Development of a robust method for an online P300 Speller Brain Computer Interface

Tahmasebzadeh, Alireza; Bahrani, Masih; Setarehdan, Seyed Kamaledin

doi:10.1109/ner.2013.6696122

Cited by 12 publications

(7 citation statements)

References 11 publications

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“…Due to setup difficulties, however, the authors of that study were unable to provide evidence to support the use of Emotiv in paradigms that rely on time-locked events. However, some reports of Emotiv EEG systems use in such tasks are available (Tahmasebzadeh et al, 2013).…”

Section: Hardware Descriptionmentioning

confidence: 99%

Prediction of Pilot's Reaction Time Based on EEG Signals

Binias

Myszor

Palus

et al. 2020

Front. Neuroinform.

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The main hypothesis of this work is that the time of delay in reaction to an unexpected event can be predicted on the basis of the brain activity recorded prior to that event. Such mental activity can be represented by electroencephalographic data. To test this hypothesis, we conducted a novel experiment involving 19 participants that took part in a 2-h long session of simulated aircraft flights. An EEG signal processing pipeline is proposed that consists of signal preprocessing, extracting bandpass features, and using regression to predict the reaction times. The prediction algorithms that are used in this study are the Least Absolute Shrinkage Operator and its Least Angle Regression modification, as well as Kernel Ridge and Radial Basis Support Vector Machine regression. The average Mean Absolute Error obtained across the 19 subjects was 114 ms. The present study demonstrates, for the first time, that it is possible to predict reaction times on the basis of EEG data. The presented solution can serve as a foundation for a system that can, in the future, increase the safety of air traffic.

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Section: Hardware Descriptionmentioning

confidence: 99%

Prediction of Pilot's Reaction Time Based on EEG Signals

Binias

Myszor

Palus

et al. 2020

Front. Neuroinform.

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“…One of the most basic applications of BCIs for people with disabilities used for communication purposes, where users using their brain activity choose appropriate letter [192][193][194][195].…”

Section: Description Source Data Analysis Methods Spelling Applicationsmentioning

confidence: 99%

Summary of over Fifty Years with Brain-Computer Interfaces—A Review

et al. 2021

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Over the last few decades, the Brain-Computer Interfaces have been gradually making their way to the epicenter of scientific interest. Many scientists from all around the world have contributed to the state of the art in this scientific domain by developing numerous tools and methods for brain signal acquisition and processing. Such a spectacular progress would not be achievable without accompanying technological development to equip the researchers with the proper devices providing what is absolutely necessary for any kind of discovery as the core of every analysis: the data reflecting the brain activity. The common effort has resulted in pushing the whole domain to the point where the communication between a human being and the external world through BCI interfaces is no longer science fiction but nowadays reality. In this work we present the most relevant aspects of the BCIs and all the milestones that have been made over nearly 50-year history of this research domain. We mention people who were pioneers in this area as well as we highlight all the technological and methodological advances that have transformed something available and understandable by a very few into something that has a potential to be a breathtaking change for so many. Aiming to fully understand how the human brain works is a very ambitious goal and it will surely take time to succeed. However, even that fraction of what has already been determined is sufficient e.g., to allow impaired people to regain control on their lives and significantly improve its quality. The more is discovered in this domain, the more benefit for all of us this can potentially bring.

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“…Due to setup problems authors of that work were not able to provide evidence to support the use of Emotiv in paradigms that rely on time-locked events. However, some reports of use of Emotiv EEG systems in such tasks are available [ 21 ].…”

Section: Methodsmentioning

confidence: 99%

A Machine Learning Approach to the Detection of Pilot’s Reaction to Unexpected Events Based on EEG Signals

Binias

Myszor

Cyran

2018

Computational Intelligence and Neuroscience

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This work considers the problem of utilizing electroencephalographic signals for use in systems designed for monitoring and enhancing the performance of aircraft pilots. Systems with such capabilities are generally referred to as cognitive cockpits. This article provides a description of the potential that is carried by such systems, especially in terms of increasing flight safety. Additionally, a neuropsychological background of the problem is presented. Conducted research was focused mainly on the problem of discrimination between states of brain activity related to idle but focused anticipation of visual cue and reaction to it. Especially, a problem of selecting a proper classification algorithm for such problems is being examined. For that purpose an experiment involving 10 subjects was planned and conducted. Experimental electroencephalographic data was acquired using an Emotiv EPOC+ headset. Proposed methodology involved use of a popular method in biomedical signal processing, the Common Spatial Pattern, extraction of bandpower features, and an extensive test of different classification algorithms, such as Linear Discriminant Analysis, k-nearest neighbors, and Support Vector Machines with linear and radial basis function kernels, Random Forests, and Artificial Neural Networks.

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Development of a robust method for an online P300 Speller Brain Computer Interface

Cited by 12 publications

References 11 publications

Prediction of Pilot's Reaction Time Based on EEG Signals

Prediction of Pilot's Reaction Time Based on EEG Signals

Summary of over Fifty Years with Brain-Computer Interfaces—A Review

A Machine Learning Approach to the Detection of Pilot’s Reaction to Unexpected Events Based on EEG Signals

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