User’s Self-Prediction of Performance in Motor Imagery Brain–Computer Interface

Ahn, Minkyu; H, Cho; Ahn, Sangtae; Jun, Sung Chan

doi:10.3389/fnhum.2018.00059

Cited by 29 publications

(28 citation statements)

References 59 publications

(77 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The resting state EEG data (eyes-open and eyes-closed) were band-pass filtered from 1-50 Hz using FFT, and a time window of 30 s (10-40 s after onset) among the data that were collected for 1 min were used, because strong eyeball movement noise was observed frequently early or late in the trials. The spectral band powers of theta (4-8 Hz), alpha (8-13 Hz), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and low gamma (30-50 Hz) were obtained for each of the resting states, and normalized by an entire spectrum's power (4-50 Hz) to reduce inter-individual power amplitude variability. This is referred to as the relative power level (RPL).…”

Section: Resting State Analysismentioning

confidence: 99%

“…Generally, because a BCI task requires significant time and effort, the ability to predict BCI's user-specific suitability before a BCI task is conducted is highly important with respect to cost-effectiveness. Therefore, many researchers have investigated several factors related to MI-BCI performance, such as brain structure [13,14], personal characteristics [15], motivation [16], mindfulness [17], kinesthetic MI scores [18], and users' conviction that they can perform BCI [19]. However, these factors are believed to be associated less directly with MI-BCI performance.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Use of Both Eyes-Open and Eyes-Closed Resting States May Yield a More Robust Predictor of Motor Imagery BCI Performance

Kwon

Won

et al. 2020

Electronics

Self Cite

View full text Add to dashboard Cite

Motor-imagery brain-computer interface (MI-BCI) is a technique that manipulates external machines using brain activities, and is highly useful to amyotrophic lateral sclerosis patients who cannot move their limbs. However, it is reported that approximately 15–30% of users cannot modulate their brain signals, which results in the inability to operate motor imagery BCI systems. Thus, advance prediction of BCI performance has drawn researchers’ attention, and some predictors have been proposed using the alpha band’s power, as well as other spectral bands’ powers, or spectral entropy from resting state electroencephalography (EEG). However, these predictors rely on a single state alone, such as the eyes-closed or eyes-open state; thus, they may often be less stable or unable to explain inter-/intra-subject variability. In this work, a modified predictor of MI-BCI performance that considered both brain states (eyes-open and eyes-closed resting states) was investigated with 41 online MI-BCI session datasets acquired from 15 subjects. The results showed that our proposed predictor and online MI-BCI classification accuracy were positively and highly significantly correlated (r = 0.71, p < 0.1 × 10 − 7 ), which indicates that the use of multiple brain states may yield a more robust predictor than the use of a single state alone.

show abstract

Section: Resting State Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Use of Both Eyes-Open and Eyes-Closed Resting States May Yield a More Robust Predictor of Motor Imagery BCI Performance

Kwon

Won

et al. 2020

Electronics

Self Cite

View full text Add to dashboard Cite

show abstract

“…A brain-computer interface (BCI) provides a direct line of communication between a human brain and a computer by converting physiological signals into commands for the control of external devices [1][2][3][4][5]. BCIs are designed mainly to provide an alternative means of communication for people with severe motor disabilities [6][7][8][9]. Among a number of techniques, electroencephalography (EEG) is the most popular brain-imaging method for BCI implementation due to its noninvasive nature, low cost, portability and high temporal resolution [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…BCIs are designed mainly to provide an alternative means of communication for people with severe motor disabilities [6][7][8][9]. Among a number of techniques, electroencephalography (EEG) is the most popular brain-imaging method for BCI implementation due to its noninvasive nature, low cost, portability and high temporal resolution [9][10][11][12][13]. In the literature, several BCI systems have been developed by using EEG signals, including [14] event-related desynchronization/synchronization 64 target BCI system was developed by using eight SSVEP frequencies and the P300 paradigm [22].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Speller Design Using Eye Tracking and SSVEP Brain–Computer Interface

