Abstract
Estimating cognitive or affective state from neurophysiological signals and designing applications that make use of this information requires expertise in many disciplines such as neurophysiology, machine learning, experimental psychology, and human factors. This makes it difficult to perform research that is strong in all its aspects as well as to judge a study or application on its merits. On the occasion of the special topic “Using neurophysiological signals that reflect cognitive or affective state” we here summarize often occurring pitfalls and recommendations on how to avoid them, both for authors (researchers) and readers. They relate to defining the state of interest, the neurophysiological processes that are expected to be involved in the state of interest, confounding factors, inadvertently “cheating” with classification analyses, insight on what underlies successful state estimation, and finally, the added value of neurophysiological measures in the context of an application. We hope that this paper will support the community in producing high quality studies and well-validated, useful applications.
Highlights
A Brain–Computer Interface (BCI) has commonly been defined as a communication system in which messages or commands that an individual sends are encoded from brain activity as for example recorded through EEG (Wolpaw et al, 2002)
We hope that our list of recommendations will be useful for researchers working on mental state estimation based on neurophysiological signals, especially for those entering the field, and will help to maintain high standards in both fundamental and applied studies
Continuous knowledge of mental state could potentially be helpful in a range of application areas such as gaming, security, health, and mobility
Summary
A Brain–Computer Interface (BCI) has commonly been defined as a communication system in which messages or commands that an individual sends are encoded from brain activity as for example recorded through EEG (Wolpaw et al, 2002). Researchers in the general field of applied neurophysiology should take this to heart and take care that they make a clear distinction between what can be concluded from their results and what may eventually be possible This is important when addressing the layman audience and peers who may not be experts on all of the underlying expertise areas in the field. We hope that our list of recommendations will be useful for researchers working on mental state estimation based on neurophysiological signals, especially for those entering the field, and will help to maintain high standards in both fundamental and applied studies. It is important to connect the mental state of interest to its operationalization in the study at hand,
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