scholarly journals The Effect That Auditory Distractions Have on a Visual P300 Speller While Utilizing Low-Cost Off-the-Shelf Equipment

Computers ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 68
Author(s):  
Patrick Schembri ◽  
Maruisz Pelc ◽  
Jixin Ma
Keyword(s):  
Brain Computer Interface ◽  
Low Cost ◽  
User Preference ◽  
Computer Interface ◽  
Oddball Paradigm ◽  
Active Listening ◽  
P300 Speller ◽  
Non Invasive ◽  
Auditory Distractions ◽  
Visual P300

This paper investigates the effect that selected auditory distractions have on the signal of a visual P300 Speller in terms of accuracy, amplitude, latency, user preference, signal morphology, and overall signal quality. In addition, it ensues the development of a hierarchical taxonomy aimed at categorizing distractions in the P300b domain and the effect thereof. This work is part of a larger electroencephalography based project and is based on the P300 speller brain–computer interface (oddball) paradigm and the xDAWN algorithm, with eight to ten healthy subjects, using a non-invasive brain–computer interface based on low-fidelity electroencephalographic (EEG) equipment. Our results suggest that the accuracy was best for the lab condition (LC) at 100%, followed by music at 90% (M90) at 98%, trailed by music at 30% (M30) and music at 60% (M60) equally at 96%, and shadowed by ambient noise (AN) at 92.5%, passive talking (PT) at 90%, and finally by active listening (AL) at 87.5%. The subjects’ preference prodigiously shows that the preferred condition was LC as originally expected, followed by M90, M60, AN, M30, AL, and PT. Statistical analysis between all independent variables shows that we accept our null hypothesis for both the amplitude and latency. This work includes data and comparisons from our previous papers. These additional results should give some insight into the practicability of the aforementioned P300 speller methodology and equipment to be used for real-world applications.

scholarly journals Impact of Speller Size on a Visual P300 Brain-Computer Interface (BCI) System under Two Conditions of Constraint for Eye Movement

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
R. Ron-Angevin ◽  
L. Garcia ◽  
Á. Fernández-Rodríguez ◽  
J. Saracco ◽  
J. M. André ◽  
...  
Keyword(s):  
Eye Movement ◽  
High Efficiency ◽  
Brain Computer Interface ◽  
Covert Attention ◽  
Computer Interface ◽  
P300 Speller ◽  
High Satisfaction ◽  
High Workload ◽  
The One ◽  
Visual P300

The vast majority of P300-based brain-computer interface (BCI) systems are based on the well-known P300 speller presented by Farwell and Donchin for communication purposes and an alternative to people with neuromuscular disabilities, such as impaired eye movement. The purpose of the present work is to study the effect of speller size on P300-based BCI usability, measured in terms of effectiveness, efficiency, and satisfaction under overt and covert attention conditions. To this end, twelve participants used three speller sizes under both attentional conditions to spell 12 symbols. The results indicated that the speller size had, in both attentional conditions, a significant influence on performance. In both conditions (covert and overt), the best performances were obtained with the small and medium speller sizes, both being the most effective. The speller size did not significantly affect workload on the three speller sizes. In contrast, covert attention condition produced very high workload due to the increased resources expended to complete the task. Regarding users’ preferences, significant differences were obtained between speller sizes. The small speller size was considered as the most complex, the most stressful, the less comfortable, and the most tiring. The medium speller size was always considered in the medium rank, which is the speller size that was evaluated less frequently and, for each dimension, the worst one. In this sense, the medium and the large speller sizes were considered as the most satisfactory. Finally, the medium speller size was the one to which the three standard dimensions were collected: high effectiveness, high efficiency, and high satisfaction. This work demonstrates that the speller size is an important parameter to consider in improving the usability of P300 BCI for communication purposes. The obtained results showed that using the proposed medium speller size, performance and satisfaction could be improved.

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

Extending brain-computer interface access with a multilingual language model in the P300 speller

2021 ◽  
pp. 1-13
Author(s):  
P Loizidou ◽  
E Rios ◽  
A Marttini ◽  
O Keluo-Udeke ◽  
J Soetedjo ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Language Model ◽  
Computer Interface ◽  
P300 Speller

A Robust Low-Cost EEG Motor Imagery-Based Brain-Computer Interface

2018 ◽  
Author(s):  
Shivanthan A.C. Yohanandan ◽  
Isabell Kiral-Kornek ◽  
Jianbin Tang ◽  
Benjamin S. Mshford ◽  
Umar Asif ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Computer Interface

Brain.me: A Low-Cost Brain Computer Interface for AAL Applications

2014 ◽  
pp. 223-231
Author(s):  
Niccolò Mora ◽  
V. Bianchi ◽  
I. De Munari ◽  
P. Ciampolini
Keyword(s):  
Brain Computer Interface ◽  
Low Cost ◽  
Computer Interface

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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