P123 Innovative Strategies to Balance Speed and Accuracy in P300-ERP Detection for Enhanced Online Brain-Computer Interfaces
Javier Jiménez*1, Francisco B. Rodríguez1
1Grupo de Neurocomputación Biológica, Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid, Madrid, Spain *Email: javier.jimenez01@uam.es
Introduction
Brain-Computer Interfaces (BCIs) interpret signals the brain generates to control devices. These signals can be related to known Event-Related Potentials (ERPs) registered with neuroimaging methods such as electroencephalography [1]. However, ERP detection requires many trials due to its low signal-to-noise ratio [2]. This detection method leads to the well-known speed-accuracy trade-off [3], as each trial adds to the time required for evoking ERPs. We propose a methodology for analyzing this trade-off using two new measures to find the best number of trials for the required accuracy. Finally, these measures were assessed using a P300-ERP dataset [4] to explore their potential as additional early-stopping methods in future online BCI setups. Methods In the literature, the speed-accuracy trade-off is usually studied by the employment of BCI measures such as the Information-Transfer Rate (ITR) [5]. However, these measures combine speed and accuracy within a single measure hindering BCI’s evaluation of such speed and accuracy separately. Considering these two concepts may be of interest to BCI users who would be able to decide whether they prefer a fast or accurate BCI in different scenarios. This work introduces two measures called Gain and Conservation which consider the amount of saved time and preserved accuracy, respectively, against a baseline BCI employing a Bayesian Linear Discriminant Analysis (BLDA) classifier to detect P300s. Results The new measures were tested against Hoffmann et. al. dataset [4] employing a BLDA classifier to detect P300s in combination with a traditional fixed-stop strategy based upon stopping after a fixed number of trials to evaluate the speed and accuracy of BCIs. For this paradigm, the expected behaviour of these measures would be to follow the speed-accuracy trade-off i.e. faster BCIs would correspond with inaccurate BCIs and vice-versa. This is because faster BCIs employ fewer trials and therefore have access to less information leading to worse P300 detection performances. Such behaviour can be seen in (Fig. 1) where the speed and accuracy of a BCI are represented by the Gain and Conservation, respectively. Discussion The described framework proposes two measures capable of evaluating BCIs’ speed and accuracy separately in contrast with other measures such as the ITR [5]. With these new measures, designers and users are provided with a controllable way to optimize BCIs towards different goals prioritizing one measurement over the other under demand. Furthermore, employing these measures offers detailed insights into the behaviors of different BCIs and early-stopping strategies [3] among other applications. To conclude, these measurements can be tracked during the BCI operation, which represents a key future direction of this work:leveraging the speed-accuracy trade-off of BCIs online.
Figure 1. Figure 1: Pseudo-online evolution along different trials from Hoffmann et. al. [4] of normalized Gain and Conservation measures for a fixed-stop strategy compared against its ITR. Acknowledgements This work was supported by the Predoctoral Research Grants of the Universidad Autónoma de Madrid (FPI-UAM) and by PID2023-149669NB-I00 (MCIN/AEI and ERDF – “A way of making Europe”). References 1. 10.1016/0013-4694(88)90149-6 2. 10.1016/j.neuroimage.2010.06.048 3. 10.1088/1741-2560/10/3/036025 4. 10.1016/j.jneumeth.2007.03.005 5. 10.1016/S1388-2457(02)00057-3
