Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review

Saha, Simanto; Baumert, Mathias

doi:10.3389/fncom.2019.00087

Cited by 155 publications

(128 citation statements)

References 89 publications

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“…Moreover, inter-subject variability, due to anatomical and physiological differences among subjects, also represents an important challenge since it hinders the design of participant-agnostic BCIs. Due to these main challenges (intra- and inter-subject variabilities), most BCIs require time-consuming calibrations to maximize their performance, which makes the creation of one-model-fits-all solutions difficult ( Saha and Baumert, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

BCIAUT-P300: A Multi-Session and Multi-Subject Benchmark Dataset on Autism for P300-Based Brain-Computer-Interfaces

et al. 2020

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There is a lack of multi-session P300 datasets for Brain-Computer Interfaces (BCI). Publicly available datasets are usually limited by small number of participants with few BCI sessions. In this sense, the lack of large, comprehensive datasets with various individuals and multiple sessions has limited advances in the development of more effective data processing and analysis methods for BCI systems. This is particularly evident to explore the feasibility of deep learning methods that require large datasets. Here we present the BCIAUT-P300 dataset, containing 15 autism spectrum disorder individuals undergoing 7 sessions of P300-based BCI joint-attention training, for a total of 105 sessions. The dataset was used for the 2019 IFMBE Scientific Challenge organized during MEDICON 2019 where, in two phases, teams from all over the world tried to achieve the best possible object-detection accuracy based on the P300 signals. This paper presents the characteristics of the dataset and the approaches followed by the 9 finalist teams during the competition. The winner obtained an average accuracy of 92.3% with a convolutional neural network based on EEGNet. The dataset is now publicly released and stands as a benchmark for future P300-based BCI algorithms based on multiple session data.

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Section: Introductionmentioning

confidence: 99%

BCIAUT-P300: A Multi-Session and Multi-Subject Benchmark Dataset on Autism for P300-Based Brain-Computer-Interfaces

et al. 2020

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“…Additionally, the inter-subject template signals are obtained by averaging the partial trials selected from other subjects. Recent studies have demonstrated inter-subject similarity in neural responses occurs because subjects are instructed to perform a specific task over time (Saha and Baumert, 2019 ). Yuan et al presented transfer template-based canonical correlation analysis (tt-CCA) to enhance the detection of SSVEPs by exploiting inter-subject information (Yuan et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Inter- and Intra-subject Template-Based Multivariate Synchronization Index Using an Adaptive Threshold for SSVEP-Based BCIs

Wang

Sun

et al. 2020

Front. Neurosci.

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The steady-state visually evoked potential (SSVEP) has been widely used in brain-computer interfaces (BCIs). Many studies have proved that the Multivariate synchronization index (MSI) is an efficient method for recognizing the frequency components in SSVEP-based BCIs. Despite its success, the recognition accuracy has not been satisfactory because the simplified pre-constructed sine-cosine waves lack abundant features from the real electroencephalogram (EEG) data. Recent advances in addressing this issue have achieved a significant improvement in recognition accuracy by using individual calibration data. In this study, a new extension based on inter-and intra-subject template signals is introduced to improve the performance of the standard MSI method. Through template transfer, inter-subject similarity and variability are employed to enhance the robustness of SSVEP recognition. Additionally, most existed methods for SSVEP recognition utilize a fixed time window (TW) to perform frequency domain analysis, which limits the information transfer rate (ITR) of BCIs. For addressing this problem, a novel adaptive threshold strategy is integrated into the extension of MSI, which uses a dynamic window to extract the temporal features of SSVEPs and recognizes the stimulus frequency based on a pre-set threshold. The pre-set threshold contributes to obtaining an appropriate and shorter signal length for frequency recognition and filtering ignored-invalid trials. The proposed method is evaluated on a 12-class SSVEP dataset recorded from 10 subjects, and the result shows that this achieves higher recognition accuracy and information transfer rate when compared with the CCA, MSI, Multi-set CCA, and Individual Template-based CCA. This paper demonstrates that the proposed method is a promising approach for developing high-speed BCIs.

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“…Recent development of wireless EEG caps and low-cost commercial headsets has substantially reduced the setup time for real-world recordings, however they often come at the cost of precision and reliability. Also, there have been attempts in reducing calibration time by means of machine learning techniques and adaptive classifiers that extract common features among all users (Lotte, 2015 ), known as inter-subject associativity (Saha and Baumert, 2019 ). On the other hand, deep learning methods have been suggested for automatic learning of representations in the brain activity, thereby reducing the pre-processing and manual feature extraction that is required for BCI classifier training (Nagel and Spüler, 2019 ; Tanveer et al, 2019 ).…”

Section: Prospects and Challengesmentioning

confidence: 99%

Passive Brain-Computer Interfaces for Enhanced Human-Robot Interaction

Alimardani

Hiraki

2020

Front. Robot. AI

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Brain-computer interfaces (BCIs) have long been seen as control interfaces that translate changes in brain activity, produced either by means of a volitional modulation or in response to an external stimulation. However, recent trends in the BCI and neurofeedback research highlight passive monitoring of a user's brain activity in order to estimate cognitive load, attention level, perceived errors and emotions. Extraction of such higher order information from brain signals is seen as a gateway for facilitation of interaction between humans and intelligent systems. Particularly in the field of robotics, passive BCIs provide a promising channel for prediction of user's cognitive and affective state for development of a user-adaptive interaction. In this paper, we first illustrate the state of the art in passive BCI technology and then provide examples of BCI employment in human-robot interaction (HRI). We finally discuss the prospects and challenges in integration of passive BCIs in socially demanding HRI settings. This work intends to inform HRI community of the opportunities offered by passive BCI systems for enhancement of human-robot interaction while recognizing potential pitfalls.

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Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review

Cited by 155 publications

References 89 publications

BCIAUT-P300: A Multi-Session and Multi-Subject Benchmark Dataset on Autism for P300-Based Brain-Computer-Interfaces

BCIAUT-P300: A Multi-Session and Multi-Subject Benchmark Dataset on Autism for P300-Based Brain-Computer-Interfaces

Inter- and Intra-subject Template-Based Multivariate Synchronization Index Using an Adaptive Threshold for SSVEP-Based BCIs

Passive Brain-Computer Interfaces for Enhanced Human-Robot Interaction

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