Chayut Bunterngchit¹, Laith H. Baniata², Mohammad H. Baniata³, Ashraf ALDabbas⁴, Mohannad A. Khair⁵, Thanaphon Chearanai⁶, Sangwoo Kang^2,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 517-536, 2025, DOI:10.32604/cmc.2025.060368 - 26 March 2025

Abstract Stroke is a leading cause of death and disability worldwide, significantly impairing motor and cognitive functions. Effective rehabilitation is often hindered by the heterogeneity of stroke lesions, variability in recovery patterns, and the complexity of electroencephalography (EEG) signals, which are often contaminated by artifacts. Accurate classification of motor imagery (MI) tasks, involving the mental simulation of movements, is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific variability. To address these challenges, this study introduces a graph-attentive convolutional long short-term memory (LSTM) network (GACL-Net), a novel hybrid deep learning model… More >

Umapathi Krishnamoorthy^1,*, Shanmugam Jagan², Mohammed Zakariah³, Abdulaziz S. Almazyad^4,*, K. Gurunathan⁵

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3903-3926, 2024, DOI:10.32604/cmc.2024.055910 - 19 December 2024

Abstract Brain signal analysis from electroencephalogram (EEG) recordings is the gold standard for diagnosing various neural disorders especially epileptic seizure. Seizure signals are highly chaotic compared to normal brain signals and thus can be identified from EEG recordings. In the current seizure detection and classification landscape, most models primarily focus on binary classification—distinguishing between seizure and non-seizure states. While effective for basic detection, these models fail to address the nuanced stages of seizures and the intervals between them. Accurate identification of per-seizure or interictal stages and the timing between seizures is crucial for an effective seizure… More >

S. Praveenkumar^*, T. Karthick

Computer Systems Science and Engineering, Vol.48, No.4, pp. 1073-1073, 2024, DOI:10.32604/csse.2024.054414 - 17 July 2024

Abstract This article has no abstract. More >

Xin Xiong¹, Jing Zhang¹, Sanli Yi¹, Chunwu Wang², Ruixiang Liu³, Jianfeng He^1,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4659-4681, 2024, DOI:10.32604/cmc.2024.050528 - 20 June 2024

Abstract The analysis of microstates in EEG signals is a crucial technique for understanding the spatiotemporal dynamics of brain electrical activity. Traditional methods such as Atomic Agglomerative Hierarchical Clustering (AAHC), K-means clustering, Principal Component Analysis (PCA), and Independent Component Analysis (ICA) are limited by a fixed number of microstate maps and insufficient capability in cross-task feature extraction. Tackling these limitations, this study introduces a Global Map Dissimilarity (GMD)-driven density canopy K-means clustering algorithm. This innovative approach autonomously determines the optimal number of EEG microstate topographies and employs Gaussian kernel density estimation alongside the GMD index for… More >

Meiyan Zhang¹, Boqi Zhao¹, Jipu Li², Qisong Wang^1,*, Dan Liu¹, Jinwei Sun¹, Jingxiao Liao^1,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1039-1061, 2024, DOI:10.32604/cmc.2024.048643 - 25 April 2024

Abstract Driving fatigue is a physiological phenomenon that often occurs during driving. After the driver enters a fatigued state, the attention is lax, the response is slow, and the ability to deal with emergencies is significantly reduced, which can easily cause traffic accidents. Therefore, studying driver fatigue detection methods is significant in ensuring safe driving. However, the fatigue state of actual drivers is easily interfered with by the external environment (glasses and light), which leads to many problems, such as weak reliability of fatigue driving detection. Moreover, fatigue is a slow process, first manifested in physiological… More >

Hafza Ayesha Siddiqa¹, Muhammad Irfan¹, Saadullah Farooq Abbasi^2,*, Wei Chen¹

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1759-1778, 2023, DOI:10.32604/cmc.2023.041970 - 29 November 2023

Abstract Automatic sleep staging of neonates is essential for monitoring their brain development and maturity of the nervous system. EEG based neonatal sleep staging provides valuable information about an infant’s growth and health, but is challenging due to the unique characteristics of EEG and lack of standardized protocols. This study aims to develop and compare 18 machine learning models using Automated Machine Learning (autoML) technique for accurate and reliable multi-channel EEG-based neonatal sleep-wake classification. The study investigates autoML feasibility without extensive manual selection of features or hyperparameter tuning. The data is obtained from neonates at post-menstrual… More >

Fei Wang^1,2, Li Li¹, Yinxing Wan¹, Zhuorong Li¹, Lixian Luo³, Bangshun Hu¹, Jiahui Pan^1,2, Zhenfu Wen⁴, Haiyun Huang^1,2,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2301-2316, 2023, DOI:10.32604/cmc.2023.037727 - 30 August 2023

Abstract Sleep spindles are an electroencephalogram (EEG) biomarker of non-rapid eye movement (NREM) sleep and have important implications for clinical diagnosis and prognosis. However, it is challenging to accurately detect sleep spindles due to the complexity of the human brain and the uncertainty of neural mechanisms. To improve the reliability and objectivity of sleep spindle detection and to compensate for the limitations of manual annotation, this study proposes a new automatic detection algorithm based on Matching Pursuit (MP) and Least Squares Boosting (LSBoost), where the automatic sleep spindle detection algorithm can help reduce the visual annotation… More >

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 - 29 April 2023

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 More >

Srikanth Cherukuvada, R. Kayalvizhi^*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4101-4118, 2023, DOI:10.32604/cmc.2023.036207 - 31 March 2023

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… 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 - 15 March 2023

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).… More >