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Mental Face Image Retrieval Based on a Closed-Loop Brain-Computer Interface

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Augmented Cognition (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14019))

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Abstract

Retrieval of mental images from measured brain activity may facilitate communication, especially when verbal or muscular communication is impossible or inefficient. The existing work focuses mostly on retrieving the observed visual stimulus while our interest is on retrieving the imagined mental image. We present a closed-loop BCI framework to retrieve mental images of human faces. We utilize EEG signals as binary feedback to determine the relevance of an image to the target mental image. We employ the feedback to traverse the latent space of a generative model to propose new images closer to the actual target image. We evaluate the proposed framework on 13 volunteers. Unlike previous studies, we do not restrict the possible attributes of the resulting images to predefined semantic classes. Subjective and objective tests validate the ability of our model to retrieve face images similar to the actual target mental images.

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Notes

  1. 1.

    Throughout this paper we designate visual mental images as “mental images”.

  2. 2.

    Similarity here is subjective and the subjects decide by themselves whether a face is similar to the target face or not.

  3. 3.

    19 people participated in the survey from the autonomous systems division at KTH. Participants were not aware of the details of the study, particularly the results.

  4. 4.

    We acknowledge recent work on GAN inversion for out-of-distribution images [47].

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Author information

Authors and Affiliations

  1. KTH Royal Institute of Technology, Stockholm, Sweden

    Nona Rajabi, Alfredo Reichlin, Farzaneh Taleb, Ali Ghadirzadeh, Mårten Björkman & Danica Kragic

  2. Karolinska Institute, Stockholm, Sweden

    Charles Chernik

  3. INESC-ID & Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal

    Miguel Vasco

  4. Stanford University, Stanford, CA, USA

    Ali Ghadirzadeh

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  1. Nona Rajabi
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Correspondence to Nona Rajabi .

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  1. Soar Technology Inc., Orlando, FL, USA

    Dylan D. Schmorrow

  2. Katmai Government Services, Orlando, FL, USA

    Cali M. Fidopiastis

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Rajabi, N. et al. (2023). Mental Face Image Retrieval Based on a Closed-Loop Brain-Computer Interface. In: Schmorrow, D.D., Fidopiastis, C.M. (eds) Augmented Cognition. HCII 2023. Lecture Notes in Computer Science(), vol 14019. Springer, Cham. https://doi.org/10.1007/978-3-031-35017-7_3

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