Jiali Wang¹, Bing Li², Chengyu Qiu¹, Xinyun Zhang¹, Yuting Cheng¹, Peihua Wang¹, Ta Zhou³, Hong Ge², Yuanpeng Zhang^1,3,*, Jing Cai^3,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4843-4866, 2023, DOI:10.32604/cmc.2023.037457

Abstract Epilepsy is a central nervous system disorder in which brain activity becomes abnormal. Electroencephalogram (EEG) signals, as recordings of brain activity, have been widely used for epilepsy recognition. To study epileptic EEG signals and develop artificial intelligence (AI)-assist recognition, a multi-view transfer learning (MVTL-LSR) algorithm based on least squares regression is proposed in this study. Compared with most existing multi-view transfer learning algorithms, MVTL-LSR has two merits: (1) Since traditional transfer learning algorithms leverage knowledge from different sources, which poses a significant risk to data privacy. Therefore, we develop a knowledge transfer mechanism that can protect the security of source… More >

Srikanth Cherukuvada, R. Kayalvizhi^*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4101-4118, 2023, DOI:10.32604/cmc.2023.036207

Abstract The term Epilepsy refers to a most commonly occurring brain disorder after a migraine. Early identification of incoming seizures significantly impacts the lives of people with Epilepsy. Automated detection of epileptic seizures (ES) has dramatically improved the life quality of the patients. Recent Electroencephalogram (EEG) related seizure detection mechanisms encountered several difficulties in real-time. The EEGs are the non-stationary signal, and seizure patterns would change with patients and recording sessions. Further, EEG data were disposed to wide noise varieties that adversely moved the recognition accuracy of ESs. Artificial intelligence (AI) methods in the domain of ES analysis use traditional deep… More >

Umashankar Ganesan^1,*, A. Vimala Juliet², R. Amala Jenith Joshi³

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2849-2863, 2023, DOI:10.32604/iasc.2023.033759

Abstract Brain signal analysis plays a significant role in attaining data related to motor activities. The parietal region of the brain plays a vital role in muscular movements. This approach aims to demonstrate a unique technique to identify an ideal region of the human brain that generates signals responsible for muscular movements; perform statistical analysis to provide an absolute characterization of the signal and validate the obtained results using a prototype arm. This can enhance the practical implementation of these frequency extractions for future neuro-prosthetic applications and the characterization of neurological diseases like Parkinson’s disease (PD). To play out this handling… More >

Qianlei Wang^1,2,3,*, Zaijun Wang³, Renhe Xiong⁴, Xingbin Liao^1,2, Xiaojun Tan⁵

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1999-2020, 2023, DOI:10.32604/csse.2023.034183

Abstract How to accurately recognize the mental state of pilots is a focus in civil aviation safety. The mental state of pilots is closely related to their cognitive ability in piloting. Whether the cognitive ability meets the standard is related to flight safety. However, the pilot's working state is unique, which increases the difficulty of analyzing the pilot's mental state. In this work, we proposed a Convolutional Neural Network (CNN) that merges attention to classify the mental state of pilots through electroencephalography (EEG). Considering the individual differences in EEG, semi-supervised learning based on improved K-Means is used in the model training… More >

S. Praveenkumar^*, T. Karthick

Computer Systems Science and Engineering, Vol.45, No.3, pp. 2417-2433, 2023, DOI:10.32604/csse.2023.032480

Abstract Because stress has such a powerful impact on human health, we must be able to identify it automatically in our everyday lives. The human activity recognition (HAR) system use data from several kinds of sensors to try to recognize and evaluate human actions automatically recognize and evaluate human actions. Using the multimodal dataset DEAP (Database for Emotion Analysis using Physiological Signals), this paper presents deep learning (DL) technique for effectively detecting human stress. The combination of vision-based and sensor-based approaches for recognizing human stress will help us achieve the increased efficiency of current stress recognition systems and predict probable actions… More >

Anwer Mustafa Hilal^1,*, Amal Al-Rasheed², Jaber S. Alzahrani³, Majdy M. Eltahir⁴, Mesfer Al Duhayyim⁵, Nermin M. Salem⁶, Ishfaq Yaseen¹, Abdelwahed Motwakel¹

