Normalization of feature distribution in motor imagery based brain-computer interfaces

Binias, Bartosz; Grzejszczak, Tomasz; Niezabitowski, Michał

doi:10.1109/med.2016.7535985

Cited by 5 publications

(6 citation statements)

References 17 publications

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“…Since the mean value of the bandpass filtered signal tends to zero, the variance of such signal can be used to represent its bandpower. To improve the performance of chosen classification algorithm, the distribution of the extracted bandpower features is often normalized using a natural logarithm function (Binias et al, 2016a). The logarithm of variance feature, that will also be referred to as logvar, was chosen as the descriptive statistics in the described pipeline.…”

Section: Feature Extractionmentioning

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: Feature Extractionmentioning

confidence: 99%

Prediction of Pilot's Reaction Time Based on EEG Signals

Binias

Myszor

Palus

et al. 2020

Front. Neuroinform.

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“…Since the mean value of bandpass filtered EEG signal is close to 0 its power is equivalent to its variance. To normalize the distribution of calculated features a logarithm operation is commonly applied [ 39 ]. A logarithm of the variance of the signal’s amplitude calculated during a specific time interval is a very popular feature used for the description of EEG signal’s power in specific frequency band [ 39 ].…”

Section: Methodsmentioning

confidence: 99%

“…To normalize the distribution of calculated features a logarithm operation is commonly applied [ 39 ]. A logarithm of the variance of the signal’s amplitude calculated during a specific time interval is a very popular feature used for the description of EEG signal’s power in specific frequency band [ 39 ]. The frequency ranges used to represent specific brain waves for the purpose of the analysis are presented below: Theta waves: 3–7 Hz Alpha waves: 8–12 Hz Beta waves: 13–29 Hz Gamma waves: 30–69 Hz …”

Section: Methodsmentioning

confidence: 99%

Analysis of Relation between Brainwave Activity and Reaction Time of Short-Haul Pilots Based on EEG Data

Binias,

Myszor,

Binias

et al. 2023

Sensors

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The purpose of this research is to examine and assess the relation between a pilot’s concentration and reaction time with specific brain activity during short-haul flights. Participants took part in one-hour long flight sessions performed on the FNPT II class flight simulator. Subjects were instructed to respond to unexpected events that occurred during the flight. The brainwaves of each participant were recorded with the Emotiv EPOC+ Scientific Contextual EEG device. The majority of participants showed a statistically significant, positive correlation between Theta Power in the frontal lobe and response time. Additionally, most subjects exhibited statistically significant, positive correlations between band-power and reaction times in the Theta range for the temporal and parietal lobes. Statistically significant event-related changes (ERC) were observed for the majority of subjects in the frontal lobe for Theta frequencies, Beta waves in the frontal lobe and in all lobes for the Gamma band. Notably, significant ERC was also observed for Theta and Beta frequencies in the temporal and occipital Lobes, Alpha waves in the frontal, parietal and occipital lobes for most participants. A difference in brain activity patterns was observed, depending on the performance in time-restricted tasks.

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“…A logarithm of the variance of signal's amplitude is a very common feature used for the description of EEG signal's power [ 10 , 25 ]. As mean value of bandpass filtered EEG signal is close to 0, its power is in fact equivalent to its variance.…”

Section: Methodsmentioning

confidence: 99%

“…As mean value of bandpass filtered EEG signal is close to 0, its power is in fact equivalent to its variance. The normalization of the feature distribution is obtained by an application of logarithm operation [ 25 ].…”

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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Normalization of feature distribution in motor imagery based brain-computer interfaces

Cited by 5 publications

References 17 publications

Prediction of Pilot's Reaction Time Based on EEG Signals

Prediction of Pilot's Reaction Time Based on EEG Signals

Analysis of Relation between Brainwave Activity and Reaction Time of Short-Haul Pilots Based on EEG Data

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

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