Liberati, Giulia
[UCL]
Federici, Stefano
Pasqualotto, Emanuele
[UCL]
BACKGROUND: Brain–computer interfaces (BCIs) allow persons with impaired mobility to communicate and interact with the environment, supporting goal-directed thinking and cognitive function. Ideally, a BCI should be able to recognize a user’s internal state and adapt to it in real-time, to improve interaction. OBJECTIVE: Our aim was to examine studies investigating the recognition of affective states from neurophysiological signals, evaluating how current achievements can be applied to improve BCIs. METHODS: Following the PRISMA guidelines, we performed a literature search using PubMed and ProQuest databases. We considered peer-reviewed research articles in English, focusing on the recognition of emotions from neurophysiological signals in view of enhancing BCI use. RESULTS: Of the 526 identified records, 30 articles comprising 32 studies were eligible for review. Their analysis shows that the affective BCI field is developing, with a variety of combinations of neuroimaging techniques, selected neurophysiological features, and classification algorithms currently being tested. Nevertheless, there is a gap between laboratory experiments and their translation to everyday situations. CONCLUSIONS: BCI developers should focus on testing emotion classification with patients in ecological settings and in real-time, with more precise definitions of what they are investigating, and communicating results in a standardized way.
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Bibliographic reference |
Liberati, Giulia ; Federici, Stefano ; Pasqualotto, Emanuele. Extracting neurophysiological signals reflecting users’ emotional and affective responses to BCI use: A systematic literature review. In: NeuroRehabilitation : an interdisciplinary journal, Vol. 37, no.3, p. 341-358 (2015) |
Permanent URL |
http://hdl.handle.net/2078.1/169999 |