Effects of Emotional Stimulations on the Online Operation of a P300-Based Brain–Computer Interface

Abstract

Using P300-based brain–computer interfaces (BCIs) in daily life should take into account the user’s emotional state because various emotional conditions are likely to influence event-related potentials (ERPs) and consequently the performance of P300-based BCIs. This study aimed at investigating whether external emotional stimuli affect the performance of a P300-based BCI, particularly built for controlling home appliances. We presented a set of emotional auditory stimuli to subjects, which had been selected for each subject based on individual valence scores evaluated a priori, while they were controlling an electric light device using a P300-based BCI. There were four conditions regarding the auditory stimuli, including high valence, low valence, noise, and no sound. As a result, subjects controlled the electric light device using the BCI in real time with a mean accuracy of 88.14%. The overall accuracy and P300 features over most EEG channels did not show a significant difference between the four auditory conditions (p > 0.05). When we measured emotional states using frontal alpha asymmetry (FAA) and compared FAA across the auditory conditions, we also found no significant difference (p > 0.05). Our results suggest that there is no clear evidence to support a hypothesis that external emotional stimuli influence the P300-based BCI performance or the P300 features while people are controlling devices using the BCI in real time. This study may provide useful information for those who are concerned with the implementation of a P300-based BCI in practice.

Task-Relevant Stimulus Design Improves P300-Based Brain-Computer Interfaces

“…We crafted visual stimuli that were tailored by our previous work for the integration of BCIs into different user interfaces (UIs) across monitors, augmented reality (AR), and virtual reality (VR) environments [12]. Positioned at the four quadrants of the display (top-left, bottom-left, top-right, bottom-right), these stimuli featured icons indicative of on/off, play, stop, and pause functions, set against a blue rectangular background, mirroring the potential control of an external device (i.e., Bluetooth speaker in this study) [12].…”

“…Despite advancements in single-trial P300-based BCIs, their performance falls short compared to multi-trial approaches [10, 11]. Second, P300-based BCIs necessitate personalized feature extraction and decoding algorithms as well as separate calibration sessions for each use, thus hindering daily practicality [12]. Transfer learning strategies have been developed to minimize the extensive calibration requirements; yet, attaining the robustness necessary for consistent applications across diverse datasets has proven to be challenging [13-16].…”

Distraction Impact of Concurrent Conversation on Event-Related Potential Based Brain-Computer Interfaces

“…The design of visual stimuli for ERP-BCIs was adopted from previous studies [46,47] (Figure 1(A)). As visual stimuli, four blue squares each containing an icon indicating each of the four functions of the smart light to be controlled were presented on the display’s four corners.…”

Freeing P300-Based Brain-Computer Interfaces from Daily Calibration by Extracting Daily Common ERPs

“…As such, one round of highlighting the four stimuli took 0.6s. This design of stimulus presentation time (0.075s) and ISI (0.075s) was determined as the minimum amount of time needed to present visual stimuli in the P300-based BCI system by previous work [41]. We repeated the round of stimulus presentation ten times with no break between rounds, which took 6s.…”