An improved P300 pattern in BCI to catch user’s attention

Jin, Jing; Zhang, Hanhan; Daly, Ian; Wang, Xingyu; Cichocki, Andrzej

doi:10.1088/1741-2552/aa6213

Cited by 64 publications

(34 citation statements)

References 57 publications

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“…Previous work has shown that the frequency band for the P300 is mainly between 1 and 10 Hz (Basar-Eroglu et al, 1992) and different band passes have been used to filter EEG data to acquire better classification accuracy, such as 1-4, 1-12, and 1-30 Hz (Jin et al, 2017). In this study, we compared the classification accuracies at the first three superpositions (superposition times represent the number of trials, that is, the repeating times of 6 rows/columns flashing) between 1-4, 1-12, and 1-30 Hz for the famous face and self-face spelling paradigms (Figure 8).…”

Section: Classification Resultsmentioning

confidence: 99%

The Self-Face Paradigm Improves the Performance of the P300-Speller System

Gao

et al. 2020

Front. Comput. Neurosci.

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Objective: Previous studies have shown that the performance of the famous face P300-speller was better than that of the classical row/column flashing P300-speller. Furthermore, in some studies, the brain was more active when responding to one's own face than to a famous face, and a self-face stimulus elicited larger amplitude event-related potentials (ERPs) than did a famous face. Thus, we aimed to study the role of the self-face paradigm on further improving the performance of the P300-speller system with the famous face P300-speller paradigm as the control paradigm. Methods: We designed two facial P300-speller paradigms based on the self-face and a famous face (Ming Yao, a sports star; the famous face spelling paradigm) with a neutral expression. Results: ERP amplitudes were significantly greater in the self-face than in the famous face spelling paradigm at the parietal area from 340 to 480 ms (P300), from 480 to 600 ms (P600f), and at the fronto-central area from 700 to 800 ms. Offline and online classification results showed that the self-face spelling paradigm accuracies were significantly higher than those of the famous face spelling paradigm at superposing first two times (P < 0.05). Similar results were found for information transfer rates (P < 0.05). Conclusions: The self-face spelling paradigm significantly improved the performance of the P300-speller system. This has significant practical applications for brain-computer interfaces (BCIs) and could avoid infringement issues caused by using images of other people's faces.

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Section: Classification Resultsmentioning

confidence: 99%

The Self-Face Paradigm Improves the Performance of the P300-Speller System

Gao

et al. 2020

Front. Comput. Neurosci.

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“…Consideration of results from cognitive psychology may lead to new paradigms that might yield more robust ERP differences, are more engaging, or overcome practical challenges. Figure 7 illustrates a novel approach in which end users were asked to count the number of red dots in a honeycomb‐shaped icon near the target, rather than the total number of times the target flashed (Jin, Zhang, Daly, Wang, & Cichocki, 2017). Like the face speller approach, the goal of this red dot approach was to create a slightly different task for the user that could elicit more distinct differences between target versus nontarget ERPs, and may also increase subjective factors such as ease of use or user satisfaction.…”

Section: Theoretical Implicationsmentioning

confidence: 99%

30+ years of P300 brain–computer interfaces

2020

Self Cite

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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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“…Bayesian linear discriminant analysis (BLDA) was chosen to build the classifier model for online validation. is approach has been widely employed in an increasing number of P300 BCI systems due to its superior classification performance [27,28]. e classification rule can be defined as…”

Section: Feature Extraction and Classificationmentioning

confidence: 99%

Effects of Visual Attention on Tactile P300 BCI

Chen

Jin

Daly

et al. 2020

Computational Intelligence and Neuroscience

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Objective. Tactile P300 brain-computer interfaces (BCIs) can be manipulated by users who only need to focus their attention on a single-target stimulus within a stream of tactile stimuli. To date, a multitude of tactile P300 BCIs have been proposed. In this study, our main purpose is to explore and investigate the effects of visual attention on a tactile P300 BCI. Approach. We designed a conventional tactile P300 BCI where vibration stimuli were provided by five stimulators and two of them were fixed on target locations on the participant's left and right wrists. Two conditions (one condition with visual attention and the other condition without visual attention) were tested by eleven healthy participants. Main Results. Our results showed that, when participants visually attended to the location of target stimulus, significantly higher classification accuracies and information transfer rates were obtained (both for p < 0.05). Furthermore, participants reported that visually attending to the stimulus made it easier to identify the target stimulus in random sequences of vibration stimuli. Significance. ese findings suggest that visual attention has positive effects on both tactile P300 BCI performance and user-evaluation.

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An improved P300 pattern in BCI to catch user’s attention

Abstract: The results indicate that this proposed method can be a promising approach to improve the performance of the BCI system and can be an efficient method in daily application.

Cited by 64 publications

References 57 publications

The Self-Face Paradigm Improves the Performance of the P300-Speller System

The Self-Face Paradigm Improves the Performance of the P300-Speller System

30+ years of P300 brain–computer interfaces

Effects of Visual Attention on Tactile P300 BCI

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