Prediction of Pilot's Reaction Time Based on EEG Signals

Binias, Bartosz; Myszor, Dariusz; Palus, Henryk; Cyran, Krzysztof A.

doi:10.3389/fninf.2020.00006

Cited by 27 publications

(25 citation statements)

References 44 publications

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“…Evaluating brain activity while testing response time may be a solution. Recent advanced studies have used electroencephalographs to examine time delay in response, indicating brain activity [ 45 , 46 ].…”

Section: Discussionmentioning

confidence: 99%

Stereopsis and Response Times between Collegiate Table Tennis Athletes and Non-Athletes

Chang

Shieh

et al. 2021

IJERPH

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Table tennis athletes and non-athletes potentially differ in stereopsis and eye–hand response times (RT), but whether stereopsis correlates with response time has scarcely been discussed. We aimed to compare stereopsis and RT between collegiate table tennis athletes and non-athletes and to examine the correlation between stereopsis and RT. From December 2016 to October 2019, table tennis athletes (n = 80) and non-athletes (n = 56) were recruited. Stereopsis was measured in four modes (A25, A50, R25, R50: approaching and receding directions at 25 mm/s or 50 mm/s). RT was measured with simple and choice tasks at zero, shoulder, and random distance. For stereopsis, the judged deviations were smaller during the approaching phases. Men had smaller A25 than women (p =0.012), whereas table tennis athletes showed smaller R25 and A50 than non-athletes (p = 0.03, 0.01, respectively). RT increased from simple to choice conditions and from zero to random, followed by shoulder distance. Men were significantly faster than women in choice tasks (p < 0.01). Table tennis athletes performed better in RT than the non-athletes (p < 0.05). No correlation was observed between stereopsis and RT (r = −0.01 to 0.12). In conclusion, athletes from table tennis sports showed better stereopsis and RT than non-athletes. Men had better stereopsis and RT than women. There was no correlation between stereopsis and RT.

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Section: Discussionmentioning

confidence: 99%

Stereopsis and Response Times between Collegiate Table Tennis Athletes and Non-Athletes

Chang

Shieh

et al. 2021

IJERPH

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show abstract

“…Also, we see a lot of possible applications for it. The EEG characteristics prediction is helpful in versatile neurostimulation applications, such as the brain-machine interface based techniques [1, 13, 9] or other EEG-cooperating methods like electrical brain stimulation [36]. Methods of predicting changes in EEG, especially when applied online, can be of a great clinical utility.…”

Section: Discussionmentioning

confidence: 99%

Time series analysis of trial-to-trial variability of MEG power spectrum during rest state, unattented listening and frequency-modulated tones classification

Kipiński

Kordecki

2021

Preprint

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The nonstationarity of EEG/MEG signals is important for understanding the functioning of human brain. From the previous research we know that even very short, i.e. 250-500ms MEG signals are variance-nonstationary. The covariance of stochastic process is mathematically associated with its spectral density, therefore we investigate how the spectrum of such nonstationary signals varies in time. We analyze the data from 148-channel MEG, that represent rest state, unattented listening and frequency-modulated tones classification. We transform short-time MEG signals to the frequency domain using the FFT algorithm and for the dominant frequencies 8-12Hz we prepare the time series representing their trial-to-trial variability. Then, we test them for level- and trend-stationarity, unit root, heteroscedasticity and gaussianity and based on their properties we propose the ARMA-modelling for their description. The analyzed time series have the weakly stationary properties independently of the functional state of brain and localization. Only their small percentage, mostly related to the cognitive task, still presents nonstationarity. The obtained mathematical models show that the spectral density of analyzed signals depends on only 2-3 previous trials. The presented method has limitations related to FFT resolution and univariate models, but it is not computationally complicated and allows to obtain a low-complex stochastic models of the EEG/MEG pectrum variability. Although the physiological short-time MEG signals are in principle nonstationary in time domain, its power spectrum at the dominant frequencies varies as weakly stationary stochastic process. Described technique has the possible applications in prediction of the EEG/MEG spectral properties in theoretical and clinical neuroscience.

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“…For example, the University of Valladolid, Spain [21] studied the control of a tractor tracking specific trajectories based on EEG signals. Silesian Polytechnic University in Poland [18], [19] studied on the basis of EEG aircraft assisted driving to improve the pilot's accident response. Besides, the University of Tokyo in Japan [20] using sEMG to control vehicles to achieve different steering ranges.…”

Section: Discussion and Conclusion A Discussionmentioning

confidence: 99%

“…in recent years. Nowadays, BCI technology has been applied in the assistant driving of airplanes, automobiles, and other vehicles successfully to achieve steering, U-turn control, tracking control [18]- [21], etc.…”

Section: Introductionmentioning

confidence: 99%

Tractor Assistant Driving Control Method Based on EEG Combined With RNN-TL Deep Learning Algorithm

et al. 2020

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Nowadays, fieldwork is often accompanied by tight schedules, which tends to strain the shoulder muscles due to high-intensity work. Moreover, it is difficult and stressful for the disabled to drive agricultural machinery. Besides, current artificial intelligence technology could not fully realize tractor autonomous driving because of a high uncertain filed environment and short interruptions of satellite navigation signal shaded by trees. To reduce manual operations, a tractor assistant driving control method was proposed based on the human-machine interface utilizing the electroencephalographic (EEG) signal. First, the EEG signals of the tractor drivers were collected by a low-cost brain-computer interface (BCI), followed by denoising using a wavelet packet. Then the spectral features of EEG signals were calculated and extracted as the input of Recurrent Neural Network (RNN). Finally, the EEG-aided RNN driving model was trained for tractor driving robot control such as straight going, brake, left turn, and right turn operations, which control accuracy was 94.5% and time cost was 0.61 ms. Also, 8 electrodes were selected by the PCA algorithm for the design of a portable EEG controller. And the control accuracy reached 93.1% with the time cost of 0.48 ms. To solve the incomplete driving data set in the actual world because some driving manners may cause dangerous or even death, RNN-TL algorithm was employed by creating the complete driving data in the virtual environment followed by transferring the driving control experience to the actual world with small actual driving data set in the field, which control accuracy was 93.5% and time consumption was 0.48 ms. The experimental results showed the feasibility of the proposed tractor driving control method based on EEG signal combined with RNN-TL deep learning algorithm which can work with the displacement error less than 6.7 mm when the tractor speed is less than 50 km/h.

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Prediction of Pilot's Reaction Time Based on EEG Signals

Cited by 27 publications

References 44 publications

Stereopsis and Response Times between Collegiate Table Tennis Athletes and Non-Athletes

Stereopsis and Response Times between Collegiate Table Tennis Athletes and Non-Athletes

Time series analysis of trial-to-trial variability of MEG power spectrum during rest state, unattented listening and frequency-modulated tones classification

Tractor Assistant Driving Control Method Based on EEG Combined With RNN-TL Deep Learning Algorithm

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