Mannan

Kamran

Kang

et al. 2020

Sensors

View full text Add to dashboard Cite

Steady-state visual evoked potentials (SSVEPs) have been extensively utilized to develop brain–computer interfaces (BCIs) due to the advantages of robustness, large number of commands, high classification accuracies, and information transfer rates (ITRs). However, the use of several simultaneous flickering stimuli often causes high levels of user discomfort, tiredness, annoyingness, and fatigue. Here we propose to design a stimuli-responsive hybrid speller by using electroencephalography (EEG) and video-based eye-tracking to increase user comfortability levels when presented with large numbers of simultaneously flickering stimuli. Interestingly, a canonical correlation analysis (CCA)-based framework was useful to identify target frequency with a 1 s duration of flickering signal. Our proposed BCI-speller uses only six frequencies to classify forty-eight targets, thus achieve greatly increased ITR, whereas basic SSVEP BCI-spellers use an equal number of frequencies to the number of targets. Using this speller, we obtained an average classification accuracy of 90.35 ± 3.597% with an average ITR of 184.06 ± 12.761 bits per minute in a cued-spelling task and an ITR of 190.73 ± 17.849 bits per minute in a free-spelling task. Consequently, our proposed speller is superior to the other spellers in terms of targets classified, classification accuracy, and ITR, while producing less fatigue, annoyingness, tiredness and discomfort. Together, our proposed hybrid eye tracking and SSVEP BCI-based system will ultimately enable a truly high-speed communication channel.

show abstract

“…It is widely accepted within the BCI research community that in any BCI system, neurological phenomena are the only source of control [21,22]. Artifacts, unwanted electrical signals that arise from sources other than the brain, can interfere with neurological phenomena.…”

Section: Introductionmentioning

confidence: 99%

Effect of EOG Signal Filtering on the Removal of Ocular Artifacts and EEG-Based Brain-Computer Interface: A Comprehensive Study

et al. 2018

View full text Add to dashboard Cite

It is a fact that contamination of EEG by ocular artifacts reduces the classification accuracy of a brain-computer interface (BCI) and diagnosis of brain diseases in clinical research. Therefore, for BCI and clinical applications, it is very important to remove/reduce these artifacts before EEG signal analysis. Although, EOG-based methods are simple and fast for removing artifacts but their performance, meanwhile, is highly affected by the bidirectional contamination process. Some studies emphasized that the solution to this problem is low-pass filtering EOG signals before using them in artifact removal algorithm but there is still no evidence on the optimal low-pass frequency limits of EOG signals. In this study, we investigated the optimal EOG signal filtering limits using state-of-the-art artifact removal techniques with fifteen artificially contaminated EEG and EOG datasets. In this comprehensive analysis, unfiltered and twelve different low-pass filtering of EOG signals were used with five different algorithms, namely, simple regression, least mean squares, recursive least squares, REGICA, and AIR. Results from statistical testing of time and frequency domain metrics suggested that a low-pass frequency between 6 and 8 Hz could be used as the most optimal filtering frequency of EOG signals, both to maximally overcome/minimize the effect of bidirectional contamination and to achieve good results from artifact removal algorithms. Furthermore, we also used BCI competition IV datasets to show the efficacy of the proposed framework on real EEG signals. The motor-imagery-based BCI achieved statistically significant high-classification accuracies when artifacts from EEG were removed by using 7 Hz low-pass filtering as compared to all other filterings of EOG signals. These results also validated our hypothesis that low-pass filtering should be applied to EOG signals for enhancing the performance of each algorithm before using them for artifact removal process. Moreover, the comparison results indicated that the hybrid algorithms outperformed the performance of single algorithms for both simulated and experimental EEG datasets.

show abstract

User’s Self-Prediction of Performance in Motor Imagery Brain–Computer Interface

Cited by 29 publications

References 59 publications

Use of Both Eyes-Open and Eyes-Closed Resting States May Yield a More Robust Predictor of Motor Imagery BCI Performance

Use of Both Eyes-Open and Eyes-Closed Resting States May Yield a More Robust Predictor of Motor Imagery BCI Performance

A Hybrid Speller Design Using Eye Tracking and SSVEP Brain–Computer Interface

Effect of EOG Signal Filtering on the Removal of Ocular Artifacts and EEG-Based Brain-Computer Interface: A Comprehensive Study

Contact Info

Product

Resources

About