Relationships Between Behavioral And Single-Trial Target Detection Performance With Magnetoencephalography

Target detection during serial visual presentation tasks is an active research topic in the brain-computer interface (BCI) community as this type of paradigm allows to take advantage of event-related potentials (ERPs) through electroencephalography (EEG) recordings to enhance the accuracy of target detection. The detection of brain evoked responses at the single-trial level remains a challenging task and can be exploited in various applications. Typical non-invasive BCIs based on event-related brain responses use EEG. In clinical settings, brain signals recorded with magnetoencephalography (MEG) can be advantageously used thanks to their high spatial and temporal resolution. In this study, we address the problem of the relationships between behavioral performance and single-trial detection by considering a task with different levels of difficulty. We consider images of faces with six different facial expressions (anger, disgust, fear, neutrality, sadness, and happiness). We consider MEG signals recorded on ten healthy participants in six sessions where targets were one of the six types of facial expressions in each session. The results support the conclusion that a high performance can be obtained at the single-trial level (AUG = 0.903±0.045), and that the performance is correlated with the behavioral performance (reaction time and hit rate).

I. Introduction

Most of the Brain-Computer Interfaces (BCIs) are currently based on electroencephalography (EEG) recordings as EEG amplifiers can be portable and wireless. However, despite the possibility to obtain a high spatial resolution with high density EEG (e.g., 128 channels), it requires a long preparation time, which can become an obstacle when dealing daily with BCI or when an application requires a high attentional load and the preparation time absorbs the focus of the participant, limiting his/her attention during signal recording. Although dry EEG solutions have been recently proposed, they don't provide a high spatial resolution due to the size of the sensors that are used. In clinical settings where the quality of the signal is a major concern and there is no constraint related to the portability, other brain imaging techniques can be considered, such as magnetoencephalography (MEG), which has, despite its current cost, advantages over EEG signals.