentiate pain intensities by deeply characterizing the alpha band in terms of distribution, spectrum and complexity changes in response to five different intensities of pain. Here, 44 subjects executed the Cold Pressor Task (CPT) and experienced five defined levels of pain while their EEGs were recorded via 34 silver channels. After de-noising and filtering the EEGs through the alpha band, 12 informative features were extracted from each channel in successive time frames. Since none of the features could discriminate the five classes, we applied the Kruskal-Wallis test to the features for observing their distribution in differentiating two or more classes. According to this result, we designed a decision tree classifier, where a Bayes optimized support vector machine (BSVM) was selected in each decision node. Sequential forward selection was applied in order to customize a subset of features for each BSVM. Our results provided 93.33% accuracy over the five classes and also generate 99.8% accuracy for separating pain and no-pain classes, which is statistically superior (P<0.05) to state-of-the-art methods over our collected dataset.
... Hence, highly accurate systems that are able to monitor and detect driver fatigue are valuable measures for decreasing fatigue-associated road accidents [140]. Recently, numerous analytical approaches have been applied to EEGs to quantitatively detect fatigue state [141]. Driving information concerning drivers' physiological signals, such as EEGs and eyetracking, are commonly used [142]. ...
The application of artificial intelligence (AI) technologies in assisting human electroencephalogram (EEG) analysis has become an active scientific field. This study aims to present a comprehensive review of the research field of AI-enhanced human EEG analysis. Using bibliometrics and topic modeling, research articles concerning AI-enhanced human EEG analysis collected from the Web of Science database during the period 2009–2018 were analyzed. After examining 2053 research articles published around the world, it was found that the annual number of articles had significantly grown from 78 to 468, with the USA and China being the most influential and prolific. The results of the keyword analysis showed that “electroencephalogram,” “brain–computer interface,” “classification,” “support vector machine,” “electroencephalography,” and “signal” were the most frequently used. The results of topic modeling and evolution analyses highlighted several important issues, including epileptic seizure detection, brain–machine interface, EEG classification, mental disorders, emotion, and alcoholism and anesthesia. The findings suggest that such visualization and analysis of the research articles could provide a comprehensive overview of the field for communities of practice and inquiry worldwide.
... Gruve et al. used NMF to extract the weight of the EEG channels to improve the accuracy of motor imagery detection [43] and classification of eye states [44]. With the successful application of NMF algorithm on EEG data, more and more NMF algorithms are constantly emerging [45][46][47][48]. In these studies, researchers used different NMF algorithms to analyze EEG data. ...
Background:
Nonnegative matrix factorization (NMF) has been successfully used for electroencephalography (EEG) spectral analysis. Since NMF was proposed in the 1990s, many adaptive algorithms have been developed. However, the performance of their use in EEG data analysis has not been fully compared. Here, we provide a comparison of four NMF algorithms in terms of accuracy of estimation, stability (repeatability of the results) and time complexity of algorithms with simulated data. In the practical application of NMF algorithms, stability plays an important role, which was an emphasis in the comparison. A Hierarchical clustering algorithm was implemented to evaluate the stability of NMF algorithms.
Results:
In simulation-based comprehensive analysis of fit, stability, accuracy of estimation and time complexity, hierarchical alternating least squares (HALS) low-rank NMF algorithm (lraNMF_HALS) outperformed the other three NMF algorithms. In the application of lraNMF_HALS for real resting-state EEG data analysis, stable and interpretable features were extracted.
Conclusion:
Based on the results of assessment, our recommendation is to use lraNMF_HALS, providing the most accurate and robust estimation.
... Electroencephalogram (EEG) signal is a modality which is recently increasing in use in pain study field [3], [7] [8] [9], [10]. Taking into account the high temporal resolution of electroencephalogram (EEG) signals, analysis of EEG seems to provide online physiological-based data to quantitatively track the dynamic changes in pain sensation. ...
... The reason is that the pain stimuli evoke, increases neural activity between 150 to 400 milliseconds after stimulus [16]. High overlap between many components of the Eps and the alpha band of EEG (8)(9)(10)(11)(12) Hz is the most challenging problem in this area. Eps are very informative features although they are not characterized in order to extract all pain related information as they deserve [12]. ...
... In different studies, pain related changes in the features extracted from all different frequency bands of EEG, have been observed via band power feature [8], [9], [10], [12] [13], [14]. However the significance of each band for pain is still unknown. ...
This chapter describes the proposed MFA framework, going over the requirements of such systems and detailing their implementation for the actual framework, including hardware and software integration. This chapter also approaches the experimental setup applied in all case studies, which will be discussed in the following chapters.
