A Passive EEG-BCI for Single-Trial Detection of Changes in Mental State

Abstract

Traditional brain-computer interfaces often exhibit unstable performance over time. It has recently been proposed that passive brain-computer interfaces may provide a way to complement and stabilize these traditional systems. In this study, we investigated the feasibility of a passive brain-computer interface that uses electroencephalography to monitor changes in mental state on a single-trial basis. We recorded cortical activity from 15 locations while 11 able-bodied adults completed a series of challenging mental tasks. Using a feature clustering algorithm to account for redundancy in EEG signal features, we classified self-reported changes in fatigue, frustration, and attention levels with 74.8 ± 9.1%, 71.6 ± 5.6%, and 84.8 ± 7.4% accuracy, respectively. Based on the most frequently-selected features across all participants, we note the importance of the frontal and central electrodes for fatigue detection, posterior alpha band and frontal beta band activity for frustration detection, and posterior alpha band activity for attention detection. Future work will focus on integrating these results with an active brain-computer interface.

Bioinformatics-Based Adaptive System towards Real-Time Dynamic E-learning Content Personalization

“…However, very few recent studies on AEHS have started exploring other pedagogical learning theories so as to adapt learners' cognitive processes [18][19][20][21] and metacognitive skills [22] into e-learning platforms. Such cognitive processes play a crucial role in predicting learners' performance, attention level [23,24], and cognitive load [25][26][27]. Hence, the design of this study approach is highly influenced by the previously mentioned pilot studies on Brain-Computer Interface (BCI) [23][24][25][26] and cognitive load [22,27].…”

“…Such cognitive processes play a crucial role in predicting learners' performance, attention level [23,24], and cognitive load [25][26][27]. Hence, the design of this study approach is highly influenced by the previously mentioned pilot studies on Brain-Computer Interface (BCI) [23][24][25][26] and cognitive load [22,27]. Thus, this study approach derives its conceptual framework not only from educational technology (AEHS) [28] but also cognitive science (visual spatial attention and cognitive load) and educational psychology.…”

Classification of Movement Preparation Between Attended and Distracted Self-Paced Motor Tasks

“…Previous studies have relied only on spectral features from EEG signals to reflect changes in attention, alertness and workload [46,47]. Specifically, for attention to a motor task, classification of attentional state attained values of only 61-68% [48,49]. Although the latter study is not directly comparable to the current study due to methodological differences, classification accuracies presented here were on average ~84%.…”

Wired Emotions: Ethical Issues of Affective Brain–Computer Interfaces

“…Further, levels of frustration or joy could be used to adapt a computer application to the affective state of the user. Based on research on the classification of sadness and happiness using EEG (Pan et al 2016) and research on the neurophysiological underpinnings of frustration (Myrden and Chau 2017;Reuderink et al 2013), one can easily envision a computer application that adapts to these affective states of the user. A potential field for such adaptive computer applications is computer games, where information about the affective state of the user could be used to change how the game is presented or how the game unfolds in order to match or influence the affective state of the player (Andujar et al 2015).…”