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Search Results (12)
  • Open Access


    Multi-View & Transfer Learning for Epilepsy Recognition Based on EEG Signals

    Jiali Wang1, Bing Li2, Chengyu Qiu1, Xinyun Zhang1, Yuting Cheng1, Peihua Wang1, Ta Zhou3, Hong Ge2, Yuanpeng Zhang1,3,*, Jing Cai3,*

    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 >

  • Open Access


    Feature Selection with Deep Belief Network for Epileptic Seizure Detection on EEG Signals

    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 >

  • Open Access


    Spectral Analysis and Validation of Parietal Signals for Different Arm Movements

    Umashankar Ganesan1,*, 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 >

  • Open Access


    Competitive Multi-Verse Optimization with Deep Learning Based Sleep Stage Classification

    Anwer Mustafa Hilal1,*, 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 >

  • Open Access


    Epileptic Seizures Diagnosis Using Amalgamated Extremely Focused EEG Signals and Brain MRI

    Farah Mohammad*, Saad Al-Ahmadi

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


    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 >

  • Open Access


    EEG Emotion Recognition Using an Attention Mechanism Based on an Optimized Hybrid Model

    Huiping Jiang1,*, Demeng Wu1, Xingqun Tang1, Zhongjie Li1, Wenbo Wu2

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2697-2712, 2022, DOI:10.32604/cmc.2022.027856

    Abstract Emotions serve various functions. The traditional emotion recognition methods are based primarily on readily accessible facial expressions, gestures, and voice signals. However, it is often challenging to ensure that these non-physical signals are valid and reliable in practical applications. Electroencephalogram (EEG) signals are more successful than other signal recognition methods in recognizing these characteristics in real-time since they are difficult to camouflage. Although EEG signals are commonly used in current emotional recognition research, the accuracy is low when using traditional methods. Therefore, this study presented an optimized hybrid pattern with an attention mechanism (FFT_CLA) for EEG emotional recognition. First, the… More >

  • Open Access


    Prediction of Epileptic EEG Signal Based on SECNN-LSTM

    Jian Qiang Wang1, Wei Fang1,2,*, Victor S. Sheng3

    Journal of New Media, Vol.4, No.2, pp. 73-84, 2022, DOI:10.32604/jnm.2022.027040

    Abstract Brain-Computer Interface (BCI) technology is a way for humans to explore the mysteries of the brain and has applications in many areas of real life. People use this technology to capture brain waves and analyze the electroencephalograph (EEG) signal for feature extraction. Take the medical field as an example, epilepsy disease is threatening human health every moment. We propose a convolutional neural network SECNN-LSTM framework based on the attention mechanism can automatically perform feature extraction and analysis on the collected EEG signals of patients to complete the prediction of epilepsy diseases, overcoming the problem that the disease requires long time… More >

  • Open Access


    Efficient Feature Selection and Machine Learning Based ADHD Detection Using EEG Signal

    Md. Maniruzzaman1, Jungpil Shin1,*, Md. Al Mehedi Hasan1, Akira Yasumura2

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5179-5195, 2022, DOI:10.32604/cmc.2022.028339

    Abstract Attention deficit hyperactivity disorder (ADHD) is one of the most common psychiatric and neurobehavioral disorders in children, affecting 11% of children worldwide. This study aimed to propose a machine learning (ML)-based algorithm for discriminating ADHD from healthy children using their electroencephalography (EEG) signals. The study included 61 children with ADHD and 60 healthy children aged 7–12 years. Different morphological and time-domain features were extracted from EEG signals. The t-test (p-value < 0.05) and least absolute shrinkage and selection operator (LASSO) were used to select potential features of children with ADHD and enhance the classification accuracy. The selected potential features were… More >

  • Open Access


    Evolutionary Algorithsm with Machine Learning Based Epileptic Seizure Detection Model

    Manar Ahmed Hamza1,*, Noha Negm2, Shaha Al-Otaibi3, Amel A. Alhussan4, Mesfer Al Duhayyim5, Fuad Ali Mohammed Al-Yarimi2, Mohammed Rizwanullah1, Ishfaq Yaseen1

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4541-4555, 2022, DOI:10.32604/cmc.2022.027048

    Abstract Machine learning (ML) becomes a familiar topic among decision makers in several domains, particularly healthcare. Effective design of ML models assists to detect and classify the occurrence of diseases using healthcare data. Besides, the parameter tuning of the ML models is also essential to accomplish effective classification results. This article develops a novel red colobuses monkey optimization with kernel extreme learning machine (RCMO-KELM) technique for epileptic seizure detection and classification. The proposed RCMO-KELM technique initially extracts the chaotic, time, and frequency domain features in the actual EEG signals. In addition, the min-max normalization approach is employed for the pre-processing of… More >

  • Open Access


    Intelligent Machine Learning Based EEG Signal Classification Model

    Mesfer Al Duhayyim1, Haya Mesfer Alshahrani2, Fahd N. Al-Wesabi3, Mohammed Abdullah Al-Hagery4, Anwer Mustafa Hilal5,*, Abu Sarwar Zaman5

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1821-1835, 2022, DOI:10.32604/cmc.2022.021119

    Abstract In recent years, Brain-Computer Interface (BCI) system gained much popularity since it aims at establishing the communication between human brain and computer. BCI systems are applied in several research areas such as neuro-rehabilitation, robots, exoeskeletons, etc. Electroencephalography (EEG) is a technique commonly applied in capturing brain signals. It is incorporated in BCI systems since it has attractive features such as non-invasive nature, high time-resolution output, mobility and cost-effective. EEG classification process is highly essential in decision making process and it incorporates different processes namely, feature extraction, feature selection, and classification. With this motivation, the current research paper presents an Intelligent… More >

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