Brain Computer Interfaces

2015-11-12
Brain Computer Interface (BCI) systems provide control of external devices by using only brain activity. In recent years, there has been a great interest in developing BCI systems for different applications. These systems are capable of solving daily life problems for both healthy and disabled people. One of the most important applications of BCI is to provide communication for disabled people that are totally paralysed. In this paper, different parts of a BCI system and different methods used in each part are reviewed. Neuroimaging devices, with an emphasis on EEG (electroencephalography), are presented and brain activities as well as signal processing methods used in EEG-based BCIs are explained in detail. Current methods and paradigms in BCI based speech communication are considered.
2015 International Symposium on Computer and Information Sciences, 11 - 12 Kasım 2015

Suggestions

Brain Computer Interfaces for Silent Speech
TABAR, Yousef Rezaei; Halıcı, Uğur (2017-05-01)
Brain Computer Interface (BCI) systems provide control of external devices by using only brain activity. In recent years, there has been a great interest in developing BCI systems for different applications. These systems are capable of solving daily life problems for both healthy and disabled people. One of the most important applications of BCI is to provide communication for disabled people that are totally paralysed. In this paper, different parts of a BCI system and different methods used in each part ...
Analysis and classification of spelling paradigm EEG data and an attempt for optimization of channels used
Yıldırım, Asil; Halıcı, Uğur; Department of Electrical and Electronics Engineering (2010)
Brain Computer Interfaces (BCIs) are systems developed in order to control devices by using only brain signals. In BCI systems, different mental activities to be performed by the users are associated with different actions on the device to be controlled. Spelling Paradigm is a BCI application which aims to construct the words by finding letters using P300 signals recorded via channel electrodes attached to the diverse points of the scalp. Reducing the letter detection error rates and increasing the speed of...
Prototype Hardware Design for Brain Computer Interface Applications
Erdogan, Balkar; Akinci, Berna; Acar, Erman; Usakli, Ali Buelent; Gençer, Nevzat Güneri (2009-01-01)
Brain Computer Interface (BCI) is an alternative communication pathway between the human brain and outside world in which only the brain activity is interpreted in a special way. These systems are based on the electrical activity of the brain that can be measured via Electroencephalography (EEG) devices. BCI enables people with severe motor disorders (like ALS) to communicate with their environment or control a wheelchair for their movement by using the EEG signals. In this study, a prototype data acquisito...
A computational model of the brain for decoding mental states from FMRI images
Alkan, Sarper; Yarman Vural, Fatoş Tunay; Department of Cognitive Sciences (2019)
Brain decoding from brain images obtained using functional magnetic resonance imaging (fMRI) techniques is an important task for the identification of mental states and illnesses as well as for the development of brain machine interfaces. The brain decoding methods that use multi-voxel pattern analysis that rely on the selection of voxels (volumetric pixels) that have relevant activity with respect to the experimental tasks or stimuli of the fMRI experiments are the most commonly used methods. While MVPA ba...
Brain oscillatory analysis of visual working memory errors
Mapelli, Igor; Özkurt, Tolga Esat; Department of Health Informatics (2019)
Brain dynamics of memory formation were explored throughout a working memory (WM) task. Electroencephalography data were acquired from participants while presented with grayscale photos of object categories (each category defined by images sharing a common gist). Following a short delay, two probes were shown to test memory accuracy. Time-frequency representations of successful and erroneous memories were contrasted. Additionally, brain connectivity was studied via coherency and phase-amplitude coupling (PA...
Citation Formats
U. Halıcı, “Brain Computer Interfaces,” presented at the 2015 International Symposium on Computer and Information Sciences, 11 - 12 Kasım 2015, Londrina, Brezilya, 2015, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/81150.