F-Value Time-Frequency Analysis: Between-Within Variance Analysis

Yeom, Hong Gi; Jeong, Hyundoo

doi:10.3389/fnins.2021.729449

Cited by 5 publications

(4 citation statements)

References 46 publications

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“…In addition, analyses of variance (ANOVAs) were performed on the time-frequency power to investigate changes in neural activity across levels of proactive or reactive driving condition. We estimated the F-value of ANOVA for both driving conditions (proactive and reactive) using variation in frequency over time 36 . This was accomplished by dividing the variance between groups (i.e., among task levels) by the variance within the group (i.e., within the same task level).…”

Section: Discussionmentioning

confidence: 99%

Blink-related EEG activity measures cognitive load during proactive and reactive driving

Alyan,

Arnau,

Reiser

et al. 2023

Sci Rep

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Assessing drivers’ cognitive load is crucial for driving safety in challenging situations. This research employed the occurrence of drivers’ natural eye blinks as cues in continuously recorded EEG data to assess the cognitive workload while reactive or proactive driving. Twenty-eight participants performed either a lane-keeping task with varying levels of crosswind (reactive) or curve road (proactive). The blink event-related potentials (bERPs) and spectral perturbations (bERSPs) were analyzed to assess cognitive load variations. The study found that task load during reactive driving did not significantly impact bERPs or bERSPs, possibly due to enduring alertness for vehicle control. The proactive driving revealed significant differences in the occipital N1 component with task load, indicating the necessity to adapt the attentional resources allocation based on road demands. Also, increased steering complexity led to decreased frontal N2, parietal P3, occipital P2 amplitudes, and alpha power, requiring more cognitive resources for processing relevant information. Interestingly, the proactive and reactive driving scenarios demonstrated a significant interaction at the parietal P2 and occipital N1 for three difficulty levels. The study reveals that EEG measures related to natural eye blink behavior provide insights into the effect of cognitive load on different driving tasks, with implications for driver safety.

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Section: Discussionmentioning

confidence: 99%

Blink-related EEG activity measures cognitive load during proactive and reactive driving

Alyan,

Arnau,

Reiser

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…They were usually focused on the methods to improve the prediction accuracy [5,8,23,[30][31][32], raise the number of commands [12,33], increase the information transfer rate (ITR) [34][35][36][37][38], or reduce the training efforts [7,30,34,39]. To enhance the prediction accuracy, new classification algorithms [30,40,41] or feature extraction methods have been proposed [31,32,42]. Recent BCI studies frequently applied deep learning algorithms for high accuracy [5,23].…”

Section: Discussionmentioning

confidence: 99%

Studies to Overcome Brain–Computer Interface Challenges

Choi

Yeom

2022

Applied Sciences

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A brain–computer interface (BCI) is a promising technology that can analyze brain signals and control a robot or computer according to a user’s intention. This paper introduces our studies to overcome the challenges of using BCIs in daily life. There are several methods to implement BCIs, such as sensorimotor rhythms (SMR), P300, and steady-state visually evoked potential (SSVEP). These methods have different pros and cons according to the BCI type. However, all these methods are limited in choice. Controlling the robot arm according to the intention enables BCI users can do various things. We introduced the study predicting three-dimensional arm movement using a non-invasive method. Moreover, the study was described compensating the prediction using an external camera for high accuracy. For daily use, BCI users should be able to turn on or off the BCI system because of the prediction error. The users should also be able to change the BCI mode to the efficient BCI type. The BCI mode can be transformed based on the user state. Our study was explained estimating a user state based on a brain’s functional connectivity and a convolutional neural network (CNN). Additionally, BCI users should be able to do various tasks, such as carrying an object, walking, or talking simultaneously. A multi-function BCI study was described to predict multiple intentions simultaneously through a single classification model. Finally, we suggest our view for the future direction of BCI study. Although there are still many limitations when using BCI in daily life, we hope that our studies will be a foundation for developing a practical BCI system.

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“…Therefore, the shared MEG datasets may be used to investigate brain mechanisms using new analytical methods or compare prediction performance for newly proposed algorithms. MATLAB codes to analyse the shared data are also provided for time-frequency analysis, F-value time-frequency analysis 25 , and topography analysis. To the best of our knowledge, this data is the only publicly available MEG data measured during reaching movements.…”

Section: Background and Summarymentioning

confidence: 99%

“…Although time-frequency analysis is a powerful and widely used tool in the analysis of neural activities, it is difficult to identify differences caused by different tasks, such as movement directions, using this method. FTF analysis is a statistical method used to investigate distinguishing characteristics by applying the F-value of ANOVA to time-frequency analysis 25 . FTF analysis visualises the statistical differences among conditions in time-frequency power spectra.…”

Section: Technical Validationmentioning

confidence: 99%

A magnetoencephalography dataset during three-dimensional reaching movements for brain-computer interfaces

Yeom,

Kim,

Chung

2023

Sci Data

Self Cite

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Studying the motor-control mechanisms of the brain is critical in academia and also has practical implications because techniques such as brain-computer interfaces (BCIs) can be developed based on brain mechanisms. Magnetoencephalography (MEG) signals have the highest spatial resolution (~3 mm) and temporal resolution (~1 ms) among the non-invasive methods. Therefore, the MEG is an excellent modality for investigating brain mechanisms. However, publicly available MEG data remains scarce due to expensive MEG equipment, requiring a magnetically shielded room, and high maintenance costs for the helium gas supply. In this study, we share the 306-channel MEG and 3-axis accelerometer signals acquired during three-dimensional reaching movements. Additionally, we provide analysis results and MATLAB codes for time-frequency analysis, F-value time-frequency analysis, and topography analysis. These shared MEG datasets offer valuable resources for investigating brain activities or evaluating the accuracy of prediction algorithms. To the best of our knowledge, this data is the only publicly available MEG data measured during reaching movements.

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F-Value Time-Frequency Analysis: Between-Within Variance Analysis

Cited by 5 publications

References 46 publications

Blink-related EEG activity measures cognitive load during proactive and reactive driving

Blink-related EEG activity measures cognitive load during proactive and reactive driving

Studies to Overcome Brain–Computer Interface Challenges

A magnetoencephalography dataset during three-dimensional reaching movements for brain-computer interfaces

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