Tawfeeq Shawly¹, Ahmed A. Alsheikhy^2,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 731-751, 2023, DOI:10.32604/cmc.2023.039433

Abstract More than 500,000 patients are diagnosed with breast cancer annually. Authorities worldwide reported a death rate of 11.6% in 2018. Breast tumors are considered a fatal disease and primarily affect middle-aged women. Various approaches to identify and classify the disease using different technologies, such as deep learning and image segmentation, have been developed. Some of these methods reach 99% accuracy. However, boosting accuracy remains highly important as patients’ lives depend on early diagnosis and specified treatment plans. This paper presents a fully computerized method to detect and categorize tumor masses in the breast using two deep-learning models and a classifier… More >

Arshia Arif¹, M. Jawad Khan^1,2,*, Kashif Javed¹, Hasan Sajid^1,2, Saddaf Rubab¹, Noman Naseer³, Talha Irfan Khan⁴

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 535-555, 2022, DOI:10.32604/cmc.2022.018318

Abstract For BCI systems, it is important to have an accurate and less complex architecture to control a device with enhanced accuracy. In this paper, a novel methodology for more accurate detection of the hemodynamic response has been developed using a multimodal brain-computer interface (BCI). An integrated classifier has been developed for achieving better classification accuracy using two modalities. An integrated EEG-fNIRS-based vector-phase analysis (VPA) has been conducted. An open-source dataset collected at the Technische Universität Berlin, including simultaneous electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals of 26 healthy participants during n-back tests, has been used for this research. Instrumental… More >

Ayesha Sarwar¹, Kashif Javed¹, Muhammad Jawad Khan¹, Saddaf Rubab¹, Oh-Young Song^2,*, Usman Tariq³

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3825-3840, 2021, DOI:10.32604/cmc.2021.016893

Abstract Brain-Computer Interface (BCI) is a system that provides a link between the brain of humans and the hardware directly. The recorded brain data is converted directly to the machine that can be used to control external devices. There are four major components of the BCI system: acquiring signals, preprocessing of acquired signals, features extraction, and classification. In traditional machine learning algorithms, the accuracy is insignificant and not up to the mark for the classification of multi-class motor imagery data. The major reason for this is, features are selected manually, and we are not able to get those features that give… More >

A. Benabid Najjar^1,*, N. AlSahly², R. AlShamass¹, M. Hosny²

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 1169-1189, 2021, DOI:10.32604/cmc.2021.014140

Abstract Technology has tremendously contributed to improving communication and facilitating daily activities. Brain-Computer Interface (BCI) study particularly emerged from the need to serve people with disabilities such as Amyotrophic Lateral Sclerosis (ALS). However, with the advancements in cost-effective electronics and computer interface equipment, the BCI study is flourishing, and the exploration of BCI applications for people without disabilities, to enhance normal functioning, is increasing. Particularly, the P300-based spellers are among the most promising applications of the BCI technology. In this context, the region-based paradigm for P300 BCI spellers was introduced in an effort to reduce the crowding effect and adjacency problem… More >

Talit Jumphoo¹, Monthippa Uthansakul¹, Pumin Duangmanee¹, Naeem Khan², Peerapong Uthansakul^1,*

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 961-976, 2021, DOI:10.32604/cmc.2020.012433

Abstract The patients with brain diseases (e.g., Stroke and Amyotrophic Lateral Sclerosis (ALS)) are often affected by the injury of motor cortex, which causes a muscular weakness. For this reason, they require rehabilitation with continuous physiotherapy as these diseases can be eased within the initial stages of the symptoms. So far, the popular control system for robot-assisted rehabilitation devices is only of two types which consist of passive and active devices. However, if there is a control system that can directly detect the motor functions, it will induce neuroplasticity to facilitate early motor recovery. In this paper, the control system, which… More >

M. Uma^a,c, T. Sheela^b

Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 747-757, 2018, DOI:10.1080/10798587.2017.1332804

Abstract Brain-Computer Interfaces (BCI) use Electroencephalography (EEG) signals recorded from the brain scalp, which enable a communication between the human and the outside world. The present study helps the patients who are people locked-in to manage their needs such as accessing of web url’s, sending/receiving sms to/from mobile device, personalized music player, personalized movie player, wheelchair control and home appliances control. In the proposed system, the user needs are designed as a button in the form of a matrix, in which the main panel of rows and columns button is flashed in 3 sec intervals. Subjects were asked to choose the… More >