P300 Speller Performance Predictor Based on RSVP Multi-feature

Won, Kyungho; Kwon, Min Seong; Jang, Sehyeon; Ahn, Minkyu; Jun, Sung Chan

doi:10.3389/fnhum.2019.00261

Cited by 29 publications

(29 citation statements)

References 47 publications

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“…RSVP systems ( Ratcliffe and Puthusserypady, 2020 ) were proposed to solve this problem, but the defect of these protocols was the long duration of the experiment time that deteriorates the ITR and lowers accuracy. Many scholars have done extensive research on improving the performance of BCI, such as creating a P300 speller performance predictor based on RSVP multifeature ( Won et al, 2019 ), finding the optimal features ( Yin et al, 2015 , 2016 ; Won et al, 2018 ), and using deep neural network algorithm ( Zhao et al, 2020 ), which cannot have a better result in a large subject study. In our study, the average ITR of the RSVP speller is 43.18 bits/min, which is 13% higher than the matrix paradigm, and we achieved the better accuracy results at present in a large subject study.…”

Section: Discussionmentioning

confidence: 99%

Improving the Cross-Subject Performance of the ERP-Based Brain–Computer Interface Using Rapid Serial Visual Presentation and Correlation Analysis Rank

Liu

Wang

Sheng

et al. 2020

Front. Hum. Neurosci.

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The brain-computer interface (BCI) is a system that is designed to provide communication channels to anyone through a computer. Initially, it was suggested to help the disabled, but actually had been proposed a wider range of applications. However, the cross-subject recognition in BCI systems is difficult to break apart from the individual specific characteristics, unsteady characteristics, and environmental specific characteristics, which also makes it difficult to develop highly reliable and highly stable BCI systems. Rapid serial visual presentation (RSVP) is one of the most recent spellers with a clean, unified background and a single stimulus, which may evoke event-related potential (ERP) patterns with less individual difference. In order to build a BCI system that allows new users to use it directly without calibration or with less calibration time, RSVP was employed as evoked paradigm, then correlation analysis rank (CAR) algorithm was proposed to improve the cross-individual classification and simultaneously use as less training data as possible. Fifty-eight subjects took part in the experiments. The flash stimulation time is 200 ms, and the off time is 100 ms. The P300 component was locked to the target representation by time. The results showed that RSVP could evoke more similar ERP patterns among subjects compared with matrix paradigm. Then, the included angle cosine was calculated and counted for averaged ERP waveform between each two subjects. The average matching number of all subjects was 6 for the matrix paradigm, while for the RSVP paradigm, the average matching number range was 20 when the threshold value was set to 0.5, more than three times as much larger, quantificationally indicating that ERP waveforms evoked by the RSVP paradigm produced smaller individual differences, and it is more favorable for crosssubject classification. Information transfer rates (ITR) were also calculated for RSVP and matrix paradigms, and the RSVP paradigm got the average ITR of 43.18 bits/min, which was 13% higher than the matrix paradigm. Then, the receiver operating characteristic (ROC) curve value was computed and compared using the proposed CAR algorithm and traditional random selection. The results showed that the proposed CAR got significantly better performance than the traditional random selection and got the best AUC value

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Section: Discussionmentioning

confidence: 99%

Improving the Cross-Subject Performance of the ERP-Based Brain–Computer Interface Using Rapid Serial Visual Presentation and Correlation Analysis Rank

Liu

Wang

Sheng

et al. 2020

Front. Hum. Neurosci.

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“…Previous works have reported single-trial classification rates in the order of 0.8 (see Reference [ 58 , 59 , 65 ]). The classification rates reported are similar to those achieved in this work.…”

Section: Discussionmentioning

confidence: 99%

“…First, the effect of stimulation condition and of the number of symbols on the single-trial classification between target and non-target responses was assessed through a five-fold cross-validation process. Although there exist a sample overlapping, this performance evaluation approach is quite acceptable to estimate the online performance of the BCI [ 58 , 59 ]. Here, all the epochs in a given dataset were randomly allocated into five sets.…”

