Abstract
This study explores the feasibility of developing an EEG-based neural indicator of task proficiency based on subject-independent mental state classification. Such a neural indicator could be used in the development of a passive brain-computer interface to potentially enhance training effectiveness and efficiency. A spatial knowledge acquisition training protocol was used in this study. Fifteen participants acquired spatial knowledge in a novel virtual environment via 60 navigation trials (divided into ten blocks). Task performance (time required to complete trials), perceived task certainty, and EEG signal data were collected. For each participant, 1 s epochs of EEG data were classified as either from the "low proficiency, 0" or "high proficiency, 1" state using a support vector machine classifier trained on data from the remaining 14 participants. The average epoch classification per trial was used to calculate a neural indicator (NI) ranging from 0 ("low proficiency") to 1 ("high proficiency"). Trends in the NI throughout the session-from the first to the last trial-were analyzed using a repeated measure mixed model linear regression. There were nine participants for whom the neural indicator was quite effective in tracking the progression from low to high proficiency. These participants demonstrated a significant (p < 0.001) increase in the neural indicator throughout the training from NI = 0.15 in block 1 to NI = 0.81 (on average) in block 10, with the average NI reaching a plateau after block 7. For the remaining participants, the NI did not effectively track the progression of task proficiency. The results support the potential of a subject-independent EEG-based neural indicator of task proficiency and encourage further research toward this objective.
Highlights
A passive brain-computer interface is a system that enriches human-machine interaction by providing implicit information about a user’s mental state and adapting the environment (Brunner et al, 2015)
A passive brain-computer interface (pBCI) for learning/training would support, for example, programs based on “mastery learning,” an effective method of instruction where a trainee must achieve proficiency in more fundamental, prerequisite knowledge prior to progressing to subsequent tasks of increasing difficulty and complexity (Block and Burns, 1976; Kulik et al, 1990). pBCIs would be useful for any software-based training programs, including immersive, experiential training based on simulation or virtual environments (Lécuyer et al, 2008)
This study explored a potential neural indicator of task proficiency based on subject-independent EEG signal classification
Summary
A passive brain-computer interface (pBCI) is a system that enriches human-machine interaction by providing implicit information about a user’s mental state (e.g., cognitive, affective) and adapting the environment (Brunner et al, 2015). Example applications include workload estimation in the workplace, feedback of mental states to improve wellness or manage stress, and enhanced product development (Baldwin and Penaranda, 2012). An example of the latter is “Intelligent Tutoring Systems (ITS)” (Chaouachi et al, 2015), which provide the user with an adapted and individualized learning environment. A trainee’s performance may reach acceptable levels as indicated by performance measures alone before they are truly proficient in a skill; that is, before they are able to achieve good performance with little mental effort. A truly effective training program would make use of an assessment measure that could capture this information about the trainee’s cognitive state
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