ABSTRACT
Performing mathematical calculations is a cognitive activity that can affect biological signals. This study aims to examine the changes in electroencephalogram (EEG) and electrocardiogram (ECG) signals of 36 healthy subjects during the performance of arithmetic tasks. To process EEG signals in different frequency bands, the energy and entropy of entropy (EoE) were extracted from the power spectrum and phase spectrum, respectively. Statistical analysis was conducted to determine meaningful features. These features were sent into support vector machine (SVM) and multi-layer perception (MLP) classifiers to assess their capability in separating math and rest classes. Results indicated the highest classification accuracy of 98.4% for classifying good counters in math and rest state using the MLP method. Based on the majority of features selected for each EEG channel, discriminative brain areas were identified. Analyzing EEG signals proved that math calculation may have multiple influences on various parts of the brain. By comparing good counters' brain activities to those in a resting state, prominent changes were observed in the F4, C4, T4, T5, P3, and O2 areas. However, O1 and O2 channels showed significant changes in the brain of bad counters compared to the resting state. Considering ECG signals also demonstrated that during math calculation the number of heart rates per minute surpasses the rest state. These alterations can occur due to cognitive abilities or emotional processes which were observed to be prominent in subjects who performed more accurate calculations.
Subject(s)
Brain , Electrocardiography , Electroencephalography , Heart Rate , Support Vector Machine , Humans , Brain/physiology , Male , Heart Rate/physiology , Female , Adult , Young Adult , Cognition/physiologyABSTRACT
Developing an electroencephalography (EEG)-based brain-computer interface (BCI) system is crucial to enhancing the control of external prostheses by accurately distinguishing various movements through brain signals. This innovation can provide comfortable circumstances for the populace who have movement disabilities. This study combined the most prospering methods used in BCI systems, including one-versus-rest common spatial pattern (OVR-CSP) and convolutional neural network (CNN), to automatically extract features and classify eight different movements of the shoulder, wrist, and elbow via EEG signals. The number of subjects who participated in the experiment was 10, and their EEG signals were recorded while performing movements at fast and slow speeds. We used preprocessing techniques before transforming EEG signals into another space by OVR-CSP, followed by sending signals into the CNN architecture consisting of four convolutional layers. Moreover, we extracted feature vectors after applying OVR-CSP and considered them as inputs to KNN, SVM, and MLP classifiers. Then, the performance of these classifiers was compared with the CNN method. The results demonstrated that the classification of eight movements using the proposed CNN architecture obtained an average accuracy of 97.65% for slow movements and 96.25% for fast movements in the subject-independent model. This method outperformed other classifiers with a substantial difference; ergo, it can be useful in improving BCI systems for better control of prostheses.
Subject(s)
Brain-Computer Interfaces , Electroencephalography , Movement , Neural Networks, Computer , Humans , Electroencephalography/methods , Movement/physiology , Adult , Male , Brain/physiology , Signal Processing, Computer-Assisted , Female , Young Adult , Algorithms , Wrist/physiologyABSTRACT
Long-term use of methamphetamine (meth) causes cognitive and neuropsychological impairments. Analysing the impact of this substance on the human brain can aid prevention and treatment efforts. In this study, the electroencephalogram (EEG) signals of meth abusers in the abstinence period and healthy subjects were recorded during eyes-closed and eyes-opened states to distinguish the brain regions that meth can significantly influence. In addition, a decision support system (DSS) was introduced as a complementary method to recognize substance users accompanied by biochemical tests. According to these goals, the recorded EEG signals were pre-processed and decomposed into frequency bands using the discrete wavelet transform (DWT) method. For each frequency band, energy, KS entropy, Higuchi and Katz fractal dimensions of signals were calculated. Then, statistical analysis was applied to select features whose channels contain a p-value less than 0.05. These features between two groups were compared, and the location of channels containing more features was specified as discriminative brain areas. Due to evaluating the performance of features and distinguishing the two groups in each frequency band, features were fed into a k-nearest neighbour (KNN), support vector machine (SVM), multilayer perceptron neural networks (MLP) and linear discriminant analysis (LDA) classifiers. The results indicated that prolonged consumption of meth has a considerable impact on the brain areas responsible for working memory, motor function, attention, visual interpretation, and speech processing. Furthermore, the best classification accuracy, almost 95.8%, was attained in the gamma band during the eyes-closed state.
Subject(s)
Algorithms , Brain , Humans , Wavelet Analysis , Electroencephalography/methods , Support Vector MachineABSTRACT
BACKGROUND: In recent years dramatic changes in multiple sclerosis (MS) incidence have been reported in different provinces in Iran. This study was conducted to assess MS incidence temporal trends from March 21, 2005, to March 20, 2020, and provide a forecast until the end of 2025 in Shahroud county. METHODS: This longitudinal study was carried out based on the data obtained from the MS registration system in Shahroud county. First, the annual incidence rates were calculated based on the year of diagnosis and smoothed using the Empirical Bayesian Method. Then temporal trends and annual percent change (APC) of MS incidence were analyzed using Joinpoint (JP) regression. Finally, the univariate time series model analysis was used to estimate the MS incidence trend until the end of 2025. RESULTS: A total of 234 newly diagnosed cases (60 [25.64%] males and 174 [74.36.4%] females) were examined in this study. The mean age of patients at the time of diagnosis was 31.40 ± 3.78. It was 32.01 ± 6.35 and 30.66 ± 4.27 years for males and females, respectively (P<0.22). The mean annual MS incidence was 5.99 ± 1.46, 3.03 ± 0.21, and 8.98 ± 2.79 per 100,000 in overall, males and females respectively. The MS incidence increased significantly from 5.67 (95% CI: 3.63-7.99) in 2005 to 7.58 (95% CI: 5.17-10.28) in 2020 with an APC of 4.5 (2.8 - 6.1). The MS incidence had a non-linear time trend in the study period and the best time trend fitted to the annual MS incidence trend was the non-linear quadratic curve. Based on this model, the annual MS incidence is expected to increase until the end of 2025. CONCLUSION: Shahroud county is one of the high-risk areas for MS and the increasing trend of MS incidence in it is similar to regional and global changes. This study, also, showed that MS incidence in Shahroud county will be increasing in the coming years.