Signal Detection, Processing and Challenges of Non-invasive Brain-Computer Interface Technology

2019 ◽  
pp. 60-67
Author(s):  
Xiaoyuan Li ◽  
Feng Chen ◽  
Yaohui Jia ◽  
Xinyu Liu
Keyword(s):  
Signal Detection ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Non Invasive ◽  
Detection Processing

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.

Portable non-invasive brain-computer interface: challenges and opportunities of optical modalities

2017 ◽  
Author(s):  
Clara A. Scholl ◽  
Scott M. Hendrickson ◽  
Bruce A. Swett ◽  
Michael J. Fitch ◽  
Erich C. Walter ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Non Invasive ◽  
Challenges And Opportunities

scholarly journals A Simple Convolutional Neural Network for Accurate P300 Detection and Character Spelling in Brain Computer Interface

2018 ◽  
Author(s):  
Hongchang Shan ◽  
Yu Liu ◽  
Todor Stefanov
Keyword(s):  
Signal Detection ◽  
Spatial Information ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Detection Accuracy ◽  
Signal Recognition ◽  
Human Beings ◽  
Feature Representations ◽  
Machine Learning Methods ◽  
Benchmark Datasets

A Brain Computer Interface (BCI) character speller allows human-beings to directly spell characters using eye-gazes, thereby building communication between the human brain and a computer. Convolutional Neural Networks (CNNs) have shown better performance than traditional machine learning methods for BCI signal recognition and its application to the character speller. However, current CNN architectures limit further accuracy improvements of signal detection and character spelling and also need high complexity to achieve competitive accuracy, thereby preventing the use of CNNs in portable BCIs. To address these issues, we propose a novel and simple CNN which effectively learns feature representations from both raw temporal information and raw spatial information. The complexity of the proposed CNN is significantly reduced compared with state-of-the-art CNNs for BCI signal detection. We perform experiments on three benchmark datasets and compare our results with those in previous research works which report the best results. The comparison shows that our proposed CNN can increase the signal detection accuracy by up to 15.61% and the character spelling accuracy by up to 19.35%.

scholarly journals EEG BASED SMART HOME AUTOMATION USING NON-INVASIVE BRAIN-COMPUTER INTERFACE APPROACH.

2016 ◽  
Vol 4 (6) ◽  
pp. 1501-1505
Author(s):  
SnehaPushpa S ◽  
◽  
Chandrashekar. NS ◽  
Keyword(s):  
Smart Home ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Home Automation ◽  
Non Invasive
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