Fuzzy logic technique to control a robot based on non-invasive brain computer interface by human group

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

International Conference on Technics Technologies and Education ◽

10.15547/ictte.2019.02.095 ◽

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.

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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

10.1109/mmar49549.2021.9528447 ◽

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

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

Brain Sciences ◽

10.3390/brainsci8110199 ◽

2018 ◽

Vol 8 (11) ◽

pp. 199 ◽

Cited By ~ 6

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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