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1249-1263, 2023, DOI:10.32604/csse.2023.030603

Abstract Sleep plays a vital role in optimum working of the brain and the body. Numerous people suffer from sleep-oriented illnesses like apnea, insomnia, etc. Sleep stage classification is a primary process in the quantitative examination of polysomnographic recording. Sleep stage scoring is mainly based on experts’ knowledge which is laborious and time consuming. Hence, it can be essential to design automated sleep stage classification model using machine learning (ML) and deep learning (DL) approaches. In this view, this study focuses on the design of Competitive Multi-verse Optimization with Deep Learning Based Sleep Stage Classification (CMVODL-SSC) model using Electroencephalogram (EEG) signals.… More >

Farah Mohammad^*, Saad Al-Ahmadi

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 623-639, 2023, DOI:10.32604/cmc.2023.032552

Abstract

There exists various neurological disorder based diseases like tumor, sleep disorder, headache, dementia and Epilepsy. Among these, epilepsy is the most common neurological illness in humans, comparable to stroke. Epilepsy is a severe chronic neurological illness that can be discovered through analysis of the signals generated by brain neurons and brain Magnetic resonance imaging (MRI). Neurons are intricately coupled in order to communicate and generate signals from human organs. Due to the complex nature of electroencephalogram (EEG) signals and MRI’s the epileptic seizures detection and brain related problems diagnosis becomes a challenging task. Computer based techniques and machine learning models… More >

Hanan Abdullah Mengash¹, Alaaeldin M. Hafez², Hanan A. Hosni Mahmoud^3,*

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3129-3140, 2023, DOI:10.32604/iasc.2023.030836

Abstract Encephalitis is a brain inflammation disease. Encephalitis can yield to seizures, motor disability, or some loss of vision or hearing. Sometimes, encephalitis can be a life-threatening and proper diagnosis in an early stage is very crucial. Therefore, in this paper, we are proposing a deep learning model for computerized detection of Encephalitis from the electroencephalogram data (EEG). Also, we propose a Density-Based Clustering model to classify the distinctive waves of Encephalitis. Customary clustering models usually employ a computed single centroid virtual point to define the cluster configuration, but this single point does not contain adequate information. To precisely extract accurate… More >

Fei Wang^1,2, Yinxing Wan¹, Man Li^1,2, Haiyun Huang^1,2, Li Li¹, Xueying Hou¹, Jiahui Pan^1,2, Zhenfu Wen³, Jingcong Li^1,2,*

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3573-3586, 2023, DOI:10.32604/iasc.2023.029698

Abstract Fatigue is a state commonly caused by overworked, which seriously affects daily work and life. How to detect mental fatigue has always been a hot spot for researchers to explore. Electroencephalogram (EEG) is considered one of the most accurate and objective indicators. This article investigated the development of classification algorithms applied in EEG-based fatigue detection in recent years. According to the different source of the data, we can divide these classification algorithms into two categories, intra-subject (within the same subject) and cross-subject (across different subjects). In most studies, traditional machine learning algorithms with artificial feature extraction methods were commonly used… More >

Riaz Muhammad¹, Ahmed Ali¹, M. Abid Anwar¹, Toufique Ahmed Soomro^2,*, Omar AlShorman³, Adel Alshahrani⁴, Mahmoud Masadeh⁵, Ghulam Md Ashraf^6,7, Naif H. Ali⁸, Muhammad Irfan⁹, Athanasios Alexiou¹⁰

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2821-2835, 2023, DOI:10.32604/iasc.2023.026279

Abstract Electroencephalogram (EEG) is a method of capturing the electrophysiological signal of the brain. An EEG headset is a wearable device that records electrophysiological data from the brain. This paper presents the design and fabrication of a customized low-cost Electroencephalogram (EEG) headset based on the open-source OpenBCI Ultracortex Mark IV system. The electrode placement locations are modified under a 10–20 standard system. The fabricated headset is then compared to commercially available headsets based on the following parameters: affordability, accessibility, noise, signal quality, and cost. First, the data is recorded from 20 subjects who used the EEG Headset, and signals were recorded.… More >