Section: Methodsmentioning

confidence: 99%

Single-Option P300-BCI Performance Is Affected by Visual Stimulation Conditions

Peguero

Mendoza-Montoya

Antelis

2020

Sensors

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The P300 paradigm is one of the most promising techniques for its robustness and reliability in Brain-Computer Interface (BCI) applications, but it is not exempt from shortcomings. The present work studied single-trial classification effectiveness in distinguishing between target and non-target responses considering two conditions of visual stimulation and the variation of the number of symbols presented to the user in a single-option visual frame. In addition, we also investigated the relationship between the classification results of target and non-target events when training and testing the machine-learning model with datasets containing different stimulation conditions and different number of symbols. To this end, we designed a P300 experimental protocol considering, as conditions of stimulation: the color highlighting or the superimposing of a cartoon face and from four to nine options. These experiments were carried out with 19 healthy subjects in 3 sessions. The results showed that the Event-Related Potentials (ERP) responses and the classification accuracy are stronger with cartoon faces as stimulus type and similar irrespective of the amount of options. In addition, the classification performance is reduced when using datasets with different type of stimulus, but it is similar when using datasets with different the number of symbols. These results have a special connotation for the design of systems, in which it is intended to elicit higher levels of evoked potentials and, at the same time, optimize training time.

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“…In the past decade, human-centered BCIs, such as those in mental fatigue detection tasks (Binias et al, 2020 ; Ko et al, 2020b ), emotion recognition (Qing et al, 2019 ), and controlling exoskeletons (Lee et al, 2017 ) have shed light on the success of improving human ability. An active BCI (Fahimi et al, 2020 ) recognizes complex patterns from EEG spontaneously caused by a user's intention independent of external stimuli, and a reactive BCI (Won et al, 2019 ) identifies brain activities in reaction to external events. A Passive BCI (Ko et al, 2020b ) is exploited to acquire implicit information of a user's cognitive status without any voluntary control.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Deep Learning-Based Short/Zero-Calibration Approaches for EEG-Based Brain–Computer Interfaces

Jeon

Jeong

et al. 2021

Front. Hum. Neurosci.

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Brain–computer interfaces (BCIs) utilizing machine learning techniques are an emerging technology that enables a communication pathway between a user and an external system, such as a computer. Owing to its practicality, electroencephalography (EEG) is one of the most widely used measurements for BCI. However, EEG has complex patterns and EEG-based BCIs mostly involve a cost/time-consuming calibration phase; thus, acquiring sufficient EEG data is rarely possible. Recently, deep learning (DL) has had a theoretical/practical impact on BCI research because of its use in learning representations of complex patterns inherent in EEG. Moreover, algorithmic advances in DL facilitate short/zero-calibration in BCI, thereby suppressing the data acquisition phase. Those advancements include data augmentation (DA), increasing the number of training samples without acquiring additional data, and transfer learning (TL), taking advantage of representative knowledge obtained from one dataset to address the so-called data insufficiency problem in other datasets. In this study, we review DL-based short/zero-calibration methods for BCI. Further, we elaborate methodological/algorithmic trends, highlight intriguing approaches in the literature, and discuss directions for further research. In particular, we search for generative model-based and geometric manipulation-based DA methods. Additionally, we categorize TL techniques in DL-based BCIs into explicit and implicit methods. Our systematization reveals advances in the DA and TL methods. Among the studies reviewed herein, ~45% of DA studies used generative model-based techniques, whereas ~45% of TL studies used explicit knowledge transferring strategy. Moreover, based on our literature review, we recommend an appropriate DA strategy for DL-based BCIs and discuss trends of TLs used in DL-based BCIs.

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P300 Speller Performance Predictor Based on RSVP Multi-feature

Cited by 29 publications

References 47 publications

Improving the Cross-Subject Performance of the ERP-Based Brain–Computer Interface Using Rapid Serial Visual Presentation and Correlation Analysis Rank

Improving the Cross-Subject Performance of the ERP-Based Brain–Computer Interface Using Rapid Serial Visual Presentation and Correlation Analysis Rank

Single-Option P300-BCI Performance Is Affected by Visual Stimulation Conditions

A Survey on Deep Learning-Based Short/Zero-Calibration Approaches for EEG-Based Brain–Computer Interfaces

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