Feature Extraction and Classification Algorithm of Brain-computer Interface Based on Human Brain Central Nervous System

2018 ◽  
Vol 16 (5) ◽  
Author(s):  
Minjun Zhang ◽  
Qingyi Hua ◽  
Wei Jia ◽  
Rui Chen ◽  
Hui Su ◽  
...  
2021 ◽  
Vol 2 (3) ◽  
pp. 79-89
Author(s):  
Md Ahnaf Shariar ◽  
Syeda Maliha Monowara ◽  
Md. Shafayat Ul Islam ◽  
Muhammed Junaid Noor Jawad ◽  
Saifur Rahman Sabuj

The Brain-Computer Interface (BCI) is a system based on brainwaves that can be used to translate and comprehend the innumerable activities of the brain. Brainwave refers to the bioelectric impulses invariably produced in the human brain during neurotransmission, often measured as the action potential. Moreover, BCI essentially uses the widely studied Electroencephalography (EEG) technique to capture brainwave data. Paralysis generally occurs when there is a disturbance in the central nervous system prompted by a neurodegenerative or unforeseen event. To overcome the obstacles associated with paralysis, this paper on the brainwave-assistive system is based on the BCI incorporated with Internet-of-things. BCI can be implemented to achieve control over external devices and applications. For instance, the process of cursor control, motor control, neuroprosthetics and wheelchair control, etc. In this paper, the OpenBCI Cyton-biosensing board has been used for the collection of the EEG data. The accumulated EEG data is executed subsequently to obtain control over the respective systems in real-time. Hence, it can be concluded that the experiments of the paper support the idea of controlling an interfaced system through the real-time application of EEG data.


2018 ◽  
Vol 11 (2) ◽  
pp. 29-34 ◽  
Author(s):  
Fanny Monori ◽  
Stefan Oniga

Abstract BCI (Brain-Computer Interface) is a technology which goal is to create and manage a connection between the human brain and a computer with the help of EEG signals. In the last decade consumer-grade BCI devices became available thus giving opportunity to develop BCI applications outside of clinical settings. In this paper we use a device called NeuroSky MindWave Mobile. We investigate what type of information can be deducted from the data acquired from this device, and we evaluate whether it can help us in BCI applications. Our methods of processing the data involves feature extraction methods, and neural networks. Specifically, we make experiments with finding patterns in the data by binary and multiclass classification. With these methods we could detect sharp changes in the signal such as blinking patterns, but we could not extract more complex information successfully.


2021 ◽  
Author(s):  
Patrick A. Lewis

Abstract Cellular control of vesicle biology and trafficking is critical for cell viability, with disruption of these pathways within the cells of the central nervous system resulting in neurodegeneration and disease. The past two decades have provided important insights into both the genetic and biological links between vesicle trafficking and neurodegeneration. In this essay, the pathways that have emerged as being critical for neuronal survival in the human brain will be discussed – illustrating the diversity of proteins and cellular events with three molecular case studies drawn from different neurological diseases.


2019 ◽  
Vol 5 (1) ◽  
pp. 91
Author(s):  
Abdi - Reza

Electrocorticography-based brain computer interface has accepted recognition as modality connecting human brain to computer device for its signal recording excellence and stability. Implantation for medical purpose has welcomed this modality for bypassing existing nervous system and natural organ to create alternative solution towards previously-unsolved medical problems. Clinical trials have already initiated for Electrocorticography-based BCI implantation in adult. Therefore, bridging a direct brain to computer connectivity in pediatric patient should also become possible. However, several characteristics has made Electrocorticography-based BCI for pediatric patient more complex and should await for further technical solution rather than its adult counterparts. Penghubung otak-komputer berbasis elektrokortikografi telah diakui sebagai metode menghubungkan sinyal otak langsung ke komputer dengan kualitas dan stabilitas perekaman sinyal yang andal. Sinyal otak yang dihubungkan langsung ke komputer dapat memberikan pertolongan bagi pasien dengan kelumpuhan oleh berbagai sebab. Uji penerapan klinis dengan implantasi penghubung otak-komputer berbasis elektrokortikografi telah diujilaksanakan pada pasien dewasa (Vastenseel et al., 2016). Dimasa mendatang, kelompok pasien anak diharapkan dapat memperoleh manfaat dari terobosan teknologi kedokteran tersebut. Beberapa perbedaan karakteristik antara pasien dewasa dan anak membatasi kemungkinan implantasi jangka panjang pasien anak. Solusi bersama ilmu kedokteran dan teknik akan membuka kesempatan dimasa datang bagi implantasi elektroda pada anak.


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