Increasing session-to-session transfer in a brain–computer interface with on-site background noise acquisition

H, Cho; Ahn, Minkyu; Kim, Ki Woong; Jun, Sung Chan

doi:10.1088/1741-2560/12/6/066009

Cited by 39 publications

(26 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the online task, the subjects received feedback from a 2 s EEG signal window after they began to imagine limb movement. Each subject performed the online MI task in 75 trials per class [26].…”

Section: Data Descriptionmentioning

confidence: 99%

“…They were down-sampled by 4 and a time window of 0-2 s after imagination onset was used for analysis. Feature extraction (invariant common spatio-spectral pattern [26,27]) and binary classification (Fisher's linear discriminant analysis (FLDA) [28]) of these data were applied to each pair of classes. Thus, three classifiers were generated with 3-class MI.…”

Section: Motor Imagery Task Analysismentioning

confidence: 99%

“…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%

See 2 more Smart Citations

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: Data Descriptionmentioning

confidence: 99%

Section: Motor Imagery Task Analysismentioning

confidence: 99%

Section: Resting State Analysismentioning

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

show abstract

“…Even though regularization is of specific importance for ill-posed problems such as source reconstruction (Tian et al, 2013), less underdetermined problems can also profit. For CSP, a broad bandwidth of regularization approaches has been published, such as L1-and L2-norm penalties (Wang and Li, 2016;Lotte and Guan, 2011;Arvaneh et al, 2011;Farquhar et al, 2006), regularized transfer learning strategies that accumulate information across multiple sessions and subjects (Cheng et al, 2017;Devlaminck et al, 2011;Samek et al, 2013;Kang et al, 2009;Lotte and Guan, 2010) and variants which favor invariant solutions across sessions/runs under EEG non-stationarities (Arvaneh et al, 2013;Samek et al, 2012Samek et al, , 2014Cho et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Characterizing Regularization Techniques for Spatial Filter Optimization in Oscillatory EEG Regression Problems

et al. 2018

View full text Add to dashboard Cite

We report on novel supervised algorithms for single-trial brain state decoding. Their reliability and robustness are essential to efficiently perform neurotechnological applications in closed-loop. When brain activity is assessed by multichannel recordings, spatial filters computed by the source power comodulation (SPoC) algorithm allow identifying oscillatory subspaces. They regress to a known continuous trial-wise variable reflecting, e.g. stimulus characteristics, cognitive processing or behavior. In small dataset scenarios, this supervised method tends to overfit to its training data as the involved recordings via electroencephalogram (EEG), magnetoencephalogram or local field potentials generally provide a low signal-to-noise ratio. To improve upon this, we propose and characterize three types of regularization techniques for SPoC: approaches using Tikhonov regularization (which requires model selection via cross-validation), combinations of Tikhonov regularization and covariance matrix normalization as well as strategies exploiting analytical covariance matrix shrinkage. All proposed techniques were evaluated both in a novel simulation framework and on real-world data. Based on the simulation findings, we saw our expectations fulfilled, that SPoC regularization generally reveals the largest benefit for small training sets and under severe label noise conditions. Relevant for practitioners, we derived operating ranges of regularization hyperparameters for cross-validation based approaches and offer open source code. Evaluating all methods additionally on real-world data, we observed an improved regression performance mainly for datasets from subjects with initially poor performance. With this proof-of-concept paper, we provided a generalizable regularization framework for SPoC which may serve as a starting point for implementing advanced techniques in the future.

show abstract

“…To classify MI EEG signals, a variety of machine learning algorithms have appeared in the literature [17]; they include support vector machine (SVM) [18], linear discriminant analysis (LDA) [19,20], and restricted Boltzmann machines (RBM) [21]. The deep neural network (DNN) approach has recently shown excellent classification performance in this field.…”

Section: Introductionmentioning

confidence: 99%

Application of Continuous Wavelet Transform and Convolutional Neural Network in Decoding Motor Imagery Brain-Computer Interface

Lee

Choi

2019

Entropy

View full text Add to dashboard Cite

The motor imagery-based brain-computer interface (BCI) using electroencephalography (EEG) has been receiving attention from neural engineering researchers and is being applied to various rehabilitation applications. However, the performance degradation caused by motor imagery EEG with very low single-to-noise ratio faces several application issues with the use of a BCI system. In this paper, we propose a novel motor imagery classification scheme based on the continuous wavelet transform and the convolutional neural network. Continuous wavelet transform with three mother wavelets is used to capture a highly informative EEG image by combining time-frequency and electrode location. A convolutional neural network is then designed to both classify motor imagery tasks and reduce computation complexity. The proposed method was validated using two public BCI datasets, BCI competition IV dataset 2b and BCI competition II dataset III. The proposed methods were found to achieve improved classification performance compared with the existing methods, thus showcasing the feasibility of motor imagery BCI.

show abstract

Increasing session-to-session transfer in a brain–computer interface with on-site background noise acquisition

Abstract: We inferred from our results that, with an on-site background noise suppression feature extractor and pre-existing training data, further training time may be unnecessary.

Cited by 39 publications

References 51 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

Characterizing Regularization Techniques for Spatial Filter Optimization in Oscillatory EEG Regression Problems

Application of Continuous Wavelet Transform and Convolutional Neural Network in Decoding Motor Imagery Brain-Computer Interface

Contact Info

Product

Resources

About