Ashit Kumar Dutta^1,*, Yasser Albagory², Manal Al Faraj¹, Yasir A. M. Eltahir³, Abdul Rahaman Wahab Sait⁴

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1517-1529, 2023, DOI:10.32604/csse.2023.026482 - 15 June 2022

Abstract The recently developed machine learning (ML) models have the ability to obtain high detection rate using biomedical signals. Therefore, this article develops an Optimal Sparse Autoencoder based Sleep Stage Classification Model on Electroencephalography (EEG) Biomedical Signals, named OSAE-SSCEEG technique. The major intention of the OSAE-SSCEEG technique is to find the sleep stage disorders using the EEG biomedical signals. The OSAE-SSCEEG technique primarily undergoes preprocessing using min-max data normalization approach. Moreover, the classification of sleep stages takes place using the Sparse Autoencoder with Smoothed Regularization (SAE-SR) with softmax (SM) approach. Finally, the parameter optimization of the More >

Abdullah A Asiri¹, Akhtar Badshah², Fazal Muhammad^3,*, Hassan A Alshamrani¹, Khalil Ullah⁴, Khalaf A Alshamrani¹, Samar Alqhtani⁵, Muhammad Irfan⁶, Hanan Talal Halawani⁷, Khlood M Mehdar⁸

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5075-5089, 2022, DOI:10.32604/cmc.2022.031156 - 28 July 2022

Abstract Electroencephalogram (EEG) is a medical imaging technology that can measure the electrical activity of the scalp produced by the brain, measured and recorded chronologically the surface of the scalp from the brain. The recorded signals from the brain are rich with useful information. The inference of this useful information is a challenging task. This paper aims to process the EEG signals for the recognition of human emotions specifically happiness, anger, fear, sadness, and surprise in response to audiovisual stimuli. The EEG signals are recorded by placing neurosky mindwave headset on the subject’s scalp, in response… More >

Abeer Abdulaziz AlArfaj, Hanan Ahmed Hosni Mahmoud^*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5655-5669, 2022, DOI:10.32604/cmc.2022.031135 - 28 July 2022

Abstract Lie detection test is highly significant task due to its impact on criminology and society. Computerized lie detection test model using electroencephalogram (EEG) signals is studied in literature. In this paper we studied deep learning framework in lie detection test paradigm. First, we apply a preprocessing technique to utilize only a small fragment of the EEG image instead of the whole image. Our model describes a temporal feature map of the EEG signals measured during the lie detection test. A deep learning attention model (V-TAM) extracts the temporal map vector during the learning process. This… More >

Huiping Jiang^1,*, 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 - 16 June 2022

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… More >

Jian Qiang Wang¹, Wei Fang^1,2,*, Victor S. Sheng³

Journal of New Media, Vol.4, No.2, pp. 73-84, 2022, DOI:10.32604/jnm.2022.027040 - 13 June 2022

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 More >

Jian Liu¹, Yipeng Du¹, Xiang Wang^1,*, Wuguang Yue², Jim Feng³

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1995-2011, 2022, DOI:10.32604/cmc.2022.029073 - 18 May 2022

Abstract Epilepsy is a common neurological disease and severely affects the daily life of patients. The automatic detection and diagnosis system of epilepsy based on electroencephalogram (EEG) is of great significance to help patients with epilepsy return to normal life. With the development of deep learning technology and the increase in the amount of EEG data, the performance of deep learning based automatic detection algorithm for epilepsy EEG has gradually surpassed the traditional hand-crafted approaches. However, the neural architecture design for epilepsy EEG analysis is time-consuming and laborious, and the designed structure is difficult to adapt… More >

Nur Syazreen Ahmad^*, Jia Hui Teo, Patrick Goh

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 611-628, 2022, DOI:10.32604/cmc.2022.025823 - 18 May 2022

Abstract A single-channel electroencephalography (EEG) device, despite being widely accepted due to convenience, ease of deployment and suitability for use in complex environments, typically poses a great challenge for reactive brain-computer interface (BCI) applications particularly when a continuous command from users is desired to run a motorized actuator with different speed profiles. In this study, a combination of an inconspicuous visual stimulus and voluntary eyeblinks along with a machine learning-based decoder is considered as a new reactive BCI paradigm to increase the degree of freedom and minimize mismatches between the intended dynamic command and transmitted control… More >

Md. Maniruzzaman¹, Jungpil Shin^1,*, Md. Al Mehedi Hasan¹, Akira Yasumura²

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5179-5195, 2022, DOI:10.32604/cmc.2022.028339 - 21 April 2022

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… More >

Souad Larabi-Marie-Sainte¹, Eatedal Alabdulkreem², Mohammad Alamgeer³, Mohamed K Nour⁴, Anwer Mustafa Hilal^5,*, Mesfer Al Duhayyim⁶, Abdelwahed Motwakel⁵, Ishfaq Yaseen⁵

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4589-4601, 2022, DOI:10.32604/cmc.2022.027922 - 21 April 2022

Abstract Electroencephalography (EEG) eye state classification becomes an essential tool to identify the cognitive state of humans. It can be used in several fields such as motor imagery recognition, drug effect detection, emotion categorization, seizure detection, etc. With the latest advances in deep learning (DL) models, it is possible to design an accurate and prompt EEG EyeState classification problem. In this view, this study presents a novel compact bat algorithm with deep learning model for biomedical EEG EyeState classification (CBADL-BEESC) model. The major intention of the CBADL-BEESC technique aims to categorize the presence of EEG EyeState. More >

Manar Ahmed Hamza^1,*, 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 - 21 April 2022

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 More >