Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis

Nakanishi, Masaki; Wang, Yijun; Chen, Xiaogang; Wang, Yu-Te; Gao, Xiaorong; Jung, Tzyy-Ping

doi:10.1109/tbme.2017.2694818

Cited by 540 publications

(613 citation statements)

References 39 publications

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“…TRCA was originally proposed in functional neuroimaging [21] and then used in SSVEP-based BCIs to obtain optimized spatial filters to improve SNR of SSVEP response [20]. The method recovers the task-related components (here SSVEP) using a linear, weighted sum of the observed signals (here, multichannel EEG signals):…”

Section: Trca-based Methodsmentioning

confidence: 99%

“…They are unable to discriminate two different phases [11], and their performance degrades in short time windows due to the presence of the background noise in the EEG signal. To solve these problems, incorporating individual calibration data has been proposed [12,[17][18][19][20]. Extended CCA method was introduced to combine CCA coefficient with the Pearson correlation coefficients among the test and training data [12].…”

Section: Introductionmentioning

confidence: 99%

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Enhancing performance of subject-specific models via subject-independent information for SSVEP-based BCIs

Mehdizavareh¹,

Hemati²,

Soltanian-Zadeh³

2020

PLoS ONE

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Recently, steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has attracted much attention due to its high information transfer rate (ITR) and increasing number of targets. However, the performance of SSVEP-based methods in terms of accuracy and time length required for target detection can be improved. We propose a new canonical correlation analysis (CCA)-based method to integrate subject-specific models and subject-independent information and enhance BCI performance. To optimize hyperparameters for CCA-based model of a specific subject, we propose to use training data of other subjects. An ensemble version of the proposed method is also developed and used for a fair comparison with ensemble task-related component analysis (TRCA). A publicly available 35-subject SSVEP benchmark dataset is used to evaluate different methods. The proposed method is compared with TRCA and extended CCA methods as reference methods. The performance of the methods is evaluated using classification accuracy and ITR. Offline analysis results show that the proposed method reaches highest ITR compared with TRCA and extended CCA. Also, the proposed method significantly improves performance of extended CCA in all conditions and TRCA for time windows greater than 0.3 s. In addition, the proposed method outperforms TRCA for low number of training blocks and electrodes. This study illustrates that adding subject-independent information to subject-specific models can improve the performance of SSVEP-based BCIs.

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Section: Trca-based Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancing performance of subject-specific models via subject-independent information for SSVEP-based BCIs

Mehdizavareh¹,

Hemati²,

Soltanian-Zadeh³

2020

PLoS ONE

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“…With recent advances in system design and signal processing, the performance of SSVEP-based BCIs has dramatically improved in the past decade [3]. Numbers of studies have reported a variety of BCI applications including text speller [3], [4], phone-dialing system [5], game controller [6], etc.…”

Section: Introductionmentioning

confidence: 99%

“…The target identification process can traditionally be divided into two parts: 1) spatial filtering, and 2) model fitting [3], [7]. Spatial filtering techniques, which include minimum energy combination (MEC) [8], canonical correlation analysis (CCA) [9], and task-related component analysis (TRCA) [4], have been introduced to enhance the signal-tonoise ratio (SNR) of SSVEPs by reducing the interference from the spontaneous EEG activities. After spatial filtering, target stimuli are identified by fitting the models of SSVEPs.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Yuan et al [10], [11] proposed subject-to-subject transfer learning methods, which transfer SSVEP data from existing subjects to new ones. More recently, Nakanishi et al made it possible to transfer SSVEP data across sessions even with different electrode montages [4]. Although these approaches achieved better performance than training-free algorithms, none of them has reached comparable accuracy obtained by using individualized calibration data.…”

Section: Introductionmentioning

confidence: 99%

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Cross-Subject Transfer Learning Improves the Practicality of Real-World Applications of Brain-Computer Interfaces

Chiang

Wei

Nakanishi

et al. 2019

2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)

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Steady-state visual evoked potential (SSVEP)based brain-computer interfaces (BCIs) have shown its robustness in facilitating high-efficiency communication. Stateof-the-art training-based SSVEP decoding methods such as extended Canonical Correlation Analysis (CCA) and Task-Related Component Analysis (TRCA) are the major players that elevate the efficiency of the SSVEP-based BCIs through a calibration process. However, due to notable human variability across individuals and within individuals over time, calibration (training) data collection is non-negligible and often laborious and time-consuming, deteriorating the practicality of SSVEP BCIs in a real-world context. This study aims to develop a cross-subject transferring approach to reduce the need for collecting training data from a test user with a newly proposed least-squares transformation (LST) method. Study results show the capability of the LST in reducing the number of training templates required for a 40-class SSVEP BCI. The LST method may lead to numerous real-world applications using near-zerotraining/plug-and-play high-speed SSVEP BCIs.

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Brain–Computer Interfacing Using EEG

2021

EEG Signal Processing and Machine Learning

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Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis

Abstract: The high-speed SSVEP-based BCIs using the TRCA method have great potential for various applications in communication and control.

Cited by 540 publications

References 39 publications

Enhancing performance of subject-specific models via subject-independent information for SSVEP-based BCIs

Enhancing performance of subject-specific models via subject-independent information for SSVEP-based BCIs

Cross-Subject Transfer Learning Improves the Practicality of Real-World Applications of Brain-Computer Interfaces

Brain–Computer Interfacing Using EEG

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