Mobile BCI dataset of scalp- and ear-EEGs with ERP and SSVEP paradigms while standing, walking, and running

Lee, Young‐Eun; Shin, Gi-Hwan; Lee, Minji; Lee, Seong–Whan

doi:10.1038/s41597-021-01094-4

Cited by 17 publications

(10 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the future, we intend to examine SSVEP scenarios that will result in BCI systems that are more comfortable and have a better user experience. Prefrontal EEG channels, which have been used successfully in driver drowsiness detection [ 44 ], plays significant role in such systems since they are placed in hairless brain regions, and they provides us with signals less subjected to noise [ 45 ]. Furthermore, there are indications that occipital and frontal areas play important roles in the generation of SSVEP signals [ 46 ].…”

Section: Discussion and Conclusionmentioning

confidence: 99%

An Adaptive Task-Related Component Analysis Method for SSVEP Recognition

Oikonomou

2022

Sensors

View full text Add to dashboard Cite

Steady-State Visual Evoked Potential (SSVEP) recognition methods use a subject’s calibration data to differentiate between brain responses, hence, providing the SSVEP-based brain–computer interfaces (BCIs) with high performance. However, they require sufficient calibration EEG trials to achieve that. This study develops a new method to learn from limited calibration EEG trials, and it proposes and evaluates a novel adaptive data-driven spatial filtering approach for enhancing SSVEP detection. The spatial filter learned from each stimulus utilizes temporal information from the corresponding EEG trials. To introduce the temporal information into the overall procedure, a multitask learning approach, based on the Bayesian framework, is adopted. The performance of the proposed method was evaluated into two publicly available benchmark datasets, and the results demonstrated that our method outperformed competing methods by a significant margin.

show abstract

Section: Discussion and Conclusionmentioning

confidence: 99%

An Adaptive Task-Related Component Analysis Method for SSVEP Recognition

Oikonomou

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The performance of the proposed denoising framework for motion artifacts was assessed through tests on two separate publicly accessible standard EEG datasets, both of which are free from seizure activity. dataset-1 contains semi-simulated EEG data with provided reference EEG signals [15,18], while dataset-2 comprises real-time EEG data captured from a Mobile Brain-Computer Interface (BCI) [19]. To evaluate the efficacy of denoising in ambulatory EEG seizure recordings, we obtained seizure activity data from Andrzejak et al [20] at Bonn University, Germany, and the CHB-MIT database [21].…”

Section: Data Collectionmentioning

confidence: 99%

“…The dataset-2 [19] comprises mobile EEG data collected from 18 subjects engaged in BCI tasks while moving at various speeds. The data were gathered using scalp and ear EEG sensors, as well as locomotion sensors, under 16 different experimental conditions.…”

Section: Data Collectionmentioning

confidence: 99%

Efficient EEG motion artifact elimination framework for ambulatory epileptic seizure detection application

2024

Biomed. Phys. Eng. Express

View full text Add to dashboard Cite

Motion artifacts are a pervasive challenge in EEG ambulatory monitoring, often obscuring critical neurological signals and impeding accurate seizure detection. In this study, we propose a new approach of outlier based grouping of two level Singular Spectrum Analysis (SSA) decomposition combined with Relative Total Variation (RTV) filter for the effective removal of motion-induced noise from ambulatory EEG data. A two-stage SSA method was employed to decompose single-channel EEG signal, which had been interfered with, into various frequency bands. The affected sub-band signal was then subjected to an RTV filter to estimate the artifact signal. Subtracting this estimated artifact signal from the contaminated sub-band signal yielded the filtered sub-band signal. Subsequently, the filtered sub-band signal was reintegrated with the other decomposed components from noise-free bands, culminating in the generation of the ultimate denoised EEG signal. Based on the comprehensive set of simulation results, it can be deduced that the algorithm described in the paper outperforms existing methods. It demonstrates superior metrics evaluation in terms of ∆SNR,η , MAE, and PSNR when compared to these alternatives. Our framework significantly enhances the quality of EEG data by successfully eliminating motion artifacts while preserving crucial brainwave information. To evaluate the practical impact of this noise reduction technique, we assess its performance in the context of seizure detection. The results reveal a substantial improvement in the accuracy and reliability of seizure detection algorithms when applied to EEG data preprocessed with proposed method.

show abstract

“…We adopted the publicly available EEG dataset used by Lee et al [44], which is one of the largest datasets based on EEG-MI tasks. This dataset has been widely used in many previous studies [48][49][50] and has been well collected by proven system protocols [51]. The raw EEG signals were recorded from 54 healthy users (ages: 24-35; 29 males) by 62 channels with a sampling frequency of 1 kHz.…”

Section: A Eeg Datasetsmentioning

confidence: 99%

User Biometric Identification Methodology via EEG-Based Motor Imagery Signals

Bak

Jeong

2023

IEEE Access

View full text Add to dashboard Cite

Human brain activities-electroencephalogram (EEG) signals-are likely to provide a secure biometric approach for user identification because they are more sensitive, secretive, and difficult to replicate. Many studies have recently focused on identifying and quantifying important frequency patterns in motor imagery (MI), recorded through EEG. However, there is still a lack of an optimal methodology for recognizing users with EEG-based MI. Therefore, we aimed to propose an EEG-MI methodology that utilizes optimized feature extraction methods and classifiers to improve user-aware accuracy. To accomplish this goal, we extracted four features related to MI and compared the accuracies for recognizing users using a support vector machine (SVM) and Gaussian Naïve Bayes (GNB). We then used the half-total error rate (HTER) to determine whether the results were reliable due to an imbalance problem caused by the differences in the data sizes. Thus, we used a common spatial pattern (CSP) to achieve the highest user identification accuracies of 98.97% and 97.47% using SVM and GNB, respectively. All user recognition accuracies are guaranteed by the HTERs, which are below 0.5. However, CSP has the disadvantage of decreasing accuracy on a small dataset scale. Therefore, we proposed and tested a statistical methodology for estimating a minimum dataset scale to ensure CSP performance. We confirm that the used dataset adequately guarantees CSP performance. This study makes a great contribution to the field of information security by presenting an EEG-MI methodology that improves the identification accuracy in human biometrics based on EEG-MI signals.

show abstract

Mobile BCI dataset of scalp- and ear-EEGs with ERP and SSVEP paradigms while standing, walking, and running

Cited by 17 publications

References 57 publications

An Adaptive Task-Related Component Analysis Method for SSVEP Recognition

An Adaptive Task-Related Component Analysis Method for SSVEP Recognition

Efficient EEG motion artifact elimination framework for ambulatory epileptic seizure detection application

User Biometric Identification Methodology via EEG-Based Motor Imagery Signals

Contact Info

Product

Resources

About