AMBER: advancing multimodal brain-computer interfaces for enhanced robustness—A dataset for naturalistic settings

Awais, Muhammad Ahsan; Redmond, Peter; Ward, Tomas Emmanuel; Healy, Graham

doi:10.3389/fnrgo.2023.1216440

Cited by 1 publication

(2 citation statements)

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“…The primary objective of this research was to predict target versus non-target single-trial responses in a novel dataset, specifically, a RSVP-based P300 dataset (Awais et al 2023), which included both clean and intentionally induced noisy recording conditions. We have systematically examined the impact of noise and artifacts on detection performance, examining how this influences the accuracy of predictions.…”

Section: Related Work and Problem Statementmentioning

confidence: 99%

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From lab to life: assessing the impact of real-world interactions on the operation of rapid serial visual presentation-based brain-computer interfaces

Ahsan Awais,

Ward,

Redmond

et al. 2024

J. Neural Eng.

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Objective. Brain-computer interfaces (BCI) have been extensively researched in controlled lab settings where the P300 Event-related Potential (ERP), elicited in the Rapid Serial Visual Presentation (RSVP) paradigm, has shown promising potential. However, deploying BCIs outside of laboratory settings is challenging due to the presence of contaminating artifacts that often occur as a result of activities such as talking, head movements, and body movements. These artifacts can severely contaminate the measured EEG signals and consequently impede detection of the P300 ERP. Our goal is to assess the impact of these real-world noise factors on the performance of a RSVP-BCI, specifically focusing on single-trial P300 detection. Approach. In this study, we examine the impact of movement activity on the performance of a P300-based RSVP-BCI application designed to allow users to search images at high speed. Using machine learning, we assessed P300 detection performance using both EEG data captured in optimal recording conditions (e.g. where participants were instructed to refrain from moving) and a variety of conditions where the participant intentionally produced movements to contaminate the EEG recording. Main results. The results, presented as Area Under the Receiver Operating Characteristic Curve (ROC-AUC) scores, provide insight into the significant impact of noise on single-trial P300 detection. Notably, there is a reduction in classifier detection accuracy when intentionally contaminated RSVP trials are used for training and testing, when compared to using non-intentionally contaminated RSVP trials. Significance. Our findings underscore the necessity of addressing and mitigating noise in EEG recordings to facilitate the use of BCIs in real-world settings, thus extending the reach of EEG technology beyond the confines of the laboratory.

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Section: Related Work and Problem Statementmentioning

confidence: 99%

“…The dataset employed in this work is 'AMBER: Advancing Multimodal BCIs for Enhanced Robustness-A Dataset for Naturalistic Settings' (Awais et al 2023). Data collection took place at Dublin City University, Ireland, and the study included ten healthy participants within the age range of 20-35 years.…”

Section: Datasetmentioning

confidence: 99%