Brain computer interface: estimation of cortical activity from non invasive high resolution EEG recordings

2005 ◽  
Author(s):  
F. Babiloni ◽  
F. Cincotti ◽  
M. Mattiocco ◽  
A. Timperi ◽  
S. Salinari ◽  
...  
Keyword(s):  
High Resolution ◽  
Brain Computer Interface ◽  
Cortical Activity ◽  
Computer Interface ◽  
Non Invasive ◽  
Eeg Recordings ◽  
High Resolution Eeg

Estimation of cortical activity from noninvasive high-resolution EEG recordings

2004 ◽  
Vol 1270 ◽  
pp. 245-248 ◽  
Author(s):  
Donatella Mattia ◽  
Marco Mattiocco ◽  
Alessandro Timperi ◽  
Serenella Salinari ◽  
Maria Grazia Marciani ◽  
...  
Keyword(s):  
High Resolution ◽  
Cortical Activity ◽  
Eeg Recordings ◽  
High Resolution Eeg

scholarly journals High-resolution EEG techniques for brain–computer interface applications

2008 ◽  
Vol 167 (1) ◽  
pp. 31-42 ◽  
Author(s):  
Febo Cincotti ◽  
Donatella Mattia ◽  
Fabio Aloise ◽  
Simona Bufalari ◽  
Laura Astolfi ◽  
...  
Keyword(s):  
High Resolution ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
High Resolution Eeg

scholarly journals Continuous sensorimotor rhythm based brain computer interface learning in a large population

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
James R. Stieger ◽  
Stephen A. Engel ◽  
Bin He
Keyword(s):  
Brain Computer Interface ◽  
Large Population ◽  
Computer Interface ◽  
Robotic Arms ◽  
Non Invasive ◽  
Computer Interfaces ◽  
Sensorimotor Rhythm ◽  
Data Record ◽  
Eeg Recordings ◽  
Longitudinal Dataset

AbstractBrain computer interfaces (BCIs) are valuable tools that expand the nature of communication through bypassing traditional neuromuscular pathways. The non-invasive, intuitive, and continuous nature of sensorimotor rhythm (SMR) based BCIs enables individuals to control computers, robotic arms, wheel-chairs, and even drones by decoding motor imagination from electroencephalography (EEG). Large and uniform datasets are needed to design, evaluate, and improve the BCI algorithms. In this work, we release a large and longitudinal dataset collected during a study that examined how individuals learn to control SMR-BCIs. The dataset contains over 600 hours of EEG recordings collected during online and continuous BCI control from 62 healthy adults, (mostly) right hand dominant participants, across (up to) 11 training sessions per participant. The data record consists of 598 recording sessions, and over 250,000 trials of 4 different motor-imagery-based BCI tasks. The current dataset presents one of the largest and most complex SMR-BCI datasets publicly available to date and should be useful for the development of improved algorithms for BCI control.

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

Pilot Study on Using Innovative Counting Peaks Method for Assessment Purposes of the EEG Data Recorded from a Single-Channel Non-Invasive Brain-Computer Interface

2021 ◽  
Author(s):  
Natalia Browarska ◽  
Jaroslaw Zygarlicki ◽  
Mariusz Pelc ◽  
Michal Niemczynowicz ◽  
Malgorzata Zygarlicka ◽  
...  
Keyword(s):  
Pilot Study ◽  
Single Channel ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Non Invasive ◽  
Eeg Data

scholarly journals EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces

2018 ◽  
Vol 8 (11) ◽  
pp. 199 ◽  
Author(s):  
Rodrigo Ramele ◽  
Ana Villar ◽  
Juan Santos
Keyword(s):  
Brain Computer Interface ◽  
Waveform Analysis ◽  
Computer Interface ◽  
Clinical Tool ◽  
Non Invasive ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Public Dataset ◽  
Imaging Sensor ◽  
Clinical Eeg

The Electroencephalography (EEG) is not just a mere clinical tool anymore. It has become the de-facto mobile, portable, non-invasive brain imaging sensor to harness brain information in real time. It is now being used to translate or decode brain signals, to diagnose diseases or to implement Brain Computer Interface (BCI) devices. The automatic decoding is mainly implemented by using quantitative algorithms to detect the cloaked information buried in the signal. However, clinical EEG is based intensively on waveforms and the structure of signal plots. Hence, the purpose of this work is to establish a bridge to fill this gap by reviewing and describing the procedures that have been used to detect patterns in the electroencephalographic waveforms, benchmarking them on a controlled pseudo-real dataset of a P300-Based BCI Speller and verifying their performance on a public dataset of a BCI Competition.

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