Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userBrain Machine Interface
واجهات التخاطب بين الدماغ والحاسب
2135
3 185
0
(
0
)
Created by
Alaa Joukhadar
Ask ChatGPT about the research
واجهة التخاطب بين الحاسب والدماغ (BCI)هي آخر تطورات واجهة التخاطب بين الحاسب والانسان (HCI). فعلى عكس أدوات الادخال التقليدية (لوحة المفاتيح، الفأرة....) تقوم الـ (BCI) بقراءة إشارات الدماغ من مناطق مختلفة من رأس الإنسان وتترجم هذه الإشارات إلى أوامر تستطيع التحكم بالحاسب. تكمن أهمية الـ (BCI) الكثير من التطبيقات كالتطبيقات الطبية وخاصة لمساعد الأشخاص المعاقين كمساعدتهم على التعامل مع أجهزة الحاسوب، أو مساعدة الأشخاص المصابين بـ Locked-In Syndrome على التواصل مع العالم الخارجي، وتطبيقات في الدعاية إذ يتم معرفة مدى إعجاب الزبون بالمنتج، وتطبيقات تعليمية وتطبيقات أمنية، أو أيجاد طريق جديدة للعب الألعاب باستخدام الدماغ. الهدف من هذا البحث هو بيان أجدد الحلول للمشاكل التي تواجهها واجهات التخاطب بين الحاسب والدماغ والخوارزميات المستخدمة لتصنيف إشارات الدماغ. وتكمن صعوبة هذا النوع من الأبحاث في صعوبة استخراج الإشارة ومعالجتها لتصبح قابلة للتصنيف.
The Brain Computer Interface (BCI) is considered the latest development of the Human Computer Interface (HCI). Unlike traditional input devices (keyboard, mouse, etc.) BCI reads brain signals from different areas of the human head and translates these signals into commands that can control the computer. The importance of BCI comes from its many applications such as medical applications, especially to assist people with disabilities to help them deal with computers, and help people with Locked-In Syndrome to communicate with the outside world. and advertising applications to see how much the customer appreciates the product, Security applications, or finding a new way to play games using your brain. The aim of this research is to demonstrate the most recent solutions to the problems faced by computer-brain interfaces and the algorithms used to classify brain signals. The difficulty of this research is the in extracting and processing the signal.
References used
D. K. Abhijeet Mallick, "A Review on Signal Pre-processing Techniques in Brain Computer Inrterface," 2015.
S. R. M. A. E. R. A. A. Rabie A. Ramadan, "Basics of Brain Computer Interface," 2015.
Z. S. Z. M. B. Nelly Elsayed, "Brain Computer Interface EEG Signal Preprocessing Issues and Solutions," 2017.
T. M. N. S. T. K. J. D. John Thomas, "Deep Learning-based Classification for Brain-Computer Interfaces," 2017.
A. Sharmila, "Signal Processing Algorithm for Brain Computer Interface - A Review," 2017.
R. K. W. G. E. B. A. B. Hossein Bashashati, "Comparing Different Classifiers in Sensory Motor Brain Computer Interfaces".
N. A. E. A. I. G. A. Ç. Rahib H. Abiyev, "Brain-Computer Interface for Control of Wheelchair Using Fuzzy Neural Networks," 2016.
R. B. R. L. M. H. M. S. Anatole Lécuyer and Fabien Lotte, "Brain-Computer Interfaces, Virtual Reality, and Videogames," 2014.
J. S. K. C. K. C. Hong Gi Yeom, "Common Neural Mechanism for Reaching Movements," 2015.
Y. C. S. F. C. W. Taeho Kang, "Decoding of Human Memory Formation with EEG signals using Convolutional Networks," 2017.
C.-H. I. Chang-Hee Han, "EEG-based Brain-Computer Interface for Real-Time Communication of Patients in Completely Locked-in State," 2018.
J. P. P. O. A. S. C. L. D. R. J. K. L. H. A. P. A. I. S. Gernot R. Müller-Putz, "Towards Non-Invasive Brain-Computer Interface for Hand/Arm Control in Users With Spinal Cord Injury".
rate research
Read More
Recent evidence supports a role for coreference processing in guiding human expectations about upcoming words during reading, based on covariation between reading times and word surprisal estimated by a coreference-aware semantic processing model (Ja
ffe et al. 2020).The present study reproduces and elaborates on this finding by (1) enabling the parser to process subword information that might better approximate human morphological knowledge, and (2) extending evaluation of coreference effects from self-paced reading to human brain imaging data. Results show that an expectation-based processing effect of coreference is still evident even in the presence of the stronger psycholinguistic baseline provided by the subword model, and that the coreference effect is observed in both self-paced reading and fMRI data, providing evidence of the effect's robustness.
A number of patients who had undergone to craniotomies for tumor resection,
re- intubated in ICU as urgent procedure. This may result in poor prognosis, overloading the staff, and
high cost.
The goal is looking for clinical, surgical, and laborato
ry risk factors helping in early detection of cases
which require keep ETT in place and maintaining of ventilation.
A popular approach to decompose the neural bases of language consists in correlating, across individuals, the brain responses to different stimuli (e.g. regular speech versus scrambled words, sentences, or paragraphs). Although successful, this model
-free' approach necessitates the acquisition of a large and costly set of neuroimaging data. Here, we show that a model-based approach can reach equivalent results within subjects exposed to natural stimuli. We capitalize on the recently-discovered similarities between deep language models and the human brain to compute the mapping between i) the brain responses to regular speech and ii) the activations of deep language models elicited by modified stimuli (e.g. scrambled words, sentences, or paragraphs). Our model-based approach successfully replicates the seminal study of Lerner et al. (2011), which revealed the hierarchy of language areas by comparing the functional-magnetic resonance imaging (fMRI) of seven subjects listening to 7min of both regular and scrambled narratives. We further extend and precise these results to the brain signals of 305 individuals listening to 4.1 hours of narrated stories. Overall, this study paves the way for efficient and flexible analyses of the brain bases of language.
The 4th edition of WHO classification of CNS tumors
introduced important changes in the classification of the tumors of central
nervous system, compared with WHO classification 2000. Ten New entities
and variants were described; three of them conc
erned gliomas. Changes in
the grading of 7 of these tumors were introduced; two of them concerned
gliomas.
This study aim reclassify and to compare two series of
gliomas, observed in the Syrian and French populations.
Recent studies emphasize the need of document context in human evaluation of machine translations, but little research has been done on the impact of user interfaces on annotator productivity and the reliability of assessments. In this work, we compa
re human assessment data from the last two WMT evaluation campaigns collected via two different methods for document-level evaluation. Our analysis shows that a document-centric approach to evaluation where the annotator is presented with the entire document context on a screen leads to higher quality segment and document level assessments. It improves the correlation between segment and document scores and increases inter-annotator agreement for document scores but is considerably more time consuming for annotators.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
