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  • Open Access


    Enhanced Adaptive Brain-Computer Interface Approach for Intelligent Assistance to Disabled Peoples

    Ali Usman1, Javed Ferzund1, Ahmad Shaf1, Muhammad Aamir1, Samar Alqhtani2,*, Khlood M. Mehdar3, Hanan Talal Halawani4, Hassan A. Alshamrani5, Abdullah A. Asiri5, Muhammad Irfan6

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1355-1369, 2023, DOI:10.32604/csse.2023.034682

    Abstract Assistive devices for disabled people with the help of Brain-Computer Interaction (BCI) technology are becoming vital bio-medical engineering. People with physical disabilities need some assistive devices to perform their daily tasks. In these devices, higher latency factors need to be addressed appropriately. Therefore, the main goal of this research is to implement a real-time BCI architecture with minimum latency for command actuation. The proposed architecture is capable to communicate between different modules of the system by adopting an automotive, intelligent data processing and classification approach. Neuro-sky mind wave device has been used to transfer the data to our implemented server… More >

  • Open Access


    Design and Development of Low-cost Wearable Electroencephalograms (EEG) Headset

    Riaz Muhammad1, Ahmed Ali1, M. Abid Anwar1, Toufique Ahmed Soomro2,*, Omar AlShorman3, Adel Alshahrani4, Mahmoud Masadeh5, Ghulam Md Ashraf6,7, 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 >

  • Open Access


    Gaussian Process for a Single-channel EEG Decoder with Inconspicuous Stimuli and Eyeblinks

    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

    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 signal. The proposed decoder is… More >

  • Open Access


    Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

    Ayman Altameem1, Jaideep Singh Sachdev2, Vijander Singh2, Ramesh Chandra Poonia3, Sandeep Kumar4, Abdul Khader Jilani Saudagar5,*

    Computer Systems Science and Engineering, Vol.42, No.3, pp. 1095-1107, 2022, DOI:10.32604/csse.2022.023256

    Abstract Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We… More >

  • Open Access


    Enhanced Accuracy for Motor Imagery Detection Using Deep Learning for BCI

    Ayesha Sarwar1, Kashif Javed1, Muhammad Jawad Khan1, Saddaf Rubab1, Oh-Young Song2,*, 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 >

  • Open Access


    Soft Robotic Glove Controlling Using Brainwave Detection for Continuous Rehabilitation at Home

    Talit Jumphoo1, Monthippa Uthansakul1, Pumin Duangmanee1, Naeem Khan2, Peerapong Uthansakul1,*

    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 >

  • Open Access


    A Survey on Machine Learning Algorithms in Little-Labeled Data for Motor Imagery-Based Brain-Computer Interfaces

    Yuxi Jia1, Feng Li1,2, Fei Wang1,2,*, Yan Gui1,2,3

    Journal of Information Hiding and Privacy Protection, Vol.1, No.1, pp. 11-21, 2019, DOI:10.32604/jihpp.2019.05979

    Abstract The Brain-Computer Interfaces (BCIs) had been proposed and used in therapeutics for decades. However, the need of time-consuming calibration phase and the lack of robustness, which are caused by little-labeled data, are restricting the advance and application of BCI, especially for the BCI based on motor imagery (MI). In this paper, we reviewed the recent development in the machine learning algorithm used in the MI-based BCI, which may provide potential solutions for addressing the issue. We classified these algorithms into two categories, namely, and enhancing the representation and expanding the training set. Specifically, these methods of enhancing the representation of… More >

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