Comparison of the BCI Performance between the Semitransparent Face Pattern and the Traditional Face Pattern

Cheng, Jinhua; Jin, Jing; Wang, Xingyu

doi:10.1155/2017/1323985

Cited by 12 publications

(8 citation statements)

References 27 publications

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“…Jin and his colleagues reported on a new emotion-detecting BCI system that employs a face-based image-induced paradigm (Cheng et al, 2017 ). In another study, Thammasan et al studied a continuous music-emotion-recognition approach for the construction of affective BCI (Thammasan et al, 2016 ).…”

Section: Discussionmentioning

confidence: 99%

A novel EEG decoding method for a facial-expression-based BCI system using the combined convolutional neural network and genetic algorithm

Liu

et al. 2022

Front. Neurosci.

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Multiple types of brain-control systems have been applied in the field of rehabilitation. As an alternative scheme for balancing user fatigue and the classification accuracy of brain–computer interface (BCI) systems, facial-expression-based brain control technologies have been proposed in the form of novel BCI systems. Unfortunately, existing machine learning algorithms fail to identify the most relevant features of electroencephalogram signals, which further limits the performance of the classifiers. To address this problem, an improved classification method is proposed for facial-expression-based BCI (FE-BCI) systems, using a convolutional neural network (CNN) combined with a genetic algorithm (GA). The CNN was applied to extract features and classify them. The GA was used for hyperparameter selection to extract the most relevant parameters for classification. To validate the superiority of the proposed algorithm used in this study, various experimental performance results were systematically evaluated, and a trained CNN-GA model was constructed to control an intelligent car in real time. The average accuracy across all subjects was 89.21 ± 3.79%, and the highest accuracy was 97.71 ± 2.07%. The superior performance of the proposed algorithm was demonstrated through offline and online experiments. The experimental results demonstrate that our improved FE-BCI system outperforms the traditional methods.

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Section: Discussionmentioning

confidence: 99%

A novel EEG decoding method for a facial-expression-based BCI system using the combined convolutional neural network and genetic algorithm

Liu

et al. 2022

Front. Neurosci.

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“…Related studies have indicated that, when familiar faces are used as stimuli, they may strongly elicit several ERPs, including the VPP, N200, P300, and N400 components. Cheng et al (2017) reported that the semitransparent face pattern can evoke larger N200 components, which can contribute to improving classification accuracy. Eimer (2000) revealed that familiar faces could elicit an N400 in parietal and central cortical areas.…”

Section: Discussionmentioning

confidence: 99%

Comparison of the ERP-Based BCI Performance Among Chromatic (RGB) Semitransparent Face Patterns

Jin

Daly

et al. 2020

Front. Neurosci.

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Objective: Previous studies have shown that combing with color properties may be used as part of the display presented to BCI users in order to improve performance. Build on this, we explored the effects of combinations of face stimuli with three primary colors (RGB) on BCI performance which is assessed by classification accuracy and information transfer rate (ITR). Furthermore, we analyzed the waveforms of three patterns. Methods:We compared three patterns in which semitransparent face is overlaid three primary colors as stimuli: red semitransparent face (RSF), green semitransparent face (GSF), and blue semitransparent face (BSF). Bayesian linear discriminant analysis (BLDA) was used to construct the individual classifier model. In addition, a Repeated-measures ANOVA (RM-ANOVA) and Bonferroni correction were chosen for statistical analysis. Results:The results indicated that the RSF pattern achieved the highest online averaged accuracy with 93.89%, followed by the GSF pattern with 87.78%, while the lowest performance was caused by the BSF pattern with an accuracy of 81.39%. Furthermore, significant differences in classification accuracy and ITR were found between RSF and GSF (p < 0.05) and between RSF and BSF patterns (p < 0.05). Conclusion:The semitransparent faces colored red (RSF) pattern yielded the best performance of the three patterns. The proposed patterns based on ERP-BCI system have a clinically significant impact by increasing communication speed and accuracy of the P300-speller for patients with severe motor impairment.

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“…A study that compared the face speller to a conventional matrix speller and another speller approach found that the face speller was most effective for participants with ALS, due largely to early ERP components (Geronimo & Simmons, 2017). Other reports described potential improvements to the face speller, such as semi‐transparent faces, familiar faces, moving faces, and presenting a new face image with each flash (Cheng, Jin, & Wang, 2017; Jin et al, 2012; Jin, Allison, Zhang, Wang, & Cichocki, 2014; Kaufmann, Schulz, et al, 2013; Yeom, Fazli, Müller, & Lee, 2014). Some work has focused on improving usability with face spellers.…”

Section: Seminal Studiesmentioning

confidence: 99%

30+ years of P300 brain–computer interfaces

2020

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Brain–computer interfaces (BCIs) directly measure brain activity with no physical movement and translate the neural signals into messages. BCIs that employ the P300 event‐related brain potential often have used the visual modality. The end user is presented with flashing stimuli that indicate selections for communication, control, or both. Counting each flash that corresponds to a specific target selection while ignoring other flashes will elicit P300s to only the target selection. P300 BCIs also have been implemented using auditory or tactile stimuli. P300 BCIs have been used with a variety of applications for severely disabled end users in their homes without frequent expert support. P300 BCI research and development has made substantial progress, but challenges remain before these tools can become practical devices for impaired patients and perhaps healthy people.

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Comparison of the BCI Performance between the Semitransparent Face Pattern and the Traditional Face Pattern

Cited by 12 publications

References 27 publications

A novel EEG decoding method for a facial-expression-based BCI system using the combined convolutional neural network and genetic algorithm

A novel EEG decoding method for a facial-expression-based BCI system using the combined convolutional neural network and genetic algorithm

Comparison of the ERP-Based BCI Performance Among Chromatic (RGB) Semitransparent Face Patterns

30+ years of P300 brain–computer interfaces

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