VISUAL PARADIGM COMPARISON SERIAL
VISUAL PARADIGM COMPARISON TRIAL

VISUAL PARADIGM COMPARISON SERIAL

In this respect, we proposed another large EEG dataset that contains eyes-open/closed resting state, rapid serial visual presentation (RSVP), and visual P300-based BCI from 55 participants. examined cross-dataset variability and proposed a pre-alignment method across EEG dataset using eight BCI datasets containing various number of participants (average N = 27.13 ± 36.52, 4 to 109 participants) 16.

Such datasets are highly advantageous because of the amount of information available to train complex neural networks and transfer learning, and they are likely to have a broad performance distribution so that one could investigate whether a model works properly for a wide spectrum of participants. The BCI competition datasets have been used commonly to evaluate proposed model performance 12, 13 however, recently, datasets with a large number of participants have growing attention as benchmark datasets 14, 15. In addition, classification accuracy has been improved by applying deep learning techniques to BCI, and many cross-subject models that use other participants’ training data have been proposed 9, 10, 11. As P300 has been reported to be highly stable 7 and replicable 8, the P300 speller yields stable performance and has helped people who require a communication tool that uses brain activity alone. Among the BCI applications, P300 speller is a popular BCI application Farwell and Donchin developed in 1988 that enters letters using brain activity 3 called P300, which is one of the event-related potentials (ERPs) showing a positive deflection in EEG that appears approximately 300 ms in response to infrequent target stimuli 4, 5, 6. According to control signals, BCI can be divided into several types 2 each type can provide a specific function, such as cursor control, virtual keyboard, and so on.

Further, it may be used to evaluate feature extraction and classification algorithm effectively, such as for cross-subjects/cross-datasets, and even for the cross-paradigm BCI model.įor many years, people have benefited from brain-computer interface (BCI) as a new non-muscular channel for communicating with the external world 1. The presented dataset could potentially improve P300 speller applications.

VISUAL PARADIGM COMPARISON TRIAL

In addition, P300 speller performance over trial repetitions up to 15 was explored. Regarding P300 speller performance, the participants (N = 55) achieved about 92% mean accuracy. At the feature level, we observed notable ERPs (at 315 ms in the RSVP at 262 ms in the P300 speller) during target events compared to non-target events. For the RSVP, the participants (N = 50) achieved about 92% mean target detection accuracy. We validated our dataset with respect to features and accuracy. Accordingly, our electroencephalogram (EEG) dataset for rapid serial visual representation (RSVP) and P300 speller may contribute to increasing such BCI research. However, to do so, a large and clear dataset is essential to increase the model’s reliability and performance. As attention to deep learning techniques has grown, many researchers have attempted to develop ready-to-go brain-computer interfaces (BCIs) that include automatic processing pipelines.

Visual paradigm comparison