A Brain-Computer Interface Based on Multifocal SSVEPs Detected by Inter-Task-Related Component Analysis

Tang, Jiming; Xu, Minpeng; Liu, Zheng; Qiao, Jingjuan; Liu, Shuang; Chen, Shanguang; Jung, Tzyy-Ping; Ming, Dong

doi:10.1109/access.2020.3012283

Cited by 7 publications

(4 citation statements)

References 43 publications

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“…TRCA is an efficient decoding method, which was widely used in designing high-speed SSVEP-BCI [9,28]. In its modeling process, multi-channel EEG signals were assumed to consist of a task-related component s(t) and a task-unrelated component n(t) in different linear weights, and this process can be described as:…”

Section: Task-related Component Analysis and Ensemble Trcamentioning

confidence: 99%

Detection of fixation points using a small visual landmark for brain–computer interfaces

Zhou¹,

Xu²,

Xiao³

et al. 2021

J. Neural Eng.

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Objective. The speed of visual brain–computer interfaces (v-BCIs) has been greatly improved in recent years. However, the traditional v-BCI paradigms require users to directly gaze at the intensive flickering items, which would cause severe problems such as visual fatigue and excessive visual resource consumption in practical applications. Therefore, it is imperative to develop a user-friendly v-BCI. Approach. According to the retina-cortical relationship, this study developed a novel BCI paradigm to detect the fixation point of eyes using a small visual stimulus that subtended only 0.6° in visual angle and was out of the central visual field. Specifically, the visual stimulus was treated as a landmark to judge the eccentricity and polar angle of the fixation point. Sixteen different fixation points were selected around the visual landmark, i.e. different combinations of two eccentricities (2° and 4°) and eight polar angles (, , , , , , and ). Twelve subjects participated in this study, and they were asked to gaze at one out of the 16 points for each trial. A multi-class discriminative canonical pattern matching (Multi-DCPM) algorithm was proposed to decode the user’s fixation point. Main results. We found the visual stimulation landmark elicited different spatial event-related potential patterns for different fixation points. Multi-DCPM could achieve an average accuracy of 66.2% with a standard deviation of 15.8% for the classification of the sixteen fixation points, which was significantly higher than traditional algorithms (). Experimental results of this study demonstrate the feasibility of using a small visual stimulus as a landmark to track the relative position of the fixation point. Significance. The proposed new paradigm provides a potential approach to alleviate the problem of irritating stimuli in v-BCIs, which can broaden the applications of BCIs.

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Section: Task-related Component Analysis and Ensemble Trcamentioning

confidence: 99%

Detection of fixation points using a small visual landmark for brain–computer interfaces

Zhou¹,

Xu²,

Xiao³

et al. 2021

J. Neural Eng.

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“…Brain-computer interface (BCI) is a technology that decodes brain signals to control instructions to an external device between a human and a computer [1][2][3][4]. BCI has been applied in many fields, such as medicinal areas [5], robotic arms [6] and communication [7].…”

Section: Introductionmentioning

confidence: 99%

The Effect of SOA on An Asynchronous ERP and VEP-Based BCI

Yang

Liu

et al. 2021

IEEE Access

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Our previous study established an asynchronous BCI system by using the oddball paradigm to simultaneously induce event-related potentials (ERPs) and visual evoked potentials (VEPs) (E-V BCIs). We found that stimulus onset asynchrony (SOA) is an important factor for performance since it significantly affects the ERP and VEP. Increasing the SOA increases the ERP, which improves the accuracy of detecting target stimuli. However, a larger SOA leads to a lower VEP frequency, which causes the VEP to have poor accuracy when discriminating between the brain states. How to balance the two potentials and accuracies is a problem. This study established eight SOAs from 100 ms to 375 ms that were composed of different interstimulus intervals and the same stimulus duration of 80 ms. We used a probability-based Fisher linear discriminant analysis (P-FLDA) classifier to calculate the classification accuracies of the ERP-based visual speller, VEP-based brain state discrimination, and E-V BCI. The results show that as the SOA increases, the amplitudes of N200 and P300 increase, and the accuracy also shows an increasing trend. However, the frequency of the VEP and the accuracies of state discrimination show downward trends. The change in accuracies of the E-V BCI system combining these two parts is nonlinear, and the SOA optimal value is 125 ms. The SOA of 125 ms yields the best accuracies of 95.83% and practical bit rate of 57.17 bits/min in the E-V BCI system, which provides a guideline for selecting the SOA to improve the performance.

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“…Instead of gazing at a single flickering stimulus at a time, Tang et al (2019) designed the multi-focal SSVEPs (mfSSVEPs) paradigm that requires the subject to receive multiple flickers with different frequencies simultaneously. They further designed an mfSSVEPs paradigm containing 32 targets, each comprised 5 flickers flashing at different frequencies (Tang et al 2020b).…”

Section: Vision-related Paradigmsmentioning

confidence: 99%

Review of brain encoding and decoding mechanisms for EEG-based brain–computer interface

Jung

et al. 2021

Cogn Neurodyn

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A brain-computer interface (BCI) can connect humans and machines directly and has achieved successful applications in the past few decades. Many new BCI paradigms and algorithms have been developed in recent years. Therefore, it is necessary to review new progress in BCIs. This paper summarizes progress for EEG-based BCIs from the perspective of encoding paradigms and decoding algorithms, which are two key elements of BCI systems. Encoding paradigms are grouped by their underlying neural meachanisms, namely sensory-and motor-related, vision-related, cognition-related and hybrid paradigms. Decoding algorithms are reviewed in four categories, namely decomposition algorithms, Riemannian geometry, deep learning and transfer learning. This review will provide a comprehensive overview of both modern primary paradigms and algorithms, making it helpful for those who are developing BCI systems.

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A Brain-Computer Interface Based on Multifocal SSVEPs Detected by Inter-Task-Related Component Analysis

Cited by 7 publications

References 43 publications

Detection of fixation points using a small visual landmark for brain–computer interfaces

Detection of fixation points using a small visual landmark for brain–computer interfaces

The Effect of SOA on An Asynchronous ERP and VEP-Based BCI

Review of brain encoding and decoding mechanisms for EEG-based brain–computer interface

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